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        <title>Frontiers in Bioinformatics | Network Bioinformatics section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/bioinformatics/sections/network-bioinformatics</link>
        <description>RSS Feed for Network Bioinformatics section in the Frontiers in Bioinformatics journal | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-05-11T21:59:38.753+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1755843</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1755843</link>
        <title><![CDATA[Artificial intelligence in drug discovery from advanced molecular representation to pipeline applications]]></title>
        <pubdate>2026-04-07T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Xiaoyu Zhou</author><author>Weijing Tao</author>
        <description><![CDATA[The pharmaceutical research and development (R&D) process is persistently challenged by high financial costs, protracted timelines, and remarkably low success rates. Artificial intelligence (AI) technology, by simulating complex biological systems, has accelerated the innovation of the entire drug discovery pipeline. This review positions AI as a pivotal technology for reengineering the R&D process by utilizing sophisticated molecular representations to predict pharmacodynamic (PD) and toxicological effects significantly earlier. The scope systematically covers the AI foundations in chemoinformatics, detailing how the performance of AI models is intrinsically linked to the quality of molecular representation. We elaborate on representations ranging from robust string-based methods to advanced topological models, including the five key categories of Graph Neural Networks (GNNs), three-dimensional (3D)-aware Geometric Deep Learning (GDL) and emerging Quantum Machine Learning (QML) as well as Hybrid Quantum-Classical Neural Networks (HQNNs). We analyzed the practical application of these models across the drug discovery pipeline, including de novo molecular design with biological foundation models and flow matching generative architectures, data scarcity solutions via Few-Shot Learning and meta-learning, and explainable AI (XAI) for transparent validation. We propose an integrated Q-BioFusion framework that synergizes quantum computing, autonomous experimentation, and generative models to address systemic R&D constraints. We hope future research will improve the geometric fidelity to achieve more accurate and faster 3D molecular prediction and generation, enhance data efficiency, and solve the inherent data sparsity problem in biological assays, and advance integrated XAI workflows. These efforts will ensure transparent, reliable and trustworthy guidance during the computer simulation process of drug design.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1756081</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1756081</link>
        <title><![CDATA[Network pharmacology refined with non-ubiquity and decoy-controlled molecular docking reveals insights into Moringa oleifera phytochemicals targeting insulin resistance]]></title>
        <pubdate>2026-03-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Armi Katrina Santos-Enriquez</author><author>Fabian M. Dayrit</author><author>Armando Jerome de Jesus</author><author>Nina Rosario L. Rojas</author>
        <description><![CDATA[Moringa oleifera phytochemicals were predicted to target insulin resistance proteins using a modified network pharmacology and molecular docking approach. Two hundred ninety M. oleifera phytochemicals with their aglycones, acetylase and myrosinase degradation products were compiled from literature and phytochemical databases. Nine protein targets were identified from the intersection of gene lists with high relevance to insulin resistance from GeneCards and DisGeNET and the target genes predicted by reverse screening using Swiss Target Prediction: protein-tyrosine phosphatase 1B (PTPN1), 11-beta-hydroxysteroid dehydrogenase 1 (HSD11B1), peroxisome proliferator-activated receptor α (PPARα), peroxisome proliferator-activated receptor γ (PPARγ), PI3-kinase p85-alpha subunit (PIK3R1), insulin receptor (INSR), tumor necrosis factor α (TNF-α), endothelial nitric oxide synthase (eNOS) and hepatic lipase (LIPC). Binding affinities of phytochemicals with the targets were predicted using Autodock Vina. The predicted binding affinities were classified according to calculated thresholds using receiver operating characteristic (ROC) calculations of binding affinities of: (a) binders (annotated drugs and other molecules with known interaction with each target), and (b) decoys (molecules not expected to bind to a specific target). In addition, ubiquitous phytochemicals were filtered out to differentiate the effect on insulin resistance of M. oleifera from that of other plants. The resulting phytochemical-protein interaction network was visualized using Cytoscape. All mentioned targets, except hepatic lipase, were key targets based on various network centrality measures. Previous studies on murine models have shown that isothiocyanate-rich M. oleifera extracts ameliorate insulin resistance. Using our approach, the following phytochemicals, with predicted moderate bioavailability, high GI absorption, and probable binding with insulin resistance targets, are recommended for further in vivo or in vitro validation for insulin resistance activity: boldione (a steroid); aurantiamide acetate and aurantiamide (peptide derivatives); O-ethyl-[(3,4-dihydroxyphenyl)methyl] carbamothioate and O-methyl-N-[(4-hydroxyphenyl)methyl] carbamothioate (thiocarbamates); 4α,6α-dihydroxyeudesman-8β,12-olide (a sesquiterpenoid); sanleng acid and tianshic acid (fatty acid derivatives); 2′,5,5′,7-tetrahydroxyflavone; 2′,3,5,7-tetrahydroxyflavone; and 6-hydroxykaempferol (flavonoids). By combining network centrality measures of targets, using ROC-derived thresholds for docking energies, and considering ubiquity of phytochemicals, our refined network pharmacology approach may aid in discovering key bioactive phytochemicals as potential chemical markers for standardization and differentiation of an herbal drug.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1702572</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1702572</link>
        <title><![CDATA[Unveiling the potential of apigenin and kaempferol against colon cancer: an integrated network pharmacology and docking approach]]></title>
        <pubdate>2026-02-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Anushya Selvakumar</author><author>Perpetual Ansel Chandran</author><author>Sai Shraddha</author><author>Loganathan Chandramani Priya Dharshini</author><author>Sarath Perumal</author><author>Ramanathan Karuppasamy</author><author>Abul Kalam Azad Mandal</author>
        <description><![CDATA[BackgroundColon cancer is one of the prevalent and deadly malignancies, requiring advanced treatment strategies.MethodsIMPPAT database, drug-likeliness, bioavailability scores, and Lipinski/Ghosh rules were utilized to screen the phytochemicals. STITCH, SwissTargetPrediction, CTD, and GeneCards were utilized for target gene retrieval (Apigenin and Kaempferol). From GeneCards, OMIM, and the NCBI Ensembl database, colon cancer-related genes were collected. The PPI network was built from the overlapping genes using STRING and Cytoscape. 10 hub genes were screened using the MCC algorithm and subjected to functional enrichment and mutation frequency analysis. Genes with high mutation frequency were selected for molecular docking and MDS.ResultsA total of 292 overlapping targets between the two compounds and colon cancer-related genes were identified. The PPI network resulted in ten hub genes (AKT1, IL6, JUN, NFKB1, STAT3, TNF, BCL2, IL1B, HIF1A, and TGFB1). These were significantly enriched in key oncogenic pathways. Mutation frequency analysis revealed recurrent alterations in AKT1, NFKB1, and HIF1A. Docking studies showed strong binding of Apigenin and Kaempferol with AKT1, exhibiting binding energies of −9.4 and −9.2 kcal/mol, respectively. To further assess the binding stability of the apigenin–AKT1 complex, a 100 ns MDS was performed, which confirmed the structural stability.ConclusionApigenin and kaempferol showed potential as dual-targeting agents for colon cancer therapy. Cell culture and animal model studies in future are warranted to substantiate the mechanistic roles in tumor suppression.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1654326</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1654326</link>
        <title><![CDATA[Identification and validation of tumor microenvironment-related therapeutic targets in gastric cancer using integrated multi-omics and molecular docking approaches]]></title>
        <pubdate>2025-12-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mohamed Kalith Oli M.</author><author>Jafar Ali Ibrahim Syed Masood</author>
        <description><![CDATA[IntroductionWith increased drug resistance and tumor heterogeneity accounting for limited therapeutic strategies, gastric cancer remains one of the major causes of cancer-related mortality around the globe. Targeting the components of the tumor microenvironment (TME) has become a promising therapeutic strategy due to their crucial roles in cancer cell proliferation, progression, and metastasis. One of the limitations of the previously identified therapeutic targets is their limited applicability to a broader patient population.MethodsThis study aims to identify (TME)-related therapeutic targets using an integrated bioinformatics and molecular docking approach that involves a larger number of datasets to cover a broader cohort of gastric cancer patients. It analyzed multiple publicly available transcriptomic datasets using Robust Rank Aggregation (RRA) meta-analysis and Weighted Gene Co-expression Network Analysis (WGCNA) to identify significant hub genes. Furthermore, protein-protein interaction (PPI) network analyses, conducted using multiple methods such as Cytohubba topology analysis and ClusterONE module analysis, refined the potential therapeutic candidates. Functional enrichment analyses were performed to identify vital genes involved in TME interactions and ECM remodeling.ResultsThe enriched genes were validated for their significant dysregulation in the Cancer Genome Atlas gastric adenocarcinoma dataset (TCGA-STAD) and three independent GEO datasets to ensure differential expression across distinct cohorts. Genes with consistent dysregulation were used in survival analyses across TCGA and two GEO datasets to prioritize hub genes with prognostic significance. Finally, a targeted literature survey ensured the exclusion of previously targeted genes, and molecular docking analyses conducted using phytocompounds identified potential therapeutic leads with strong affinities for the identified targets.DiscussionThis integrated approach revealed notable, promising targets in the TME and natural compounds for developing potential personalized therapeutic strategies in gastric cancer.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1674179</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1674179</link>
        <title><![CDATA[Gene expression profile in colon cancer therapeutic resistance and its relationship with the tumor microenvironment]]></title>
        <pubdate>2025-10-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Priscila Galvão Doria</author><author>Gisele Vieira Rocha</author><author>Vanessa Dybal Bertoni</author><author>Roberto de Souza Batista dos Santos</author><author>Mariana Araújo-Pereira</author><author>Clarissa Gurgel</author>
        <description><![CDATA[IntroductionColon cancer is a common disease, treated with few chemotherapeutic agents with similar treatment sequencing despite its heterogeneity. A significant proportion of patients are diagnosed with metastasis, and resistance to antineoplastic drugs is associated with disease progression and therapeutic failure. It is known that the tumor microenvironment plays an essential role in cancer progression, contributing to processes that may be associated with therapeutic resistance mechanisms in colon cancer. In this study, we aim to identify a gene expression signature and its relationship with immune cell infiltration in colon cancer, contributing to the identification of potential resistance biomarkers.MethodsAn in silico study was conducted using RNA-seq data from The Cancer Genome Atlas Program (TCGA) samples, subdivided into two groups (treatment-resistant and non-resistant), taking into account the molecular subgroups (CMS1, CMS2, CMS3, and CMS4). The following algorithms were used: i. Limma was applied to identify differentially expressed genes; ii. WGCNA was applied to construct co-expression networks; iii. CIBERSORT was applied to estimate the proportion of infiltrating immune cells; and iv. TIMER was applied to explore the relationship between core genes and immune cell content.ResultsTwenty differentially expressed genes (DEGs) were found, with 18 related to the group considered resistant to oncologic treatment and presenting poorer overall survival. T CD4 memory resting cells and M0 and M2 macrophages were found in more significant proportions in the analyzed samples and more infiltrated in the tumor microenvironment, the higher the expression of some of these resistance DEGs. Additionally, these genes correlate with biological aspects of neuronal differentiation, axogenesis, and synaptic transmission.ConclusionThe gene expression signature suggests the presence of differentially expressed synaptic membrane genes, which may be involved in neuronal pathways that influence the tumor microenvironment, potentially serving as future biomarkers. Furthermore, the presence of M0 and M2 macrophages and T CD4 memory resting cells suggests a potential interaction that may play a role in therapeutic resistance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1666716</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1666716</link>
        <title><![CDATA[Drug repositioning pipeline integrating community analysis in drug-drug similarity networks and automated ATC community labeling to foster molecular docking analysis]]></title>
        <pubdate>2025-10-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Daiana Colibăşanu</author><author>Vlad Groza</author><author>Maria Antonietta Occhiuzzi</author><author>Fedora Grande</author><author>Mihai Udrescu</author><author>Lucreția Udrescu</author>
        <description><![CDATA[IntroductionDrug repositioning—finding new therapeutic uses for existing drugs—can dramatically reduce development time and cost, but requires efficient computational frameworks to generate and validate repositioning hypotheses. Network-based methods can uncover drug communities with shared pharmacological properties, while molecular docking offers mechanistic insights by predicting drug–target binding.MethodsWe introduce an end-to-end, fully automated pipeline that (1) constructs a tripartite drug-gene-disease network from DrugBank and DisGeNET, (2) projects it into a drug-drug similarity network for community detection, (3) labels communities via Anatomical Therapeutic Chemical (ATC) codes to generate repositioning hints and identify relevant targets, (4) validates hints through automated literature searches, and (5) prioritizes candidates via targeted molecular docking.ResultsAfter filtering for connectivity and size, 12 robust communities emerged from the initial 34 clusters. The pipeline correctly matched 53.4% of drugs to their ATC level 1 community label via database entries; literature validation confirmed an additional 20.2%, yielding 73.6% overall accuracy. The remaining 26.4% of drugs were flagged as repositioning candidates. To illustrate the advantages of our pipeline, molecular docking studies of chloramphenicol demonstrated stable binding and interaction profiles similar to those of known inhibitors, reinforcing its potential as an anticancer agent.ConclusionOur integrated pipeline effectively integrates network-based community analysis and automated ATC labeling with literature and docking analysis, narrowing the search space for in silico and experimental follow-up. The chloramphenicol example illustrates its utility for uncovering non-obvious repositioning opportunities. Future work will extend similarity definitions (e.g., to higher-order network motifs) and incorporate wet-lab validation of top candidates.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1664576</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1664576</link>
        <title><![CDATA[Integrative machine learning and transcriptomic analysis identifies key molecular targets in MNPN-associated oral squamous cell carcinoma pathogenesis]]></title>
        <pubdate>2025-09-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiangjun Wang</author><author>Panpan Jin</author><author>Juan Xu</author><author>Junyi Li</author><author>Mengzhen Ji</author>
        <description><![CDATA[BackgroundOral squamous cell carcinoma (OSCC) represents a significant global health challenge, with betel nut consumption being a major risk factor. 3-(methylnitrosamino)propionitrile (MNPN), a betel nut-derived nitrosamine, has been identified as a potential carcinogen, but its molecular targets in OSCC pathogenesis remain poorly understood.MethodsWe employed a comprehensive computational framework integrating target prediction, transcriptomic analysis, weighted gene co-expression network analysis (WGCNA), and machine learning approaches. Four OSCC datasets from Gene Expression Omnibus (GEO) were analyzed, and MNPN targets were predicted using ChEMBL, PharmMapper, and SwissTargetPrediction databases. Machine learning algorithms (n = 127 combinations) were evaluated for optimal biomarker identification, with model interpretability assessed using SHAP (SHapley Additive exPlanations) analysis.ResultsTarget prediction identified 881 potential MNPN targets across three databases. WGCNA revealed 534 OSCC-associated differentially expressed genes, with 38 overlapping MNPN targets. Machine learning optimization identified 13 hub genes, with PLAU demonstrating the highest predictive performance (AUC = 0.944). SHAP analysis confirmed PLAU and PLOD3 as the most influential contributors to disease prediction. Functional enrichment analysis revealed MNPN targets’ involvement in xenobiotic response, hypoxic conditions, and aberrant tissue remodeling.ConclusionThis study provides the first comprehensive molecular characterization of MNPN-associated OSCC pathogenesis, identifying PLAU as a critical therapeutic target with exceptional diagnostic potential. Our findings establish a foundation for developing targeted interventions for betel nut nitrosamine-associated oral cancers and demonstrate the power of integrative computational approaches in environmental carcinogen research.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1613136</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1613136</link>
        <title><![CDATA[Discovering biomarkers for chronic sinusitis with nasal polyps: a study integrating bioinformatics analysis and experimental validation of macrophage polarization and metabolism-related genes]]></title>
        <pubdate>2025-09-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Juan Zhou</author><author>Huan Wang</author><author>Jin Wang</author><author>Fuming Zhou</author>
        <description><![CDATA[BackgroundMacrophages play a critical role in chronic rhinosinusitis with nasal polyps (CRSwNP), and their functional imbalance may cause metabolic disturbances. However, the mechanisms of their role in CRSwNP remain unclear. This study aimed to identify CRSwNP biomarkers related to macrophage polarization and metabolism, and elucidate their molecular regulatory mechanisms.MethodsIn this study, transcriptomic data of chronic rhinosinusitis with nasal polyps (CRSwNP) were obtained from public databases. Differentially expressed genes (DEGs) were screened via differential expression analysis. Subsequently, weighted gene co-expression network analysis (WGCNA) was used to identify key module genes related to macrophage polarization-related genes (MP-RGs), which were then cross-referenced with metabolism-related genes to screen for candidate genes. After that, two machine learning methods—least absolute shrinkage and selection operator (LASSO) and random forest (RF)—were applied to further screen these candidate genes. Receiver operating characteristic (ROC) curves for the training set and validation set were constructed, and gene expression validation was conducted to finally determine the biomarkers. Finally, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to verify the expression levels of prognostic genes.ResultsALOX5, HMOX1, and PLA2G7 were identified as biomarkers for CRSwNP, with AUC >0.7 in both training and validation sets, showing strong diagnostic potential. A nomogram, built on these three biomarkers, exhibited superior diagnostic performance. Enrichment analysis suggested that these biomarkers might be implicated in immune pathways. Furthermore, all three biomarkers were found to be correlated with asthma. Selenium was identified as a co-target of ALOX5 and HMOX1, presenting potential therapeutic targets for CRSwNP. A total of 10 key miRNAs regulating these biomarkers were identified, and the upstream long non-coding RNAs of hsa-miR-642a-5p, including FOXC1 and NEAT1, were predicted. Additionally, the transcription factor FOXC1 was found to concurrently regulate all three biomarkers. RT-qPCR results validated that the expression levels of ALOX5, HMOX1, and PLA2G7 were significantly elevated in CRSwNP patients, corroborating the findings from bioinformatics analyses.ConclusionALOX5, HMOX1, and PLA2G7 were identified as biomarkers linked to macrophage polarization and metabolism in CRSwNP. These findings offer new insights for early prevention strategies and clinical drug development in CRSwNP.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1685992</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1685992</link>
        <title><![CDATA[Editorial: Networks and graphs in biological data: current methods, opportunities and challenges]]></title>
        <pubdate>2025-09-02T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Derek L. Thompson</author><author>Hsiang-Yun Wu</author><author>Christopher W. Bartlett</author><author>William C. Ray</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1637479</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1637479</link>
        <title><![CDATA[The bacteriostatic regulation of luteolin from honeysuckle by protein network interaction]]></title>
        <pubdate>2025-08-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jianfeng Zhang</author><author>Mujun Chen</author><author>Dianzeng Yang</author><author>Yanjie Jia</author>
        <description><![CDATA[A comprehensive analysis of the bacteriostatic mechanism of luteolin at the molecular level was performed. Luteolin-related targets were first retrieved from the STITCH database, followed by the acquisition of protein-protein interaction (PPI) information from the STRING database. The retrieved PPI data was subsequently imported into Cytoscape software to construct a PPI network. Finally, the Molecular Complexity Detection (MCODE) algorithm and BinGo plugin were utilized to conduct module analysis and functional annotation of the constructed network, respectively. The results showed that a total of ten targets were successfully screened from the database. Based on these targets, a PPI network consisting of 91 nodes and 332 edges was constructed. Cluster analysis identified seven distinct functional modules, and subsequent module analysis further demonstrated that luteolin was primarily involved in multiple biological processes, including pathogenic bacteria resistance, antibacterial defensive responses, pathogenic fungi resistance, and resistance to both gram-negative and gram-positive bacteria. These findings indicated that luteolin exhibits robust antibacterial and antifungal activities. By investigating the inhibitory mechanism of luteolin at the molecular-network level, this study paves the way for the development of novel bacteriostatic strategies, offering a valuable perspective for related research.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1574797</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1574797</link>
        <title><![CDATA[Bridging the gap between hepatocellular carcinoma management guidelines and personalised medicine: a Bayesian network study]]></title>
        <pubdate>2025-05-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yi-Chun Wang</author><author>Daniel Bulte</author><author>Michael Brady</author>
        <description><![CDATA[IntroductionThere are numerous treatment options available for patients with confirmed hepatocellular carcinoma (HCC). Guidelines such as Barcelona Clinic Liver Cancer (BCLC) support treatment decisions by way of a flow diagram that is organized around groups of patients. Though such guidelines continue to make a major contribution to standardization of treatment, in clinical reality, cases are often more nuanced than is captured in any flow diagram, even one as comprehensive as BCLC. A fundamental challenge for a clinician is to combine such a population-wide guideline with specific information about the individual patient. Bayesian networks (BNs) offer a way to “bridge this gap” and combine standardized care and precision medicine. They do this by enabling answers to detailed “what-if” questions from the clinician.MethodsWe use real-world data of HCC patients who received treatments between 2019 and 2020 to construct a BN to assess the potential treatment effect for cases that were not treated in compliance with BCLC.ResultsWe report detailed scenarios for ten randomly selected cases and summarise the difference in survival time for each scenario. For each case, the counterfactual treatment scenarios are made based on whether or not the case is in compliance with BCLC guidelines, the type of treatment received and the waiting time to receive treatment.DiscussionWe consider two cases with similar clinical characteristics (but received different treatments) and discuss whether or not they are treated in compliance to the guidelines resulting in better outcomes than the actual clinical decision. We include a detailed discussion about the assumptions made in constructing the BN and we highlight why such a BN can serve as an AI-based clinical decision support system particularly when there is need for further patient stratification.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2024.1419274</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2024.1419274</link>
        <title><![CDATA[Rhizobium etli CFN42 and Sinorhizobium meliloti 1021 bioinformatic transcriptional regulatory networks from culture and symbiosis]]></title>
        <pubdate>2024-08-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hermenegildo Taboada-Castro</author><author>Alfredo José Hernández-Álvarez</author><author>Juan Miguel Escorcia-Rodríguez</author><author>Julio Augusto Freyre-González</author><author>Edgardo Galán-Vásquez</author><author>Sergio Encarnación-Guevara</author>
        <description><![CDATA[Rhizobium etli CFN42 proteome–transcriptome mixed data of exponential growth and nitrogen-fixing bacteroids, as well as Sinorhizobium meliloti 1021 transcriptome data of growth and nitrogen-fixing bacteroids, were integrated into transcriptional regulatory networks (TRNs). The one-step construction network consisted of a matrix-clustering analysis of matrices of the gene profile and all matrices of the transcription factors (TFs) of their genome. The networks were constructed with the prediction of regulatory network application of the RhizoBindingSites database (http://rhizobindingsites.ccg.unam.mx/). The deduced free-living Rhizobium etli network contained 1,146 genes, including 380 TFs and 12 sigma factors. In addition, the bacteroid R. etli CFN42 network contained 884 genes, where 364 were TFs, and 12 were sigma factors, whereas the deduced free-living Sinorhizobium meliloti 1021 network contained 643 genes, where 259 were TFs and seven were sigma factors, and the bacteroid Sinorhizobium meliloti 1021 network contained 357 genes, where 210 were TFs and six were sigma factors. The similarity of these deduced condition-dependent networks and the biological E. coli and B. subtilis independent condition networks segregates from the random Erdös–Rényi networks. Deduced networks showed a low average clustering coefficient. They were not scale-free, showing a gradually diminishing hierarchy of TFs in contrast to the hierarchy role of the sigma factor rpoD in the E. coli K12 network. For rhizobia networks, partitioning the genome in the chromosome, chromids, and plasmids, where essential genes are distributed, and the symbiotic ability that is mostly coded in plasmids, may alter the structure of these deduced condition-dependent networks. It provides potential TF gen–target relationship data for constructing regulons, which are the basic units of a TRN.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2024.1358374</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2024.1358374</link>
        <title><![CDATA[A layout framework for genome-wide multiple sequence alignment graphs]]></title>
        <pubdate>2024-08-16T00:00:00Z</pubdate>
        <category>Technology and Code</category>
        <author>Jeremias Schebera</author><author>Dirk Zeckzer</author><author>Daniel Wiegreffe</author>
        <description><![CDATA[Sequence alignments are often used to analyze genomic data. However, such alignments are often only calculated and compared on small sequence intervals for analysis purposes. When comparing longer sequences, these are usually divided into shorter sequence intervals for better alignment results. This usually means that the order context of the original sequence is lost. To prevent this, it is possible to use a graph structure to represent the order of the original sequence on the alignment blocks. The visualization of these graph structures can provide insights into the structural variations of genomes in a semi-global context. In this paper, we propose a new graph drawing framework for representing gMSA data. We produce a hierarchical graph layout that supports the comparative analysis of genomes. Based on a reference, the differences and similarities of the different genome orders are visualized. In this work, we present a complete graph drawing framework for gMSA graphs together with the respective algorithms for each of the steps. Additionally, we provide a prototype and an example data set for analyzing gMSA graphs. Based on this data set, we demonstrate the functionalities of the framework using two examples.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2024.1365200</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2024.1365200</link>
        <title><![CDATA[Network analysis of driver genes in human cancers]]></title>
        <pubdate>2024-07-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shruti S. Patil</author><author>Steven A. Roberts</author><author>Assefaw H. Gebremedhin</author>
        <description><![CDATA[Cancer is a heterogeneous disease that results from genetic alteration of cell cycle and proliferation controls. Identifying mutations that drive cancer, understanding cancer type specificities, and delineating how driver mutations interact with each other to establish disease is vital for identifying therapeutic vulnerabilities. Such cancer specific patterns and gene co-occurrences can be identified by studying tumor genome sequences, and networks have proven effective in uncovering relationships between sequences. We present two network-based approaches to identify driver gene patterns among tumor samples. The first approach relies on analysis using the Directed Weighted All Nearest Neighbors (DiWANN) model, which is a variant of sequence similarity network, and the second approach uses bipartite network analysis. A data reduction framework was implemented to extract the minimal relevant information for the sequence similarity network analysis, where a transformed reference sequence is generated for constructing the driver gene network. This data reduction process combined with the efficiency of the DiWANN network model, greatly lowered the computational cost (in terms of execution time and memory usage) of generating the networks enabling us to work at a much larger scale than previously possible. The DiWANN network helped us identify cancer types in which samples were more closely connected to each other suggesting they are less heterogeneous and potentially susceptible to a common drug. The bipartite network analysis provided insight into gene associations and co-occurrences. We identified genes that were broadly mutated in multiple cancer types and mutations exclusive to only a few. Additionally, weighted one-mode gene projections of the bipartite networks revealed a pattern of occurrence of driver genes in different cancers. Our study demonstrates that network-based approaches can be an effective tool in cancer genomics. The analysis identifies co-occurring and exclusive driver genes and mutations for specific cancer types, providing a better understanding of the driver genes that lead to tumor initiation and evolution.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2023.1328262</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2023.1328262</link>
        <title><![CDATA[A breast cancer-specific combinational QSAR model development using machine learning and deep learning approaches]]></title>
        <pubdate>2024-01-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Anush Karampuri</author><author>Shyam Perugu</author>
        <description><![CDATA[Breast cancer is the most prevalent and heterogeneous form of cancer affecting women worldwide. Various therapeutic strategies are in practice based on the extent of disease spread, such as surgery, chemotherapy, radiotherapy, and immunotherapy. Combinational therapy is another strategy that has proven to be effective in controlling cancer progression. Administration of Anchor drug, a well-established primary therapeutic agent with known efficacy for specific targets, with Library drug, a supplementary drug to enhance the efficacy of anchor drugs and broaden the therapeutic approach. Our work focused on harnessing regression-based Machine learning (ML) and deep learning (DL) algorithms to develop a structure-activity relationship between the molecular descriptors of drug pairs and their combined biological activity through a QSAR (Quantitative structure-activity relationship) model. 11 popularly known machine learning and deep learning algorithms were used to develop QSAR models. A total of 52 breast cancer cell lines, 25 anchor drugs, and 51 library drugs were considered in developing the QSAR model. It was observed that Deep Neural Networks (DNNs) achieved an impressive R2 (Coefficient of Determination) of 0.94, with an RMSE (Root Mean Square Error) value of 0.255, making it the most effective algorithm for developing a structure-activity relationship with strong generalization capabilities. In conclusion, applying combinational therapy alongside ML and DL techniques represents a promising approach to combating breast cancer.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2023.1214074</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2023.1214074</link>
        <title><![CDATA[Genome-scale metabolic models consistently predict in vitro characteristics of Corynebacterium striatum]]></title>
        <pubdate>2023-10-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Famke Bäuerle</author><author>Gwendolyn O. Döbel</author><author>Laura Camus</author><author>Simon Heilbronner</author><author>Andreas Dräger</author>
        <description><![CDATA[Introduction: Genome-scale metabolic models (GEMs) are organism-specific knowledge bases which can be used to unravel pathogenicity or improve production of specific metabolites in biotechnology applications. However, the validity of predictions for bacterial proliferation in in vitro settings is hardly investigated.Methods: The present work combines in silico and in vitro approaches to create and curate strain-specific genome-scale metabolic models of Corynebacterium striatum.Results: We introduce five newly created strain-specific genome-scale metabolic models (GEMs) of high quality, satisfying all contemporary standards and requirements. All these models have been benchmarked using the community standard test suite Metabolic Model Testing (MEMOTE) and were validated by laboratory experiments. For the curation of those models, the software infrastructure refineGEMs was developed to work on these models in parallel and to comply with the quality standards for GEMs. The model predictions were confirmed by experimental data and a new comparison metric based on the doubling time was developed to quantify bacterial growth.Discussion: Future modeling projects can rely on the proposed software, which is independent of specific environmental conditions. The validation approach based on the growth rate calculation is now accessible and closely aligned with biological questions. The curated models are freely available via BioModels and a GitHub repository and can be used. The open-source software refineGEMs is available from https://github.com/draeger-lab/refinegems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2023.1276934</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2023.1276934</link>
        <title><![CDATA[Towards Chinese text and DNA shift encoding scheme based on biomass plasmid storage]]></title>
        <pubdate>2023-10-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xu Yang</author><author>Langwen Lai</author><author>Xiaoli Qiang</author><author>Ming Deng</author><author>Yuhao Xie</author><author>Xiaolong Shi</author><author>Zheng Kou</author>
        <description><![CDATA[DNA, as the storage medium in organisms, can address the shortcomings of existing electromagnetic storage media, such as low information density, high maintenance power consumption, and short storage time. Current research on DNA storage mainly focuses on designing corresponding encoders to convert binary data into DNA base data that meets biological constraints. We have created a new Chinese character code table that enables exceptionally high information storage density for storing Chinese characters (compared to traditional UTF-8 encoding). To meet biological constraints, we have devised a DNA shift coding scheme with low algorithmic complexity, which can encode any strand of DNA even has excessively long homopolymer. The designed DNA sequence will be stored in a double-stranded plasmid of 744bp, ensuring high reliability during storage. Additionally, the plasmid‘s resistance to environmental interference ensuring long-term stable information storage. Moreover, it can be replicated at a lower cost.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2023.1254668</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2023.1254668</link>
        <title><![CDATA[Editorial: Expert Opinions in Network bioinformatics: 2022]]></title>
        <pubdate>2023-07-19T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Paola Lecca</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2023.1197310</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2023.1197310</link>
        <title><![CDATA[A guide for developing comprehensive systems biology maps of disease mechanisms: planning, construction and maintenance]]></title>
        <pubdate>2023-06-22T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Alexander Mazein</author><author>Marcio Luis Acencio</author><author>Irina Balaur</author><author>Adrien Rougny</author><author>Danielle Welter</author><author>Anna Niarakis</author><author>Diana Ramirez Ardila</author><author>Ugur Dogrusoz</author><author>Piotr Gawron</author><author>Venkata Satagopam</author><author>Wei Gu</author><author>Andreas Kremer</author><author>Reinhard Schneider</author><author>Marek Ostaszewski</author>
        <description><![CDATA[As a conceptual model of disease mechanisms, a disease map integrates available knowledge and is applied for data interpretation, predictions and hypothesis generation. It is possible to model disease mechanisms on different levels of granularity and adjust the approach to the goals of a particular project. This rich environment together with requirements for high-quality network reconstruction makes it challenging for new curators and groups to be quickly introduced to the development methods. In this review, we offer a step-by-step guide for developing a disease map within its mainstream pipeline that involves using the CellDesigner tool for creating and editing diagrams and the MINERVA Platform for online visualisation and exploration. We also describe how the Neo4j graph database environment can be used for managing and querying efficiently such a resource. For assessing the interoperability and reproducibility we apply FAIR principles.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2023.1222711</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2023.1222711</link>
        <title><![CDATA[Editorial: Graph representation learning in biological network]]></title>
        <pubdate>2023-06-09T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Swarup Roy</author><author>Pietro Hiram Guzzi</author><author>Jugal Kalita</author>
        <description></description>
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