Spectral divergence prioritizes key classes, genes, and pathways shared between substance use disorders and cardiovascular disease

Introduction: Substance use disorders (SUDs) are heterogeneous diseases with overlapping biological mechanisms and often present with co-occurring disease, such as cardiovascular disease (CVD). Gene networks associated with SUDs also implicate additional biological pathways and may be used to stratify disease subtypes. Node and edge arrangements within gene networks impact comparisons between classes of disease, and connectivity metrics, such as those focused on degrees, betweenness, and centrality, do not yield sufficient discernment of disease network classification. Comparatively, the graph spectrum's use of comprehensive information facilitates hypothesis testing and inter-disease clustering by using a larger range of graph characteristics. By adding a connectivity-based method, network rankings of similarity and relationships are explored between classes of SUDs and CVD.

Methods: Graph spectral clustering's utility is evaluated relative to commonly used network algorithms for discernment between two distinct co-occurring disorders and capacity to rank pathways based on their distinctiveness. A collection of graphs' structures and connectivity to functionally identify the relationship between CVD and each of four classes of SUDs, namely alcohol use disorder (AUD), cocaine use disorder (CUD), nicotine use disorder (NUD), and opioid use disorder (OUD) is evaluated. Moreover, a Kullback-Leibler (KL) divergence is implemented to identify maximally distinctive genes (D^g). The emphasis of genes with high D^g enables a Jaccard similarity ranking of pathway distinctiveness, creating a functional “network fingerprint”.

Results: Spectral graph outperforms other connectivity-based approaches and reveals interesting observations about the relationship among SUDs. Between CUD and CVD, the gamma-aminobutyric acidergic and arginine metabolism pathways are distinctive. The neurodegenerative prion disease and tyrosine metabolism are emphasized between OUD and CVD. The graph spectrum between AUD and NUD to CVD is not significantly divergent.

Conclusion: Graph spectral clustering with KL divergence illustrates differences among SUDs with respect to their relationship to CVD, suggesting that despite a high-level co-occurring diagnosis or comorbidity, the nature of the relationship between SUD and CVD varies depending on the substance involved. The graph clustering method simultaneously provides insight into the specific biological pathways underlying these distinctions and may reveal future basic and clinical research avenues into addressing the cardiovascular sequelae of SUD.

1 Introduction

Complex diseases are caused by a variety of factors and include a range of psychological and physical disorders such as diabetes (Prasad and Groop, 2015), schizophrenia (Sullivan et al., 2003), substance use disorders (Hatoum et al., 2023) (SUDs), cardiovascular disease (CVD) (Musunuru and Kathiresan, 2019), and others (Thaker, 2017; Andrews et al., 2023). These are often co-occurring conditions (Hossain et al., 2020; Solovieff et al., 2013), and due to the many-to-many relationships among genes and disorders (Goh et al., 2007), identifying the specific biological basis of these relationships presents challenges (Wormington et al., 2024). Doing so would enable a refined classification of the particular subtypes of disease that exist and also provide a greater understanding of the nature and mechanism of comorbid disease (Chen et al., 2025; Sánchez-Valle and Valencia, 2023). For example, in various SUDs, there are associations to CVD, but for each drug, the nature of this relationship may differ (Havakuk et al., 2017; Toska and Mayrovitz, 2023; Pando-Naude et al., 2021). There are several approaches to comparing genetic studies to elucidate the nature and extent of these relationships among complex diseases (Gerring et al., 2024). For example, shared genetic liability between SUD and CVD has been found using polygenic risk scores and linkage disequilibrium, but even with shared multimorbid association (Zhou et al., 2017), the shared functional mechanisms are poorly understood (Morgan et al., 2022).

CVD is a leading cause of death and a common multimorbid and comorbid condition, with high prevalence in people with SUD (Chelikam et al., 2022). While the impacts of SUD and CVD concentrate in different tissues, they share similar genetic associations (Hatoum et al., 2023; Sanchez-Roige et al., 2022). Furthermore, the tremendous number of genetic variants impacting the function of the nervous system and heart (Jonker et al., 2023) presents challenges in prioritization of disease-associated genes (Zhukovsky et al., 2024; Guo et al., 2021). A functional enrichment provides foundational interpretation of variant effects at the level of cellular and metabolic processing underlying disease genes (Reimand et al., 2019). Furthermore, the shared risk factors between psychiatric disorders is such that a focus on specific disorders, independent of the context of comorbid and multimorbid conditions, is insufficient for classification (Chen et al., 2025). To compare disorders on a functional level, pathways have been assessed by the intersection of genes, such as network merging for “network fingerprinting”, which has shown that the arrangement of nodes and edges impacts comparisons on similarity scores (Cui et al., 2015). Moreover, the availability of data on a large number of SUDs (Bough and Pollock, 2018; Hatoum et al., 2023; Uffelmann et al., 2021) enables an assessment of the influence between SUD specific functional pathways and a common multimorbid condition, CVD (Minhas et al., 2024). Comparisons between SUDs and cardiometabolic disease provides insight into shared genes, which are highly translatable to therapeutic potential (Sanchez-Roige et al., 2022; Peng et al., 2021).

Investigating and integrating genomic studies of disease can improve disease diagnosis and characterization (Wirka et al., 2018). From genome-wide associations (GWAS) (Uffelmann et al., 2021) to curated database mining (Piñero et al., 2020), discrete experimental investigations of disorders often converge to a functional classification (Reimand et al., 2019). Enrichment software then gauges biological pathways or functional terms that have, more than by random chance, a significant representation (Kuleshov et al., 2016; Wang et al., 2017; Raudvere et al., 2019; Reimand et al., 2019). A functional characterization may focus on a set of a gene's medicinal, cellular, or biological significance (Wieder et al., 2021; Baltoumas et al., 2021). Several databases are used as a proxy for functional analyses, which include the following: the Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa and Goto, 2000; Kanehisa, 2019; Kanehisa et al., 2023), WikiPath (Agrawal et al., 2023), Gene Ontology (GO) (Ashburner et al., 2000; Consortium et al., 2023), and Reactome (Milacic et al., 2023), among others (Geistlinger et al., 2021; Zhao and Rhee, 2023).

Spectral graph analysis presents a promising approach to simultaneously compare disease based on the various genomic data sources and to identify the biomolecular pathways that can be used to classify them. Graph spectrum has been used for hypothesis testing (Takahashi et al., 2012; Fujita et al., 2017), differentiating diseases and tissues (Santos et al., 2015; Jardim et al., 2019), identifying functional pathways (Fujita et al., 2017), and clustering (Sato et al., 2013) in neurological disorders. The spectrum of a graph contains information on several important dynamics such as number of walks, diameter, and cliques (Takahashi et al., 2012); therefore, the spectrum is more informative to characterizing complex networks than modern metrics (Fujita et al., 2017). Contemporary approaches have classified genes by importance through nodal connectivity evaluations and pinpointed dysfunction or distinguished biological conditions (Barabási and Oltvai, 2004; Gu et al., 2012; Rahmatallah et al., 2013; Santos et al., 2015).

Here, we present a functional analysis of five complex diseases describing the genes underlying 4 SUDs and CVD, a commonly co-occuring condition (Minhas et al., 2024). Moreover, we attempt to elucidate key differences in classes of SUDs and their associations to CVD, resulting in the prioritization of key disease-genes and pathways through graph spectral clustering, which relies on graph structure is and not limited to genomic intersections. We conduct a graph spectral analysis of SUD and CVD related genomic studies with functional KEGG pathways using the statGraph (Castro Guzman et al., 2024) package and posit a comparative insight against other connectivity indices (Fujita et al., 2017) using high fidelity pathways created by KNeXT (Castaneda and Baker, 2024). By leveraging the topological information, the arrangement of nodes and edges, offered by KEGG graphs, we demonstrate that the spectral distribution aids in defining key divergences, or its absence, between four of the following SUDs: AUD, CUD, OUD, and NUD with its comorbidity, CVD. Moreover, we elucidated the biological relevance of principle divergent-driving elements and outline functional class differences between CUD and OUD.

2 Materials and methods

2.1 Datasets

Homo sapiens gene sets were gathered from publicly available repositories and published sources (Figure 1A). Data were collected from DisGeNET (Piñero et al., 2020) using the Harmonizome web service (Rouillard et al., 2016). DisGeNET has been widely used as a benchmark database (Barua et al., 2022; Gentili et al., 2022) due to its comprehensive and curated information on gene-disease associations (Piñero et al., 2020). For each SUD, we used affiliated search terms, noting that the current terminology for CUD has evolved but is not always used in existing data repositories (Deak and Johnson, 2021). These terms include the following: “alcohol use disorder” for AUD, “cocaine abuse” [sic] for CUD, “nicotine dependence” for NUD, and “cardiovascular pathology” for CVD. We combined “heroin dependence”, “opioid use disorder”, and “morphine dependence” for our OUD gene set, see Supplementary Table S1 for full list. Genes were then uploaded to g:Profiler, ignoring ambiguous gene queries, which was set to a g:SCS (Set Count and Sizes) threshold of 0.05. Published KEGG CVD pathways were gathered from (Barua et al., 2022), which used Gene Expression Omnibus microarray datasets for assessing CVD and all its risk factors. Neurological KEGG pathways were acquired using the following Biological Relation Inference and Classification Engine (BRITE) terms: Nervous system, Substance dependence, and Neurodegenerative diseases. Additionally, we included the pathway Neuroactive ligand-receptor interaction. All neurological pathways derived from BRITE terms were used for systematically characterizing synapses across regions of the brain (Bar-Shira et al., 2015).

Figure 1

Figure 1. The framework of prioritizing pathways and genes using spectral clustering and a KL divergence. (A) Disorder-associated genes sets derived from humans, acquired from DisGeNET and published sources, are prioritized from experimental analyses or from database mining and are subsequently enriched for KEGG pathways. (B) Enriched pathways' KGML files are then parsed in KNeXT as functional gene-gene networks. (C) KNeXT-generated gene networks are hierarchically clustered through spectral clustering. (D) Post clustering, individual genes are assessed through KL divergence against an opposing cluster, dotted green lines. (E) Genes in cluster k are compared to genes in cluster l and then the comparison is reversed where genes in cluster l are compared to genes in cluster k. All genes with high D^g are compared to all pathways within its origin cluster. (F) The results of this framework are Jaccard scores for all pathways in each cluster. KEGG pathways with a high Jaccard score have an abundance of top D^g genes, which in turn, is driving distinction between clusters.

2.2 KEGG networks

Here, we focus on KEGG's database which hosts a series of biological systems maps that offer specific molecular pathways based on highly curated and experimentally verified gene-gene, gene-compound, and gene-pathway interactions (Kanehisa and Goto, 2000; Kanehisa, 2019; Kanehisa et al., 2023). KEGG is an important resource because all molecular associations are stored for secondary parsing and analyzing in a standard language: KEGG Markup Language (KGML). KGML files can be readily parsed and used with robust software packages including the following: KEGG NetworkX Topological (KNeXT) Parser (Castaneda and Baker, 2024), graphite (Sales et al., 2012), KEGGParser (Arakelyan and Nersisyan, 2013), among others (Nersisyan et al., 2014; Chanumolu et al., 2021). KNeXT, in particular, focuses on the spatiotemporal dynamics reflected in a KGML file to create high fidelity pathways (Castaneda and Baker, 2024). All KEGG pathways were parsed using the KNeXT parser, see Figure 1B. KNeXT creates high fidelity genes-only pathways (Castaneda and Baker, 2024). For simplicity, all pathways used NCBI gene identifiers, contained no compounds, and are unweighted and undirected. For full list of all KEGG pathways, see Supplementary Table S2.

2.3 Spectral discrimination

In this study, we used the statGraph (Castro Guzman et al., 2024) package in R version 4.3.1 (R Core Team, 2023). statGraph features several tests for conducting spectral analyses of graph lists. From this package, we used the Analysis Of Graph Variability (anogva), Takahashi Test (takahashi.test), and heirarchical clustering (hclust). anogva performs a statistical test on a set of two or more graphs to determine if they are generated by the same random process (Fujita et al., 2017). takahashi.test conducts a statistical test to determine if two sets of graphs are generated by the same random process (Takahashi et al., 2012; Fujita et al., 2017). All tests used a seed set at one. hclust conducts a hierarchical clustering of a list of graphs based on their spectral distribution (Figures 1C, D). We used default parameters, which include complete agglomerative clustering method with Silverman bandwidth and exact spectral density.

2.4 Algorithmic comparisons

For baseline comparisons, we used common indices, which included the following: degree, average betweenness centrality, and closeness centrality (Fujita et al., 2017; Zito et al., 2021). Implementation of these metrics was through NetworkX (Hagberg et al., 2008). We used the Jensen-Shannon (JS) distance in the SciPy version 1.5.0 package (Virtanen et al., 2020) to create distance matrices for input into the AgglomerativeClustering function in the Scikit-learn package (Pedregosa et al., 2011). The same parameters to the hclust package were used with complete linkage.

2.5 Statistical comparisons

Statistical comparisons for graph performance was measured using the Adjusted Rand Index (ARI) (Warrens and van der Hoef, 2022). For R analyses, we used the fossil package version 0.4.0 (Vavrek, 2011), and for Python analyses, we used the Scikit-learn package (Pedregosa et al., 2011). Both metrics measure the accuracy of clustering with ARI being adjusted for randomness. An ARI ≤ 0 is equivalent to random assignments (Yeung et al., 2003). ARI has been used for comparisons of clustering performance in previous works (Wu and Wu, 2020; Zelig et al., 2023).

2.6 Gene and pathway prioritization

To prioritize genes and pathways in biological clusters, we modified a method developed by Dey et al. (2017), which uses KL divergence to compare the distinctiveness of a gene, g, with respect to any cluster l see Equation 1. Here, we used the entropy function in the Scikit-learn package (Pedregosa et al., 2011).

$\begin{array}{ll}K{L}^g\left[k,l\right]={\displaystyle \sum _{x\in X}}{p}^{S_k}(x)\times log\frac{p^{S_k}(x)}{p^{S_l}(x)}& (1)\end{array}$

Let S = {S₁, S₂, S₃, ...S_n} be a collection of KEGG pathways in cluster k with vertex set, X. p is the degree distribution for any X in any pathway in S compared to any X in any pathway in cluster l. Thereby, for each cluster k, we measure the distinctiveness of each gene as the minimum divergence (Equation 2).

$\begin{array}{ll}{D}^g\left[k\right]={\displaystyle \underset{l\ne k}{min}}K{L}^g\left[k,l\right]& (2)\end{array}$

Thereby, genes with a maximum distinctiveness (D^g) are the genes with the largest role in distinguishing cluster k from cluster l. After identifying the genes with the highest D^g we use a Jaccard index to determine the set similarity (Equation 3). D_k is the set of genes with the highest D^g in cluster k and S_k is some pathway in cluster k, see Figures 1D–F.

$\begin{array}{ll}J(D_k,S_k)=\frac{|D_k\cap S_k|}{|D_k\cup S_k|}& (3)\end{array}$

3 Results

3.1 Comparisons of KEGG enriched pathway from DisGeNET derived gene sets

Our first analysis was to determine the significance of the divergence between the four SUDs and CVD KEGG pathway lists. Here, divergence refers to the disparate random processes underlying a collection of graphs as defined by Takahashi et al. (2012); Fujita et al. (2017). We compared each KEGG pathway list derived from highly supported DisGeNET (Piñero et al., 2020) gene sets. First, we used anogva to test the spectrum of all data. anogva controls for Type I errors and is robust for unbalanced data (Fujita et al., 2017). The results showed a significant JS divergence between all five sets of graphs, see Table 1. Moreover, Takahashi's Test revealed a significant JS divergence between CUD and OUD against CVD but not for AUD and NUD, see Table 1, which parallels past epidemiological studies signifying the strong relationship between AUD, NUD, and CVD (Yeates et al., 2015). Post Takahashi's Test, we then conducted a hierarchical agglomerative clustering on the significant SUDs. Clustering quality was determined by the ARI against commonly used algorithms (Fujita et al., 2017). CUD had the highest ARI and outperformed all other connectivity metrics, and OUD generated the highest ARI compared to baseline measures (Figure 2).

Table 1

Table 1. Results of test for the JS divergence between groups.

Figure 2

Figure 2. Comparison of spectral clustering to commonly used algorithms. DisGeNET-derived pathway groups include cocaine use disorder (CUD) and opioid use disorder (OUD) compared to cardiovascular disease (CVD). Benchmarked groups (BEN) are groups derived from BRITE terms for nervous system classes and surveyed CVD pathways. Spectral clustering outperformed all other algorithms.

3.2 Comparisons of benchmarked KEGG pathways

From the perspective of KEGG pathway enrichment, there are several factors that may generate inconsistent pathway inclusions (Mubeen et al., 2022). In order to show the inherent functional divergence between neurobiologically-derived gene sets and CVD, we conducted an analysis on what we term “benchmarked” KEGG pathways. Pathways are retrieved from published surveys or extensive analyses which focused on KEGG function and require no enrichment profiling. Additionally, these pathways have been used or compared to benchmark data (Bar-Shira et al., 2015; Barua et al., 2022). Takahashi's test illustrates that KEGG pathways involved in the brain are significantly divergent from KEGG pathways involved in CVD, see Table 1. Furthermore, benchmarked KEGG pathways scored the highest ARI of all other pathways (Figure 2). Hence, evidence suggests that the sub-network of KEGG pathways involved in the brain are structurally different when compared to CVD, which is captured by the graph's spectrum.

3.3 Analysis of top driving genes

For OUD and CUD, the two SUDs that significantly differed in divergence, we used the highest D^g (Dey et al., 2017) of each cluster to create gene sets which in turn were used to rank each pathway by their composition using Jaccard similarity. The results of an agglomerative clustering between CUD and CVD illustrate two pathways, hsa00220 (arginine biosynthesis) and hsa00330 (arginine and proline metabolism), are divergent from the rest of the maps, see Figure 3. The top driving genes created gene sets that had the highest similarity with arginine biosynthesis in cluster one and gamma aminobutyric acid (GABAergic) synapse in cluster two (Figure 3).

Figure 3

Figure 3. Agglomerative hierarchical clustering for CUD vs. CVD. The top driving genes are genes that have a high D^g and are listed according to their cluster, which is color and shape coordinated. Furthermore, pathways with a high Jaccard index are bolded. Pathway hsa00220 is arginine biosynthesis and hsa04727 is GABAergic synapse.

From the top driving genes in OUD vs CVD, the pathways with the highest D^g defined tyrosine metabolism and prion disease (Figure 4). In addition, the top driving genes belonging to the GABAergic synapse in CUD are similarly reflected in the benchmarked gene sets with Glutamatergic synapse, another organismal/nervous system pathway, being highly divergent (Supplementary Figures S1, S2). Given that NF-kappa B signaling pathway was a top driving pathway whose class is not similar to any SUD, this implies an inherit distinctiveness between classes of SUD and the brain in their comparison to CVD (Supplementary Figures S1, S2).

Figure 4

Figure 4. The results of the agglomerative hierarchical clustering for OUD vs. CVD. The top driving genes are genes which have a high D^g and are listed according to their cluster, which is color coordinated. Furthermore, pathways with a high Jaccard index are bolded. Pathway hsa03050 is tyrosine metabolism and pathway hsa05020 is prion disease.

3.4 Functional fingerprint

We illustrate a “network fingerprint”, as described in Cui et al. (2015) between SUDs and CVD (Figure 5). CUD and OUD, Figures 5A, B, respectively, differ based on the pathways included in each cluster. Furthermore, the division is solely in metabolic pathways in CUD, Figure 5A, while OUD differs in both human diseases and metabolism (Figure 5B). A comprehensive difference was generated between CUD and OUD (Figure 5C). An interesting aspect is the role metabolism plays in both CUD and OUD's largest magnitude of difference, see Figure 5C.

Figure 5

Figure 5. All similarity results sorted by KEGG class. Pathways that cluster separately are highlighted in yellow. (A) Similarity results for CUD. As shown, two metabolism pathways diverged compared to the rest of the results. (B) Similarity results for OUD. For OUD, metabolism and two human disease pathways drove the cluster separation. (C) The differences in magnitude of Jaccard similarity between CUD and OUD. As shown, metabolism plays the largest role in the differences between both CUD and OUD.

4 Discussion

Here, we demonstrated the utility of graph spectral clustering for differentiating between the bases of comorbidity of disease. The technique outlined reveals the effectiveness of assessing the distinctions between classes of SUDs and a commonly co-occurring disease, CVD. We have shown this method to outperform other commonly used algorithms in classifying KEGG pathways derived from SUDs and CVD gene sets. Furthermore, we leveraged spectral clustering to rank pathways according to their distinctiveness between the two conditions, revealing a “network fingerprint” comparison, similarity described in Cui et al. (2015). Our analysis pipeline finds differences among disorders and identifies key pathways, which may have therapeutic or diagnostic consequences. The facilitation of a “network fingerprint” diagram aids in hypothesis building and identifying key functional pathways. From these clusters, we can characterize the associations between two diseases, which are unmatched by KEGG pathway lists alone and other topology-based methods.

KEGG is an important tool for disease study from a functional perspective (Kanehisa and Goto, 2000; Kanehisa, 2019; Kanehisa et al., 2023). Surveys of KEGG pathways and disease interactions infer novel association of overlapping risk factors (Barua et al., 2022) and common disorders, (Li et al., 2008) given sets of prioritized genes (Cirincione et al., 2018). Moreover, other analyses rank and prioritize pathways by significance levels (Chu et al., 2024). While these approaches are useful to generate new insights in highly related diseases that have strong pathway sharing within tissues, they do not provide adequate discernment for two convergent disorders involving different tissues, which share common and distinct biomarkers (Moon et al., 2025; Riley et al., 2022; Gu et al., 2021; Daneshafrooz et al., 2022). Here, we have emphasized pathways through graph connectivity, which does not rely merely on pathway member composition. Hence, we propose future usage for comparisons between disorders that exist in different tissues and experience limited functional overlap, such as certain comparisons of fibrotic disease (Gu et al., 2021) and frontotemporal dementia (FTP) and amyotrophic lateral sclerosis (ALS) (Daneshafrooz et al., 2022). While epidemiological studies demonstrate a strong comorbidity between SUDs, defined here as AUD, NUD, CUD, and OUD, and CVD (Gan et al., 2021; Chelikam et al., 2022), our approach distinguishes the more granular separation of SUDs and CVD based on their KEGG pathway representations. We provide evidence to show that CUD and OUD are significantly divergent to CVD while AUD and NUD are not. This divergence may be explained by differences in how these substances interact with the cardiovascular system (Havakuk et al., 2017; Toska and Mayrovitz, 2023; Jalali et al., 2021) and areas of the brain (Pando-Naude et al., 2021). Additionally, we highlight the specific metabolic and neurological pathways and genes driving these distinctions. The profile of these clusters would be useful in disease state transition surveilling (Guo et al., 2021) and model organism testing. For example, knockouts of genes involved in these pathways may show insights for vulnerabilities to CVD for a given SUD (Cacheiro et al., 2023).

While the main focus of this work was the divergence created by each KEGG pathway, network merging is a crucial aspect of heterogeneous graph development where integrating and comparing graphs is essential (Chang et al., 2016; Zitnik et al., 2024). Moreover, existing software analyses have limited scalability on large data sets (Chang et al., 2016; Smedley et al., 2015). The technique examined here may be applicable for automated KEGG enrichment data set preprocessing, trimming, and curation (Orouji et al., 2024).

Biologically, a synergism exists between the representative genes from pathways with several high D^g genes. For example, tyrosine metabolism disruption (Rathor and Ch, 2023) and OUD is known to affect circadian rhythms (Puig et al., 2023). Neurodegeneration-related pathways are linked to OUD-mediated circadian rhythm disruption (Puig et al., 2023). The prioritized pathways might imply novel transitory genes that are implicated in circadian rhythm disruption as several glutaminergic synaptic signaling genes were prioritized alongside the implications of aromatic amino acid metabolism (Humer et al., 2020; Puig et al., 2023). Moreover, “network fingerprinting” (Cui et al., 2015) clustered prion disease with Type 2 diabetes and amyotrophic lateral sclerosis, which implicates neurodegeneration playing a role in complex diseases. In our CUD clusters, arginine has been studied for its role in CVD prevention and treatment (Tousoulis et al., 2007; Bahadoran et al., 2016). GABA plays a role in both CVD (Bu et al., 2021) and CUD (Wydra et al., 2024). GABA has shown promise as pharmacotherapy for addiction (Wydra et al., 2024), and accordingly, arginine has been shown to synaptically interact with GABA in the brain of rats (Shen et al., 1997). The synergism of these pathways and their divergent-driving genes might have implications in co-occurring (Stoychev et al., 2021) CVD and CUD treatment and study (Wilson et al., 2001).

A limitation of this study is the statistical tests in statGraph do not account for multiple group memberships. Hence, diseases with high overlap of KEGG pathways will create difficulties in using the tools outlined in this analysis. An additional limitation of this study is the high redundancy of KEGG pathways (Karp et al., 2021), which creates issues in finding differences in topology, gene-gene connectivity, as suggested by low ARI scores. Moreover, several software exist for KEGG enrichment (Mubeen et al., 2022), and the pathway database itself may be biased to understudied genes, such as non-coding RNA genes (Li et al., 2022). Consequently, the topology and genomic composition of KEGG pathways are not comprehensive (Wilk and Braun, 2017; Gable et al., 2022). While a survey of all enrichment software and KEGG parsers is beyond the scope of this article, we note that use of different combinations of software and thresholds may produce varying results. Moreover, discrete combinations of search terms for disorders in DisGeNET may yield larger or smaller gene sets, which would render disparate amounts and combinations of KEGG pathways. Hence, the use of benchmarked data served solely to indicate an inherent divergence in pathways representing the brain (Bar-Shira et al., 2015) and pathways underlying a comprehensive study of CVD (Barua et al., 2022).

We have demonstrated how underexplored network features (Santos et al., 2015) may be employed to prioritize or differentiate disorders. In previous functional studies, SUDs are coalesced (Li et al., 2008), which overlooks underlying differences. We leveraged the divergence of the collection of KEGG graphs to prioritize genes that are implicated in driving the functional clustering between SUD and CVD. The pathway and genes prioritized are biologically relevant and might have implications for future studies in knockout or other experimental analyses. Additionally, the pathway and gene rankings could justify inclusion or exclusion in large-scale or heterogeneous network analyses of multiple disorder studies (Xiong et al., 2019; Gu et al., 2022). Furthermore, the magnitude of the pathway ranking differences decomposes the complexity of a collection of KEGG graphs, conferring critical visualization and processing where KEGG lists alone cannot provide.

The graph spectrum reveals a distinction among disorders that are co-occurring and can allow visualization of the relationships among multiple disorders simultaneously. Spectral clustering outperformed other commonly used algorithms in classifying clusters of a psychiatric disorder and a common multimorbidity or comorbidity in CVD, and thus its application to other comorbidities observed in SUDs, psychiatric disorders, and other complex disease is promising. Furthermore, the method can characterize the pathways that drive each cluster's distinction to reveal insights about their biological implications, potential diagnostic, and therapeutic targets. In contrast to many pathway overlap approaches that rely on data from disease that involve a limited tissue or cell population, the method introduced here has implications for identifying genes that drive co-morbid conditions in distinct diseases encompassing a diverse range of tissues and embodying systems networks that have little functional pathway overlap.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.

Ethics statement

Ethical approval was not required for the study involving humans in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was not required from the participants or the participants' legal guardians/next of kin in accordance with the national legislation and the institutional requirements.

Author contributions

ECa: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. EB: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing. ECh: Writing – review & editing.

Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Gen AI was used in the creation of this manuscript.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins.2025.1572243/full#supplementary-material

References

Agrawal, A., BalcÄś, H., Hanspers, K., Coort, S. L., Martens, M., Slenter, D. N., et al. (2023). WikiPathways 2024: next generation pathway database. Nucleic Acids Res. 52, D679–D689. doi: 10.1093/nar/gkad960

PubMed Abstract | Crossref Full Text | Google Scholar

Andrews, S. J., Renton, A. E., Fulton-Howard, B., Podlesny-Drabiniok, A., Marcora, E., and Goate, A. M. (2023). The complex genetic architecture of alzheimer's disease: novel insights and future directions. eBioMedicine 90:104511. doi: 10.1016/j.ebiom.2023.104511

PubMed Abstract | Crossref Full Text | Google Scholar

Arakelyan, A., and Nersisyan, L. (2013). KEGGParser: parsing and editing KEGG pathway maps in Matlab. Bioinformatics 29, 518–519. doi: 10.1093/bioinformatics/bts730

PubMed Abstract | Crossref Full Text | Google Scholar

Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., et al. (2000). Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29. doi: 10.1038/75556

PubMed Abstract | Crossref Full Text | Google Scholar

Bahadoran, Z., Mirmiran, P., Tahmasebinejad, Z., and Azizi, F. (2016). Dietary l-arginine intake and the incidence of coronary heart disease: Tehran lipid and glucose study. Nutr Metab. 13:23. doi: 10.1186/s12986-016-0084-z

PubMed Abstract | Crossref Full Text | Google Scholar

Baltoumas, F. A., Zafeiropoulou, S., Karatzas, E., Koutrouli, M., Thanati, F., Voutsadaki, K., et al. (2021). Biomolecule and bioentity interaction databases in systems biology: A comprehensive review. Biomolecules 11(8). doi: 10.3390/biom11081245

PubMed Abstract | Crossref Full Text | Google Scholar

BarabÃa̧si, A.-L., and Oltvai, Z. N. (2004). Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 5, 101–113. doi: 10.1038/nrg1272

PubMed Abstract | Crossref Full Text | Google Scholar

Bar-Shira, O., Maor, R., and Chechik, G. (2015). Gene expression switching of receptor subunits in human brain development. PLoS Comput. Biol. 11, 1–21. doi: 10.1371/journal.pcbi.1004559

PubMed Abstract | Crossref Full Text | Google Scholar

Barua, J. D., Omit, S. B. S., Rana, H. K., Podder, N. K., Chowdhury, U. N., and Rahman, M. H. (2022). Bioinformatics and system biological approaches for the identification of genetic risk factors in the progression of cardiovascular disease. Cardiovasc. Ther. 2022:9034996. doi: 10.1155/2022/9034996

PubMed Abstract | Crossref Full Text | Google Scholar

Bough, K. J., and Pollock, J. D. (2018). Defining substance use disorders: The need for peripheral biomarkers. Trends Mol. Med. 24, 109–120. doi: 10.1016/j.molmed.2017.12.009

PubMed Abstract | Crossref Full Text | Google Scholar

Bu, J., Huang, S., Wang, J., Xia, T., Liu, H., You, Y., et al. (2021). The gabaa receptor influences pressure overload-induced heart failure by modulating macrophages in mice. Front. Immunol. 12:670153. doi: 10.3389/fimmu.2021.670153

PubMed Abstract | Crossref Full Text | Google Scholar

Cacheiro, P., Spielmann, N., Mashhadi, H. H., Fuchs, H., Gailus-Durner, V., Smedley, D., et al. (2023). Knockout mice are an important tool for human monogenic heart disease studies. Dis. Model. Mech. 16:49770. doi: 10.1242/dmm.049770

PubMed Abstract | Crossref Full Text | Google Scholar

Castaneda, E. U., and Baker, E. J. (2024). Knext: a networkx-based topologically relevant kegg parser. Front. Genet. 15:1292394. doi: 10.3389/fgene.2024.1292394

PubMed Abstract | Crossref Full Text | Google Scholar

Castro Guzman, G. E., da Costa, D. R., Ramos, T. C., Santos, S. S., Lira, E. S., and Fujita, A. (2024). statGraph: Stati stical Methods for Graphs. The Comprehensive R Archive Network; Institute of Mathematics and Statistics; University of São Paulo.

Google Scholar

Chang, J., Cho, H., and Chou, H.-H. (2016). Mango: combining and analyzing heterogeneous biological networks. BioData Min. 9:25. doi: 10.1186/s13040-016-0105-5

PubMed Abstract | Crossref Full Text | Google Scholar

Chanumolu, S., Albahrani, M., Can, H., and Otu, H. (2021). KEGG2Net: Deducing gene interaction networks and acyclic graphs from KEGG pathways. EMBnet J. 26:949. doi: 10.14806/ej.26.0.949

PubMed Abstract | Crossref Full Text | Google Scholar

Chelikam, N., Vyas, V., Dondapati, L., Iskander, B., Patel, G., Jain, S., et al. (2022). Epidemiology, burden, and association of substance abuse amongst patients with cardiovascular disorders: national cross-sectional survey study. Cureus 14:e27016. doi: 10.7759/cureus.27016

PubMed Abstract | Crossref Full Text | Google Scholar

Chen, X., Lu, Y., Cue, J. M., Han, M. V., Nimgaonkar, V. L., Weinberger, D. R., et al. (2025). Classification of schizophrenia, bipolar disorder and major depressive disorder with comorbid traits and deep learning algorithms. Schizophrenia 11:14. doi: 10.1038/s41537-025-00564-7

PubMed Abstract | Crossref Full Text | Google Scholar

Chu, C., Liu, S., Nie, L., Hu, H., Liu, Y., and Yang, J. (2024). The interactions and biological pathways among metabolomics products of patients with coronary heart disease. Biomed. Pharmacother. 173:116305. doi: 10.1016/j.biopha.2024.116305

PubMed Abstract | Crossref Full Text | Google Scholar

Cirincione, A. G., Clark, K. L., and Kann, M. G. (2018). Pathway networks generated from human disease phenome. BMC Med. Genomics 11:75. doi: 10.1186/s12920-018-0386-2

PubMed Abstract | Crossref Full Text | Google Scholar

Consortium, T. G. O., Aleksander, S. A., Balhoff, J., Carbon, S., Cherry, J. M., Drabkin, H. J., et al. (2023). The Gene Ontology knowledgebase in 2023. Genetics 224:iyad031. doi: 10.1093/genetics/iyad031

PubMed Abstract | Crossref Full Text | Google Scholar

Cui, X., He, H., He, F., Wang, S., Li, F., and Bo, X. (2015). Network fingerprint: a knowledge-based characterization of biomedical networks. Sci. Rep. 5:13286. doi: 10.1038/srep13286

PubMed Abstract | Crossref Full Text | Google Scholar

Daneshafrooz, N., Bagherzadeh Cham, M., Majidi, M., and Panahi, B. (2022). Identification of potentially functional modules and diagnostic genes related to amyotrophic lateral sclerosis based on the WGCNA and lasso algorithms. Sci. Rep. 12:20144. doi: 10.1038/s41598-022-24306-2

PubMed Abstract | Crossref Full Text | Google Scholar

Deak, J. D., and Johnson, E. C. (2021). Genetics of substance use disorders: a review. Psychol. Med. 51, 2189–2200. doi: 10.1017/S0033291721000969

PubMed Abstract | Crossref Full Text | Google Scholar

Dey, K. K., Hsiao, C. J., and Stephens, M. (2017). Visualizing the structure of rna-seq expression data using grade of membership models. PLoS Genet. 13, 1–23. doi: 10.1371/journal.pgen.1006759

PubMed Abstract | Crossref Full Text | Google Scholar

Fujita, A., Vidal, M. C., and Takahashi, D. Y. (2017). A statistical method to distinguish functional brain networks. Front. Neurosci. 11:66. doi: 10.3389/fnins.2017.00066

PubMed Abstract | Crossref Full Text | Google Scholar

Gable, A. L., Szklarczyk, D., Lyon, D., Matias Rodrigues, J. F., and von Mering, C. (2022). Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments. Brief. Bioinformatics 23:bbac355. doi: 10.1093/bib/bbac355

PubMed Abstract | Crossref Full Text | Google Scholar

Gan, W. Q., Buxton, J. A., Scheuermeyer, F. X., Palis, H., Zhao, B., Desai, R., et al. (2021). Risk of cardiovascular diseases in relation to substance use disorders. Drug Alcohol Depend. 229:109132. doi: 10.1016/j.drugalcdep.2021.109132

PubMed Abstract | Crossref Full Text | Google Scholar

Geistlinger, L., Csaba, G., Santarelli, M., Ramos, M., Schiffer, L., Turaga, N., et al. (2021). Toward a gold standard for benchmarking gene set enrichment analysis. Brief. Bioinformatics 22, 545–556. doi: 10.1093/bib/bbz158

PubMed Abstract | Crossref Full Text | Google Scholar

Gentili, M., Martini, L., Sponziello, M., and Becchetti, L. (2022). Biological Random Walks: multi-omics integration for disease gene prioritization. Bioinformatics 38, 4145–4152. doi: 10.1093/bioinformatics/btac446

PubMed Abstract | Crossref Full Text | Google Scholar

Gerring, Z. F., Thorp, J. G., Treur, J. L., Verweij, K. J. H., and Derks, E. M. (2024). The genetic landscape of substance use disorders. Mol. Psychiatry. 29, 3694–3705. doi: 10.1038/s41380-024-02547-z

PubMed Abstract | Crossref Full Text | Google Scholar

Goh, K.-I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., and BarabÃa̧si, A.-L. (2007). The human disease network. Proc. Nat. Acad. Sci. 104, 8685–8690. doi: 10.1073/pnas.0701361104

PubMed Abstract | Crossref Full Text | Google Scholar

Gu, C., Shi, X., Dang, X., Chen, J., Chen, C., Chen, Y., et al. (2021). Identification of common genes and pathways in eight fibrosis diseases. Front. Genet. 11:627396. doi: 10.3389/fgene.2020.627396

PubMed Abstract | Crossref Full Text | Google Scholar

Gu, Y., Zheng, S., Yin, Q., Jiang, R., and Li, J. (2022). Redda: Integrating multiple biological relations to heterogeneous graph neural network for drug-disease association prediction. Comput. Biol. Med. 150:106127. doi: 10.1016/j.compbiomed.2022.106127

PubMed Abstract | Crossref Full Text | Google Scholar

Gu, Z., Liu, J., Cao, K., Zhang, J., and Wang, J. (2012). Centrality-based pathway enrichment: a systematic approach for finding significant pathways dominated by key genes. BMC Syst. Biol. 6:56. doi: 10.1186/1752-0509-6-56

PubMed Abstract | Crossref Full Text | Google Scholar

Guo, Z., Fu, Y., Huang, C., Zheng, C., Wu, Z., Chen, X., et al. (2021). Nogea: A network-oriented gene entropy approach for dissecting disease comorbidity and drug repositioning. Genom. Proteom. Bioinformat. 19, 549–564. doi: 10.1016/j.gpb.2020.06.023

PubMed Abstract | Crossref Full Text | Google Scholar

Hagberg, A. A., Schult, D. A., and Swart, P. J. (2008). Exploring network structure, dynamics, and function using networkx, in Proceedings of the 7th Python in Science Conference, eds. G. Varoquaux, T. Vaught, and J. Millman (Pasadena, CA: SciPy2008), 11–15.

Google Scholar

Hatoum, A. S., Colbert, S. M. C., Johnson, E. C., Huggett, S. B., Deak, J., Pathak, G. A., et al. (2023). Multivariate genome-wide association meta-analysis of over 1 million subjects identifies loci underlying multiple substance use disorders. Nat. Mental Health 1, 210–223. doi: 10.1101/2022.01.06.22268753

PubMed Abstract | Crossref Full Text | Google Scholar

Havakuk, O., Rezkalla, S. H., and Kloner, R. A. (2017). The cardiovascular effects of cocaine. J. Am. Coll. Cardiol. 70, 101–113. doi: 10.1016/j.jacc.2017.05.014

PubMed Abstract | Crossref Full Text | Google Scholar

Hossain, M. E., Uddin, S., Khan, A., and Moni, M. A. (2020). A framework to understand the progression of cardiovascular disease for type 2 diabetes mellitus patients using a network approach. Int. J. Environ. Res. Public Health 17:596. doi: 10.3390/ijerph17020596

PubMed Abstract | Crossref Full Text | Google Scholar

Humer, E., Pieh, C., and Brandmayr, G. (2020). Metabolomics in sleep, insomnia and sleep apnea. Int. J. Mol. Sci. 21:7244. doi: 10.3390/ijms21197244

PubMed Abstract | Crossref Full Text | Google Scholar

Jalali, Z., Khademalhosseini, M., Soltani, N., and Esmaeili Nadimi, A. (2021). Smoking, alcohol and opioids effect on coronary microcirculation: an update overview. BMC Cardiovasc. Disord. 21:185. doi: 10.1186/s12872-021-01990-y

PubMed Abstract | Crossref Full Text | Google Scholar

Jardim, V. C., Santos, S. S., Fujita, A., and Buckeridge, M. S. (2019). Bionetstat: A tool for biological networks differential analysis. Front. Genet. 10:594. doi: 10.3389/fgene.2019.00594

PubMed Abstract | Crossref Full Text | Google Scholar

Jonker, T., Barnett, P., Boink, G. J. J., and Christoffels, V. M. (2023). Role of genetic variation in transcriptional regulatory elements in heart rhythm. Cells 13:4. doi: 10.3390/cells13010004

PubMed Abstract | Crossref Full Text | Google Scholar

Kanehisa, M. (2019). Toward understanding the origin and evolution of cellular organisms. Protein Sci. 28, 1947–1951. doi: 10.1002/pro.3715

PubMed Abstract | Crossref Full Text | Google Scholar

Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M., and Ishiguro-Watanabe, M. (2023). KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 51, D587–D592. doi: 10.1093/nar/gkac963

PubMed Abstract | Crossref Full Text | Google Scholar

Kanehisa, M., and Goto, S. (2000). KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30. doi: 10.1093/nar/28.1.27

PubMed Abstract | Crossref Full Text | Google Scholar

Karp, P. D., Midford, P. E., Caspi, R., and Khodursky, A. (2021). Pathway size matters: the influence of pathway granularity on over-representation (enrichment analysis) statistics. BMC Genomics 22:191. doi: 10.1186/s12864-021-07502-8

PubMed Abstract | Crossref Full Text | Google Scholar

Kuleshov, M. V., Jones, M. R., Rouillard, A. D., Fernandez, N. F., Duan, Q., Wang, Z., et al. (2016). Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97. doi: 10.1093/nar/gkw377

PubMed Abstract | Crossref Full Text | Google Scholar

Li, C.-Y., Mao, X., and Wei, L. (2008). Genes and (common) pathways underlying drug addiction. PLoS Comput. Biol. 4, 1–7. doi: 10.1371/journal.pcbi.0040002

PubMed Abstract | Crossref Full Text | Google Scholar

Li, Z., Zhang, Y., Fang, J., Xu, Z., Zhang, H., Mao, M., et al. (2022). Ncpath: a novel platform for visualization and enrichment analysis of human non-coding RNA and KEGG signaling pathways. Bioinformatics 39:494777. doi: 10.1101/2022.06.03.494777

PubMed Abstract | Crossref Full Text | Google Scholar

Milacic, M., Beavers, D., Conley, P., Gong, C., Gillespie, M., Griss, J., et al. (2023). The reactome pathway knowledgebase 2024. Nucleic Acids Res. 52, D672–D678. doi: 10.1093/nar/gkad1025

PubMed Abstract | Crossref Full Text | Google Scholar

Minhas, A. M. K., Kewcharoen, J., Hall, M. E., Warraich, H. J., Greene, S. J., Shapiro, M. D., et al. (2024). Temporal trends in substance use and cardiovascular disease–related mortality in the united states. J. Am. Heart Assoc. 13:e030969. doi: 10.1161/JAHA.123.030969

PubMed Abstract | Crossref Full Text | Google Scholar

Moon, K. Z., Rahman, M. H., Alam, M. J., Hossain, M. A., Hwang, S., Kang, S., et al. (2025). Unraveling the interplay between cardiovascular diseases and alcohol use disorder: a bioinformatics and network-based exploration of shared molecular pathways and key biomarkers validation via western blot analysis. Comput. Biol. Chem. 115:108338. doi: 10.1016/j.compbiolchem.2024.108338

PubMed Abstract | Crossref Full Text | Google Scholar

Morgan, S. L., Naderi, P., Koler, K., Pita-Juarez, Y., Prokopenko, D., Vlachos, I. S., et al. (2022). Most pathways can be related to the pathogenesis of alzheimer's disease. Front. Aging Neurosci. 14:846902. doi: 10.3389/fnagi.2022.846902

PubMed Abstract | Crossref Full Text | Google Scholar

Mubeen, S., Tom Kodamullil, A., Hofmann-Apitius, M., and Domingo-Fernández, D. (2022). On the influence of several factors on pathway enrichment analysis. Brief. Bioinformat. 23:bbac143. doi: 10.1093/bib/bbac143

PubMed Abstract | Crossref Full Text | Google Scholar

Musunuru, K., and Kathiresan, S. (2019). Genetics of common, complex coronary artery disease. Cell 177, 132–145. doi: 10.1016/j.cell.2019.02.015

PubMed Abstract | Crossref Full Text | Google Scholar

Nersisyan, L., Samsonyan, R., and Arakelyan, A. (2014). Cykeggparser: tailoring kegg pathways to fit into systems biology analysis workflows [version 2; peer review: 2 approved]. F1000Research 3:145. doi: 10.12688/f1000research.4410.2

PubMed Abstract | Crossref Full Text | Google Scholar

Orouji, S., Liu, M. C., Korem, T., and Peters, M. A. K. (2024). Domain adaptation in small-scale and heterogeneous biological datasets. Sci. Adv. 10:eadp6040. doi: 10.1126/sciadv.adp6040

PubMed Abstract | Crossref Full Text | Google Scholar

Pando-Naude, V., Toxto, S., Fernandez-Lozano, S., Parsons, C. E., Alcauter, S., and Garza-Villarreal, E. A. (2021). Gray and white matter morphology in substance use disorders: a neuroimaging systematic review and meta-analysis. Transl. Psychiatry 11:29. doi: 10.1038/s41398-020-01128-2

PubMed Abstract | Crossref Full Text | Google Scholar

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: machine learning in Python. J. Mach. Learn. Rese. 12, 2825–2830. doi: 10.5555/1953048.2078195

PubMed Abstract | Crossref Full Text | Google Scholar

Peng, Q., Wilhelmsen, K. C., and Ehlers, C. L. (2021). Common genetic substrates of alcohol and substance use disorder severity revealed by pleiotropy detection against gwas catalog in two populations. Addict. Biol. 26:e12877. doi: 10.1111/adb.12877

PubMed Abstract | Crossref Full Text | Google Scholar

Piñero, J., Ramı́rez-Anguita, J. M., Saüch-Pitarch, J., Ronzano, F., Centeno, E., Sanz, F., et al. (2020). The disgenet knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 48, D845–855. doi: 10.1093/nar/gkz1021

PubMed Abstract | Crossref Full Text | Google Scholar

Prasad, R. B., and Groop, L. (2015). Genetics of type 2 diabetes-pitfalls and possibilities. Genes 6, 87–123. doi: 10.3390/genes6010087

PubMed Abstract | Crossref Full Text | Google Scholar

Puig, S., Xue, X., Salisbury, R., Shelton, M. A., Kim, S.-M., Hildebrand, M. A., et al. (2023). Circadian rhythm disruptions associated with opioid use disorder in synaptic proteomes of human dorsolateral prefrontal cortex and nucleus accumbens. Mol. Psychiatry 28, 4777–4792. doi: 10.1038/s41380-023-02241-6

PubMed Abstract | Crossref Full Text | Google Scholar

R Core Team (2023). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.

Google Scholar

Rahmatallah, Y., Emmert-Streib, F., and Glazko, G. (2013). Gene sets net correlations analysis (gsnca): a multivariate differential coexpression test for gene sets. Bioinformatics 30, 360–368. doi: 10.1093/bioinformatics/btt687

PubMed Abstract | Crossref Full Text | Google Scholar

Rathor, P., and Ch, R. (2023). Metabolic basis of circadian dysfunction in parkinson's disease. Biology 12:1294. doi: 10.3390/biology12101294

PubMed Abstract | Crossref Full Text | Google Scholar

Raudvere, U., Kolberg, L., Kuzmin, I., Arak, T., Adler, P., Peterson, H., et al. (2019). g:profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 47, W191–198. doi: 10.1093/nar/gkz369

PubMed Abstract | Crossref Full Text | Google Scholar

Reimand, J., Isserlin, R., Voisin, V., Kucera, M., Tannus-Lopes, C., Rostamianfar, A., et al. (2019). Pathway enrichment analysis and visualization of omics data using g:profiler, GSEA, cytoscape and EnrichmentMap. Nat. Protoc. 14, 482–517. doi: 10.1038/s41596-018-0103-9

PubMed Abstract | Crossref Full Text | Google Scholar

Riley, E. D., Kazi, D. S., Coffin, P. O., Vittinghoff, E., Wade, A. N., Bulfone, T. C., et al. (2022). Impact of multiple substance use on circulating st2, a biomarker of adverse cardiac remodelling, in women. Biomarkers 27, 802–808. doi: 10.1080/1354750X.2022.2129451

PubMed Abstract | Crossref Full Text | Google Scholar

Rouillard, A. D., Gundersen, G. W., Fernandez, N. F., Wang, Z., Monteiro, C. D., McDermott, M. G., et al. (2016). The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database 2016:baw100. doi: 10.1093/database/baw100

PubMed Abstract | Crossref Full Text | Google Scholar

Sales, G., Calura, E., Cavalieri, D., and Romualdi, C. (2012). graphite - a bioconductor package to convert pathway topology to gene network. BMC Bioinformatics 13:20. doi: 10.1186/1471-2105-13-20

PubMed Abstract | Crossref Full Text | Google Scholar

Sanchez-Roige, S., Kember, R. L., and Agrawal, A. (2022). Substance use and common contributors to morbidity: a genetics perspective. eBioMedicine 83:104212. doi: 10.1016/j.ebiom.2022.104212

PubMed Abstract | Crossref Full Text | Google Scholar

Sánchez-Valle, J., and Valencia, A. (2023). Molecular bases of comorbidities: present and future perspectives. Trends Genet. 39, 773–786. doi: 10.1016/j.tig.2023.06.003

PubMed Abstract | Crossref Full Text | Google Scholar

Santos, S. d. S, Galatro, T. F. A., Watanabe, R. A., Oba-Shinjo, S. M., Nagahashi Marie, S. K., et al. (2015). Coga: An r package to identify differentially co-expressed gene sets by analyzing the graph spectra. PLoS ONE 10:e0135831. doi: 10.1371/journal.pone.0135831

PubMed Abstract | Crossref Full Text | Google Scholar

Sato, J. R., Takahashi, D. Y., Hoexter, M. Q., Massirer, K. B., and Fujita, A. (2013). Measuring network's entropy in adhd: a new approach to investigate neuropsychiatric disorders. Neuroimage 77, 44–51. doi: 10.1016/j.neuroimage.2013.03.035

PubMed Abstract | Crossref Full Text | Google Scholar

Shen, K. Z., Cox, B. A., and Johnson, S. W. (1997). L-arginine potentiates gaba-mediated synaptic transmission by a nitric oxide-independent mechanism in rat dopamine neurons. Neuroscience 79, 649–658. doi: 10.1016/S0306-4522(97)00024-9

PubMed Abstract | Crossref Full Text | Google Scholar

Smedley, D., Jacobsen, J. O., Jäger, M., Köhler, S., Holtgrewe, M., Schubach, M., et al. (2015). Next-generation diagnostics and disease-gene discovery with the exomiser. Nat. Protoc. 10, 2004–2015. doi: 10.1038/nprot.2015.124

PubMed Abstract | Crossref Full Text | Google Scholar

Solovieff, N., Cotsapas, C., Lee, P. H., Purcell, S. M., and Smoller, J. W. (2013). Pleiotropy in complex traits: challenges and strategies. Nat. Rev. Genet. 14, 483–495. doi: 10.1038/nrg3461

PubMed Abstract | Crossref Full Text | Google Scholar

Stoychev, K., Dilkov, D., Naghavi, E., and Kamburova, Z. (2021). Genetic basis of dual diagnosis: A review of genome-wide association studies (gwas) focusing on patients with mood or anxiety disorders and co-occurring alcohol-use disorders. Diagnostics 11:1055. doi: 10.3390/diagnostics11061055

PubMed Abstract | Crossref Full Text | Google Scholar

Sullivan, P. F., Kendler, K. S., and Neale, M. C. (2003). Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies. Arch. Gen. Psychiatry 60, 1187–1192. doi: 10.1001/archpsyc.60.12.1187

PubMed Abstract | Crossref Full Text | Google Scholar

Takahashi, D. Y., Sato, J. R., Ferreira, C. E., and Fujita, A. (2012). Discriminating different classes of biological networks by analyzing the graphs spectra distribution. PLoS ONE 7:e0049949. doi: 10.1371/journal.pone.0049949

PubMed Abstract | Crossref Full Text | Google Scholar

Thaker, V. V. (2017). Genetic and epigenetic causes of obesity. Adolesc. Med. State Art Rev. 28, 379–405. doi: 10.1542/9781581109405-genetic

Crossref Full Text | Google Scholar

Toska, E., and Mayrovitz, H. N. (2023). Opioid impacts on cardiovascular health. Cureus 15:e46224. doi: 10.7759/cureus.46224

PubMed Abstract | Crossref Full Text | Google Scholar

Tousoulis, D., Böger, R. H., Antoniades, C., Siasos, G., Stefanadi, E., and Stefanadis, C. (2007). Mechanisms of disease: L-arginine in coronary atherosclerosis–a clinical perspective. Nature Clini. Pract. Cardiovascul. Med. 4, 274–283. doi: 10.1038/ncpcardio0878

PubMed Abstract | Crossref Full Text | Google Scholar

Uffelmann, E., Huang, Q. Q., Munung, N. S., de Vries, J., Okada, Y., Martin, A. R., et al. (2021). Genome-wide association studies. Nat. Rev. Methods Primers 1:59. doi: 10.1038/s43586-021-00056-9

Crossref Full Text | Google Scholar

Vavrek, M. J. (2011). fossil: Palaeoecological and palaeogeographical analysis tools, in Palaeontologia Electronica, 14.

Google Scholar

Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., et al. (2020). SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272. doi: 10.1038/s41592-020-0772-5

PubMed Abstract | Crossref Full Text | Google Scholar

Wang, J., Vasaikar, S., Shi, Z., Greer, M., and Zhang, B. (2017). WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic Acids Res. 45, W130–W137. doi: 10.1093/nar/gkx356

PubMed Abstract | Crossref Full Text | Google Scholar

Warrens, M. J., and van der Hoef, H. (2022). Understanding the adjusted rand index and other partition comparison indices based on counting object pairs. J. Classificat. 39, 487–509. doi: 10.1007/s00357-022-09413-z

Crossref Full Text | Google Scholar

Wieder, C., Frainay, C., Poupin, N., Rodrı́guez-Mier, P., Vinson, F., Cooke, J., et al. (2021). Pathway analysis in metabolomics: recommendations for the use of over-representation analysis. PLoS Comput. Biol. 17, 1–23. doi: 10.1371/journal.pcbi.1009105

PubMed Abstract | Crossref Full Text | Google Scholar

Wilk, G., and Braun, R. (2017). Integrative analysis reveals disrupted pathways regulated by micrornas in cancer. Nucleic Acids Res. 46, 1089–1101. doi: 10.1093/nar/gkx1250

PubMed Abstract | Crossref Full Text | Google Scholar

Wilson, L. D., Jeromin, J., Garvey, L., and Dorbandt, A. (2001). Cocaine, ethanol, and cocaethylene cardiotoxity in an animal model of cocaine and ethanol abuse. Acad. Emerg. Med. 8, 211–222. doi: 10.1111/j.1553-2712.2001.tb01296.x

PubMed Abstract | Crossref Full Text | Google Scholar

Wirka, R. C., Pjanic, M., and Quertermous, T. (2018). Advances in transcriptomics. Circ. Res. 122, 1200–1220. doi: 10.1161/CIRCRESAHA.117.310910

PubMed Abstract | Crossref Full Text | Google Scholar

Wormington, B., Thorp, J. G., Gerring, Z. F., Scott, J. G., Akosile, W., and Derks, E. M. (2024). The genetic architecture of substance use and its diverse correlations with mental health traits. Psychiatry Res. 342:116200. doi: 10.1016/j.psychres.2024.116200

PubMed Abstract | Crossref Full Text | Google Scholar

Wu, Z., and Wu, H. (2020). Accounting for cell type hierarchy in evaluating single cell rna-seq clustering. Genome Biol. 21:123. doi: 10.1186/s13059-020-02027-x

PubMed Abstract | Crossref Full Text | Google Scholar

Wydra, K., Frankowska, M., and Filip, M. (2024). Recent Advances on GABAB Receptor Positive Allosteric Modulators as Potential Pharmacotherapies for Substance Use Disorder and Food Addiction. (Cham: Springer International Publishing), 239–258.

Google Scholar

Xiong, Y., Guo, M., Ruan, L., Kong, X., Tang, C., Zhu, Y., et al. (2019). Heterogeneous network embedding enabling accurate disease association predictions. BMC Med. Genomics 12:186. doi: 10.1186/s12920-019-0623-3

PubMed Abstract | Crossref Full Text | Google Scholar

Yeates, K., Lohfeld, L., Sleeth, J., Morales, F., Rajkotia, Y., and Ogedegbe, O. (2015). A global perspective on cardiovascular disease in vulnerable populations. Can. J. Cardiol. 31, 1081–1093. doi: 10.1016/j.cjca.2015.06.035

PubMed Abstract | Crossref Full Text | Google Scholar

Yeung, K. Y., Medvedovic, M., and Bumgarner, R. E. (2003). Clustering gene-expression data with repeated measurements. Genome Biol. 4:R34. doi: 10.1186/gb-2003-4-5-r34

PubMed Abstract | Crossref Full Text | Google Scholar

Zelig, A., Kariti, H., and Kaplan, N. (2023). Kmd clustering: robust general-purpose clustering of biological data. Commun. Biol. 6:1110. doi: 10.1038/s42003-023-05480-z

PubMed Abstract | Crossref Full Text | Google Scholar

Zhao, K., and Rhee, S. Y. (2023). Interpreting omics data with pathway enrichment analysis. Trends in Genetics 39, 308–319. doi: 10.1016/j.tig.2023.01.003

PubMed Abstract | Crossref Full Text | Google Scholar

Zhou, H., Polimanti, R., Yang, B. Z., Wang, Q., Han, S., Sherva, R., et al. (2017). Genetic risk variants associated with comorbid alcohol dependence and major depression. JAMA Psychiatry 74, 1234–1241. doi: 10.1001/jamapsychiatry.2017.3275

PubMed Abstract | Crossref Full Text | Google Scholar

Zhukovsky, P., Tio, E. S., Coughlan, G., Bennett, D. A., Wang, Y., Hohman, T. J., et al. (2024). Genetic influences on brain and cognitive health and their interactions with cardiovascular conditions and depression. Nat. Commun. 15:5207. doi: 10.1038/s41467-024-49430-7

PubMed Abstract | Crossref Full Text | Google Scholar

Zitnik, M., Li, M. M., Wells, A., Glass, K., Morselli Gysi, D., Krishnan, A., et al. (2024). Current and future directions in network biology. Bioinformat. Adv. 4:vbae099. doi: 10.1093/bioadv/vbae099

PubMed Abstract | Crossref Full Text | Google Scholar

Zito, A., Lualdi, M., Granata, P., Cocciadiferro, D., Novelli, A., Alberio, T., et al. (2021). Gene set enrichment analysis of interaction networks weighted by node centrality. Front. Genet. 12:577623. doi: 10.3389/fgene.2021.577623

PubMed Abstract | Crossref Full Text | Google Scholar