SaGP: identifying plant saline-alkali tolerance genes based on machine learning techniques

Mining novel genes underlying agronomical traits is a crucial subject in plant biology, essential for enhancing crop quality, ensuring food security, and preserving biodiversity. Wet experiments are the main methods to uncover genes with target functions but are expensive and time-consuming. Machine learning, in contrast, can accelerate the gene discovery process by learning from accumulated data, making it more efficient and cost-effective. However, despite their potential, existing machine-learning tools to mine stress-resistant genes in plants are scarce. In this study, we developed the first known machine learning model, SaGP (Saline-alkali Genes Prediction), to identify plant saline-alkali tolerance genes based on sequencing data. It outperformed traditional computational tools, i.e., BLAST, and correctly identified the latest published genes. Moreover, we utilized SaGP to evaluate three recently published genes: GhAG2, MdBPR6, and TaCCD1. SaGP correctly identified all their functions. Overall, these results suggest that SaGP can be used for the large-scale identification of saline-alkali tolerance genes and served as a framework for the development of additional automated tools, thus promoting crop breeding and plant conservation. To efficiently identify salt-alkali resistant genes in large-scale data, we developed a user-friendly, freely accessible web service platform based on SaGP (https://www.sagprediction.com/).

1 Introduction

Enhancing plants’ tolerance to abiotic stresses has long been the focus in biology and breeding science. Early efforts focused purely on plant phenotypes (Meuwissen et al., 2001; Meyer et al., 2012). Later works began to decipher the genetic bases underlying key traits based on quantitative trait loci (QTL) mapping (Kang et al., 2019) and genome-wide association studies (GWAS) (Gupta, 2021). Many functional genetic variants have been identified, resulting in breeding plants with excellent traits (Wang et al., 2016; Zhao et al., 2024) and developing effective species conservation strategies (Chen et al., 2022; Gougherty et al., 2021). However, despite these achievements, these works are time-consuming and costly (Dou et al., 2021) and overall have low precision in determining functional variants (Mackay et al., 2009; Wray et al., 2013), resulting in inefficient plant selection and breeding. Moreover, they focus on a few model species, such as Arabidopsis, maize, rice, etc. Taking full advantage of knowledge from these species and utilizing them to boost the identification of functional genetic variants in other species are still challenging.

With the development of genetics and informatics, a new framework is now being proposed to boost efficiency and cut the cost of current research, i.e., Breeding 4.0 (Wallace et al., 2018). It is characterized by high-throughput sequence data (Ding et al., 2023; Yang et al., 2013) combined with computational methods (Jarrahi, 2018). Traditional computational methods, such as BLAST (Altschul et al., 1990), may fit Breeding 4.0, but their poor accuracy can lead to inefficiency (Dai et al., 2020; Li et al., 2022). On the other hand, machine learning (ML) may provide an alternative to traditional computational approaches (Fu et al., 2023; Qiao et al., 2024; Van Dijk et al., 2021). It has been used in genomic selection-assisted breeding (Yan and Wang, 2023) and in assessing plants’ vulnerability under future climates regarding their genetic compositions (Sang et al., 2022). Moreover, several studies have implemented machine learning algorithms to identify plant genes with specific functions. For example, PGB was used to detect photosynthetic-related genes based on a voting algorithm (Wang et al., 2022). DRPPP based on SVM was created to predict disease resistance proteins with high performances (Pal et al., 2016). ConSReg based on regularized LASSO was developed to identify key transcription factors responsive to specific abiotic stresses, which outperformed traditional enrichment-based methods (Song et al., 2020). These works can provide important tools for Breeding 4.0 to precisely screen target genes on a large scale and thus facilitate crop improvement and species conservation. Unfortunately, similar work to identify genes resistant to abiotic stresses are scarce.

In this study, we proposed a framework to construct intelligent tools to identify novel plant abiotic stress resistant genes. Focusing on saline-alkali stress, i.e., excessive accumulation of neutral salts and sodic salts that leads to decreased crop productivity (James et al., 2012) and the loss of native biodiversity worldwide (Briggs and Taws, 2003), we developed the first known machine learning model (SaGP, Saline-alkali Genes Prediction) to identify plant saline-alkali tolerance genes. It achieves 0.99 prediction accuracy better than BLAST assessed with the independent test dataset. To further evaluate the performance of SaGP, we tested some latest published genes, including GhAG2 (Yu et al., 2022), MdBPR6 (Zhang et al., 2023), and TaCCD1 (Cui et al., 2023), and SaGP correctly identified all their functions. Overall, the results suggest that SaGP can be used to fast and accurately identify saline-alkali tolerance genes in plants on a large scale with sequencing data, thus promoting crop breeding and plant conservation. SaGP is freely available at www.sagprediction.com.

2 Results

2.1 Model comparison and SaGP construction

The five cost-sensitive methods performed differently regarding their capacity to distinguish saline-alkali tolerance and non-tolerance genes. Overall, the Weighted Cross-Entropy (WCE) method had the best performances regarding MCC, Balanced Accuracy, and PR-AUC (Figure 1; see Supplementary Figure S1 for Accuracy, F1 score, and ROC-AUC values). Moreover, different groups of features showed different pertinency to the gene function of saline-alkali tolerance. Among them, ACC-PSSM achieved the highest and most stable performances across all metrics, followed by PDT-Profile (Figure 1; Supplementary Figure S1). In contrast, several features, such as ACC and AAAFF, had the lowest performances (Figure 1; Supplementary Figure S1). We further compared the performances of SaGP models constructed using four different feature sets—ACC-PSSM, all features, and the top two and top five groups ranked by MCC—with those of traditional tools HMMER and BLAST. The performances of BLAST were generally low with respect to MCC, Balanced Accuarcy, F1 score, and Accuracy (Figure 2a). Only 84% and 3.6% of saline-alkali tolerance genes can be correctly identified by BLAST (Figure 2a), respectively, suggesting their inability to screen for saline-alkali tolerance genes on a large scale. The set with all features performed slightly better than ACC-PSSM in terms of MCC, while ACC-PSSM performed best regarding F1 score and Balanced Accuracy (Figure 2a). Because extracting all features is time-consuming, we thus implemented SaGP based on ACC-PSSM.

Figure 1

Figure 1. The performances of five cost-sensitive methods and 40 groups of protein features based on the test dataset. EL, Equalization loss; FL, Focal loss; LaL, Logit-adjusted loss; LdaML, Label-distribution-aware margin loss; WCE, weighted cross-entropy; MCC, Matthew’s Correlation Coefficient; PR-AUC, the area under the precision-recall. See Table 3 for the details of 40 groups of protein features.

Figure 2

Figure 2. (a) The performances of ACC-PSSM, all features, MCC2, MCC5, BLAST and HMMER based on the test dataset. (b) Performance comparison of SVM, RF, XGBoost, DNN, and SaGP on the independent test dataset.

Next, we compared the classification performances of the SaGP with the other four classifiers—SVM, Random Forest (RF), XGBoost, and deep neural network (DNN)—under the same cost-sensitive learning setting using the WCE loss function. The comparison was based on five evaluation metrics: Accuracy, F1 score, Area Under the ROC Curve (AUROC), Area Under the Precision-Recall Curve (AUPRC), and MCC. Among all models, SaGP outperformed all other classifiers, achieving the highest MCC (0.5988) and AUPRC (0.6021) (Table 1, Figure 2b), which underscores its superior ability to correctly identify saline-alkali tolerance genes under imbalanced conditions. It also attained competitive values in F1 score (0.5563) and AUROC (0.9408) (Table 1, Figure 2b), indicating both reliable classification and strong ranking capability.

Table 1

Table 1. Performance of SVM, RF, XGBoost, DNN, and SaGP on the independent test dataset.

To further evaluate the capacity of SaGP to identify novel saline-alkali tolerance genes, we predicted the three latest published genes, i.e., GhAG2 (Yu et al., 2022), MdBPR6 (Zhang et al., 2023), and TaCCD1 (Cui et al., 2023). The predictions were consistent with the experimental results in the literature (Table 2), supporting that SaGP can correctly uncover novel saline-alkali tolerance genes.

Table 2

Table 2. The 40 groups of protein features extracted in our study, their abbreviations, and corresponding tools.

2.2 Feature importance analysis

We next analyzed the contribution of individual ACC-PSSM features to the SaGP. Based on gain values, the top 20 most important features were identified (Figure 3a). Features such as ACC_PSSM_F3215, ACC_PSSM_F2649, and ACC_PSSM_F137 contributed most to the model’s performance. Correlation analysis revealed low redundancy among these features, with most pairwise Pearson correlation coefficients below 0.5 (Figure 3b), indicating they capture distinct aspects of the input data. SHAP value analysis further confirmed the importance and directionality of these features (Figure 3c). For example, higher values of ACC_PSSM_F3215 and ACC_PSSM_F2649 were positively associated with model output, suggesting their strong influence in identifying tolerant genes.

Figure 3

Figure 3. (a) Top 20 most important ACC_PSSM features based on gain. (b) Pairwise pearson correlation of top 20 ACC_PSSM features ranked by importance. (c) SHAP value summary for top ACC_PSSM features in SaGP.

To further explore their biological relevance, we investigated the potential functional significance of key features. ACC-PSSM_3215 This feature represents the autocovariance of proline residues at a lag of 9 within the PSSM (Position-Specific Scoring Matrix), capturing the evolutionary correlation between prolines separated by nine amino acid positions in the sequence. Proline is a well-established osmoprotectant in plants under salt stress, known for enhancing osmotic adjustment, stabilizing proteins and membrane structures, and mitigating oxidative damage through reactive oxygen species (ROS) scavenging. SHAP analysis revealed a positive association between higher values of this feature and the likelihood of a sequence being classified as a positive (salt-tolerant) sample. Notably, this feature exhibited significantly elevated values in salt-tolerant sequences, suggesting an enrichment of long-range proline interactions potentially involved in the formation of adaptive structural motifs or regulatory elements. These findings indicate that the model effectively captured biologically meaningful signals associated with proline-mediated stress adaptation. Importantly, despite the absence of explicit structural domain annotations, the model implicitly leveraged functional characteristics embedded within the primary sequence. The biological relevance of this proline-related feature thus provides strong support for both the predictive consistency of the positive samples and the interpretability of the model.

2.3 Web services of SaGP

To maximize the accessibility of the SaGP and minimize the difficulty of its use, we implemented it as a highly automated webserver (https://www.sagprediction.com/) with JavaScript, Nodejs, Tailwind CSS (responsive design), HTML5, Docker, and Nginx. The only input from the users is the protein sequences encoded by their interested genes. SaGP will automatically process the sequences and return its predictions in a formatted table. Users are allowed to download the predicted results for future use.

3 Discussion

Deciphering gene functions has long been the central topic in biology and bioinformatics. With the advancement of high-throughput sequencing technologies, the massive accumulation of new sequences in public databases has far exceeded the capacity of traditional wet experiments. This has led to the development of computational methods and tools to accelerate the process of gene function identification, providing guidance for wet lab experiments and reducing the costs and time associated with wet experiments. One such method is homolog-based or domain-based (e.g., BLAST), involving comparing the genomic sequences of different organisms to infer gene functions based on their similarity to known genes. Another method is machine learning to predict the functions of unknown genes based on their sequence features. Several studies have compared the performance of both methods in identifying proteins with targeted functions, such as pathogenic proteins (Li et al., 2022) and antifreeze proteins (Eslami et al., 2018; Kandaswamy et al., 2011). Overall, these studies suggest that machine learning-based methods are superior to homolog/domain-based methods regarding speed and accuracy. Consistently, in this study, we found that the performances of SaGP were higher than homolog/domain-based methods.

One possible explanation for the incapacity of homolog/domain-based methods to identify salt-alkali tolerance genes may be caused by the fast protein evolution. In plants, the main mechanisms of salt-alkali tolerance involve ions transport (e.g., Na⁺ and Ca²⁺) and detoxification (Deinlein et al., 2014; Zhang et al., 2022). Proteins with these biochemical and cellular functions tend to evolve more rapidly, resulting in low sequence similarities among homologous proteins (Devos and Valencia, 2000; Qiao et al., 2024; Xie et al., 2024) which disadvantages homolog-based and domain-based methods. Moreover, the functional space of genes/proteins is more complex than the sequence space, making it even more challenging to identify genes with specific functions based solely on sequence similarity (Devos and Valencia, 2000). SaGP, on the other hand, has the potential to overcome this challenge by capturing complex relations hidden in the sequence data based on machine learning algorithms and key protein features. Indeed, among all features, we found that ACC-PSSM performed best followed by PDT-Profile. Both ACC-PSSM and PDT-Profile capture evolutionary information (Dong et al., 2009; Liu et al., 2012). In addition, they also include sequence order effects (Dong et al., 2009; Liu et al., 2012), which may include information about local interactions/structures that are important for ion binding and transporting. Combining both groups of features barely improved model performances, suggesting that redundant information exists between them. Nevertheless, these results suggest evolution and sequence order are two crucial components for building machine learning tools to distinguish salt-alkali tolerance and non-tolerance genes in plants.

It is important to note that SaGP was trained with negative samples from Arabidopsis thaliana. It may have low performance to identify salt-alkali non-tolerance genes in species phylogenetically far distant from Arabidopsis thaliana. To evaluate the model’s generalization capability across different species, we selected three latest published genes for validation: GhAG2 (cotton) (Yu et al., 2022), MdBPR6 (apple) (Zhang et al., 2023), and TaCCD1 (wheat) (Cui et al., 2023). The prediction results of SaGP were consistent with the experimental results, indicating the effectiveness of SaGP in predicting salt-alkali resistant genes across different species. The significant advancements in sequencing technologies allows us to access extensive genetic data from a variety of plants more quickly and at a lower cost. However, due to the long growth cycles and high costs, the stress tolerance genes of many plants are not well studied. The application of SaGP provides superior guidance compared to BLAST for the rapid and accurate identification of salt-alkali tolerance genes in genomic data of these plants. Additionally, to efficiently identify salt-alkali resistant genes, we developed a user-friendly and freely accessible web service platform based on SaGP. This platform allows users to obtain prediction results only by inputting protein sequences, without the need for downloading models, installing software, or deploying any environment. In summary, SaGP offers a reliable identification tool for mining novel salt-alkali tolerant genes based on large-scale data, it also can serve as a fundamental model for the development of additional automated tools, which can greatly facilitate studies in plant genetics (Li et al., 2024; Zafar et al., 2024) and crop breeding (Kumar et al., 2022; Sun et al., 2023), and promoting global agricultural sustainability (Sun et al., 2023). As the availability of genomic data continues to grow, the expansion of the training dataset will further enhance predictive capabilities of SaGP.

4 Materials and methods

4.1 Data collection and processing

4.1.1 Positive samples

Saline-alkali tolerance genes were manually curated from published literature. A total of 537 experimentally validated genes from 308 gene families were collected. To reduce potential confounding factors, transcription factors were removed. Additionally, we filtered out sequences containing irregular characters (e.g., “X”) and sequences shorter than 78 amino acids—the minimum observed length among positive samples. To minimize sequence redundancy, we applied CD-HIT with a sequence identity threshold of 90%, resulting in 262 high-confidence non-redundant tolerance-related protein sequences.

4.1.2 Negative samples

Negative samples were collected from the Arabidopsis thaliana genome, specifically from the TAIR database (Berardini et al., 2015), after excluding any gene families known to be associated with saline-alkali tolerance. Transcription factors and low-quality sequences (containing non-standard residues or shorter than 78 amino acids) were also removed. CD-HIT (Li et al., 2001) was used to eliminate redundant sequences at a 90% identity threshold, yielding 17,753 non-tolerance protein sequences.

To further ensure the reliability of the negative dataset, we reanalyzed RNA-seq data from Anderson et al. (2018) (Anderson et al., 2018) (GEO accession: GSE116332), which profiled gene expression in Arabidopsis thaliana under both control and salt stress conditions. Gene expression levels were quantified using StringTie, and differential expression analysis was conducted using DESeq2. Notably, none of the negative genes exhibited significant differential expression between salt-treated and control conditions, confirming their non-responsiveness to salt stress at the transcriptomic level.

4.2 Feature extraction and selection

Engineering protein features to capture the underlying patterns of salt-alkali tolerance and non-tolerance genes is crucial to constructing accurate SaGP models (Figure 4). Here, we used three programs to extract protein features, i.e., Pse-in-one2.0 (Liu et al., 2015), ftrCOOL (Amerifar et al., 2022), and MathFeature (Bonidia et al., 2022). Overall, 40 groups of protein features were extracted, representing important information about protein evolution, physicochemical properties, global and local sequence patterns, and residue interactions (Table 3). To reduce computational complexity and feature redundancy, features with zero variance or highly correlated with other features (absolute Pearson correlation coefficients > 0.8) were removed. The sequences data was split into training, validation, and independent test datasets with a ratio of 80:10:10. A univariate feature selection algorithm based on t-test and the training dataset was then used to select the set of features to construct machine learning models (Figure 4). In total, 5377 features were retained.

Figure 4

Figure 4. The framework of SaGP. In brief, protein sequences were used to construct SaGP. Both positive and negative sequences were validated with literature and RNA-seq data, respectively. Sequences were filtered to remove errors and redundancy. Protein features were extracted, selected, and used to train the machine learning models. The model with the best performances were evaluated based on the test dataset and were used to identify novel salt-alkali tolerance genes in plants.

Table 3

Table 3. The 40 groups of protein features extracted in our study, their abbreviations, and corresponding tools.

4.3 SaGP construction and evaluation

Overall, the ratio between saline-alkali tolerance and non-tolerance sequences was 1:68, which leads to an imbalanced learning problem. To address this issue and to construct SaGP with potentially optimal performance, we tested the performances of cost-sensitive methods to tackle the imbalanced learning problem (Tanha et al., 2020) (Boldini et al., 2022). compared the potentials of five cost-sensitive methods, weighted cross-entropy (WCE), Focal loss (FL), Logit-adjusted loss (LaL), Label-distribution-aware margin loss (LdaML), and Equalization loss (EL), to improve imbalanced classification in drug discovery. Here, we followed their scheme to train and evaluate our models that is Lightgbm (Ke et al., 2017) was used to train the models based on the training dataset; each model was optimized using Hyperopt (Boldini et al., 2022) based on the validation dataset; their performances were evaluated based on the independent test dataset. To assess the relative importance of different group features for identifying saline-alkali tolerance and non-tolerance genes, we evaluated each group’s features separately.

To comprehensively evaluate model performances, six metrics were calculated, i.e., Accuracy, Balanced Accuracy, F1 score, the area under the receiver operating characteristic curve (ROC-AUC), the area under the precision-recall (PR-AUC) curve and Matthew’s Correlation Coefficient (MCC). Several studies have compared the performances of these metrics for imbalanced binary classification, and in general, MCC was recommended (Chicco and Jurman, 2020, 2023). We, therefore, used MCC as the main reference to select the optimal model for SaGP.

To further confirm the power of SaGP to uncover novel saline-alkali tolerance genes, we collected three more genes from the latest publications, i.e., GhAG2 (Yu et al., 2022), MdBPR6 (Zhang et al., 2023), and TaCCD1 (Cui et al., 2023), as additional tests. In addition, we assessed the performance of BLAST to identify salt-alkali tolerance genes. In brief, all salt-alkali tolerance genes in the training and validation datasets were used to construct the search database. Sequences from the test dataset were used as the query sequence of BLAST. E value 0.01 was used to indicate a significant similarity (hit).

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 below: https://figshare.com/, https://figshare.com/account/home#/data.

Author contributions

BQ: Visualization, Writing – original draft. WG: Writing – original draft, Visualization. XZ: Writing – original draft, Data curation. MD: Data curation, Writing – original draft. SW: Visualization, Writing – original draft.. XL: Writing – review & editing, Data curation. SP: Writing – review & editing, Data curation. CY: Writing – review & editing. JW: Writing – review & editing. YZ: Writing – review & editing, Conceptualization. LX: Conceptualization, Supervision, Funding acquisition, Writing – review & editing.

Funding

The authors declare that financial support was received for the research and/or publication of this article. This work was supported by the National Key R&D Program of China during the 14th Five-Year Plan Period (Grant No. 2021YFD2200103), and the National Natural Science Foundation of China (Grant Nos. 62272094 and 62471123).

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 Generative 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/fpls.2025.1629794/full#supplementary-material.

Supplementary Figure 1 | The performances of five cost-sensitive methods and 40 groups of protein features based on the test dataset. Abbreviations: EL, Equalization loss; FL, Focal loss; LaL: Logit-adjusted loss; LdaML, Label-distribution-aware margin loss; WCE, weighted cross-entropy; MCC, Matthew’s Correlation Coefficient; ROC-AUC, the area under the receiver operating characteristic curve. See Table 3 for the details of 40 groups of protein features.

References

Altschul, S. F., Gish, W., Miller, W., Myers, E. W., and Lipman, D. J. (1990). Basic local alignment search tool. J. Mol. Biol. 215, 403–410. doi: 10.1016/s0022-2836(05)80360-2

PubMed Abstract | Crossref Full Text | Google Scholar

Amerifar, S., Norouzi, M., and Ghandi, M. (2022). A tool for feature extraction from biological sequences. Brief Bioinform. 23, bbac108. doi: 10.1093/bib/bbac108

PubMed Abstract | Crossref Full Text | Google Scholar

Anderson, S. J., Kramer, M. C., Gosai, S. J., Yu, X., Vandivier, L. E., Nelson, A. D. L., et al. (2018). N(6)-methyladenosine inhibits local ribonucleolytic cleavage to stabilize mRNAs in arabidopsis. Cell Rep. 25, 1146–1157.e3. doi: 10.1016/j.celrep.2018.10.020

PubMed Abstract | Crossref Full Text | Google Scholar

Berardini, T. Z., Reiser, L., Li, D., Mezheritsky, Y., Muller, R., Strait, E., et al. (2015). The Arabidopsis information resource: Making and mining the "gold standard" annotated reference plant genome. Genesis 53, 474–485. doi: 10.1002/dvg.22877

PubMed Abstract | Crossref Full Text | Google Scholar

Boldini, D., Friedrich, L., Kuhn, D., and Sieber, S. A. (2022). Tuning gradient boosting for imbalanced bioassay modelling with custom loss functions. J. Cheminform 14, 80. doi: 10.1186/s13321-022-00657-w

PubMed Abstract | Crossref Full Text | Google Scholar

Bonidia, R. P., Domingues, D. S., Sanches, D. S., and de Carvalho, A. (2022). MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors. Brief Bioinform. 23, bbab434. doi: 10.1093/bib/bbab434

PubMed Abstract | Crossref Full Text | Google Scholar

Briggs, S. and Taws, N. (2003). Impacts of salinity on biodiversity—clear understanding or muddy confusion? Aust. J. Bot. 51, 609–617. doi: 10.1071/BT02114

Crossref Full Text | Google Scholar

Chen, Y., Jiang, Z., Fan, P., Ericson, P. G. P., Song, G., Luo, X., et al. (2022). The combination of genomic offset and niche modelling provides insights into climate change-driven vulnerability. Nat. Commun. 13, 4821. doi: 10.1038/s41467-022-32546-z

PubMed Abstract | Crossref Full Text | Google Scholar

Chicco, D. and Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21, 6. doi: 10.1186/s12864-019-6413-7

PubMed Abstract | Crossref Full Text | Google Scholar

Chicco, D. and Jurman, G. (2023). The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification. BioData Min 16, 4. doi: 10.1186/s13040-023-00322-4

PubMed Abstract | Crossref Full Text | Google Scholar

Cui, M., Li, Y., Li, J., Yin, F., Chen, X., Qin, L., et al. (2023). Ca(2+)-dependent TaCCD1 cooperates with TaSAUR215 to enhance plasma membrane H(+)-ATPase activity and alkali stress tolerance by inhibiting PP2C-mediated dephosphorylation of TaHA2 in wheat. Mol. Plant 16, 571–587. doi: 10.1016/j.molp.2023.01.010

PubMed Abstract | Crossref Full Text | Google Scholar

Dai, X., Xu, Z., Liang, Z., Tu, X., Zhong, S., Schnable, J. C., et al. (2020). Non-homology-based prediction of gene functions in maize (Zea mays ssp. mays). Plant Genome 13, e20015. doi: 10.1002/tpg2.20015

PubMed Abstract | Crossref Full Text | Google Scholar

Deinlein, U., Stephan, A. B., Horie, T., Luo, W., Xu, G., and Schroeder, J. I. (2014). Plant salt-tolerance mechanisms. Trends Plant Sci. 19, 371–379. doi: 10.1016/j.tplants.2014.02.001

PubMed Abstract | Crossref Full Text | Google Scholar

Devos, D. and Valencia, A. (2000). Practical limits of function prediction. Proteins 41, 98–107. doi: 10.1002/1097-0134(20001001)41:1<98::AID-PROT120>3.0.CO;2-S

Crossref Full Text | Google Scholar

Ding, K., Sun, S., Luo, Y., Long, C., Zhai, J., Zhai, Y., et al. (2023). PlantCADB: A comprehensive plant chromatin accessibility database. Genomics Proteomics Bioinf. 21, 311–323. doi: 10.1016/j.gpb.2022.10.005

PubMed Abstract | Crossref Full Text | Google Scholar

Dong, Q., Zhou, S., and Guan, J. (2009). A new taxonomy-based protein fold recognition approach based on autocross-covariance transformation. Bioinformatics 25, 2655–2662. doi: 10.1093/bioinformatics/btp500

PubMed Abstract | Crossref Full Text | Google Scholar

Dou, L., Yang, F., Xu, L., and Zou, Q. (2021). A comprehensive review of the imbalance classification of protein post-translational modifications. Brief Bioinform. 22, bbab089. doi: 10.1093/bib/bbab089

PubMed Abstract | Crossref Full Text | Google Scholar

Eslami, M., Shirali Hossein Zade, R., Takalloo, Z., Mahdevar, G., Emamjomeh, A., Sajedi, R. H., et al. (2018). afpCOOL: A tool for antifreeze protein prediction. Heliyon 4, e00705. doi: 10.1016/j.heliyon.2018.e00705

PubMed Abstract | Crossref Full Text | Google Scholar

Fu, T., Zang, Y., Huang, Y., Du, Z., Huang, H., Hu, C., et al. (2023). Photonic machine learning with on-chip diffractive optics. Nat. Commun. 14, 70. doi: 10.1038/s41467-022-35772-7

PubMed Abstract | Crossref Full Text | Google Scholar

Gougherty, A. V., Keller, S. R., and Fitzpatrick, M. C. (2021). Maladaptation, migration and extirpation fuel climate change risk in a forest tree species. Nat. Climate Change 11, 166–171. doi: 10.1038/s41558-020-00968-6

Crossref Full Text | Google Scholar

Gupta, P. K. (2021). Quantitative genetics: pan-genomes, SVs, and k-mers for GWAS. Trends Genet. 37, 868–871. doi: 10.1016/j.tig.2021.05.006

PubMed Abstract | Crossref Full Text | Google Scholar

James, R. A., Blake, C., Zwart, A. B., Hare, R. A., Rathjen, A. J., and Munns, R. (2012). Impact of ancestral wheat sodium exclusion genes Nax1 and Nax2 on grain yield of durum wheat on saline soils. Funct. Plant Biol. 39, 609–618. doi: 10.1071/fp12121

PubMed Abstract | Crossref Full Text | Google Scholar

Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons 61, 577–586. doi: 10.1016/j.bushor.2018.03.007

Crossref Full Text | Google Scholar

Kandaswamy, K. K., Chou, K. C., Martinetz, T., Möller, S., Suganthan, P. N., Sridharan, S., et al. (2011). AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. J. Theor. Biol. 270, 56–62. doi: 10.1016/j.jtbi.2010.10.037

PubMed Abstract | Crossref Full Text | Google Scholar

Kang, D. Y., Cheon, K. S., Oh, J., Oh, H., Kim, S. L., Kim, N., et al. (2019). Rice genome resequencing reveals a major quantitative trait locus for resistance to bakanae disease caused by fusarium fujikuroi. Int. J. Mol. Sci. 20, 2598. doi: 10.3390/ijms20102598

PubMed Abstract | Crossref Full Text | Google Scholar

Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., et al. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst. 30, 3146–3154.

Google Scholar

Kumar, P., Choudhary, M., Halder, T., Prakash, N. R., Singh, V., V, V. T., et al. (2022). Salinity stress tolerance and omics approaches: revisiting the progress and achievements in major cereal crops. Heredity (Edinb) 128, 497–518. doi: 10.1038/s41437-022-00516-2

PubMed Abstract | Crossref Full Text | Google Scholar

Li, W., Jaroszewski, L., and Godzik, A. (2001). Clustering of highly homologous sequences to reduce the size of large protein databases. Bioinformatics 17, 282–283. doi: 10.1093/bioinformatics/17.3.282

PubMed Abstract | Crossref Full Text | Google Scholar

Li, F., Guo, X., Xiang, D., Pitt, M. E., Bainomugisa, A., and Coin, L. J. M. (2022). Computational analysis and prediction of PE_PGRS proteins using machine learning. Comput. Struct. Biotechnol. J. 20, 662–674. doi: 10.1016/j.csbj.2022.01.019

PubMed Abstract | Crossref Full Text | Google Scholar

Li, N., Shao, T., Xu, L., Long, X., Rengel, Z., and Zhang, Y. (2024). Transcriptome analysis reveals the molecular mechanisms underlying the enhancement of salt-tolerance in Melia azedarach under salinity stress. Sci. Rep. 14, 10981. doi: 10.1038/s41598-024-61907-5

PubMed Abstract | Crossref Full Text | Google Scholar

Liu, B., Liu, F., Wang, X., Chen, J., Fang, L., and Chou, K. C. (2015). Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res. 43, W65–W71. doi: 10.1093/nar/gkv458

PubMed Abstract | Crossref Full Text | Google Scholar

Liu, B., Wang, X., Chen, Q., Dong, Q., and Lan, X. (2012). Using amino acid physicochemical distance transformation for fast protein remote homology detection. PloS One 7, e46633. doi: 10.1371/journal.pone.0046633

PubMed Abstract | Crossref Full Text | Google Scholar

Mackay, T. F., Stone, E. A., and Ayroles, J. F. (2009). The genetics of quantitative traits: challenges and prospects. Nat. Rev. Genet. 10, 565–577. doi: 10.1038/nrg2612

PubMed Abstract | Crossref Full Text | Google Scholar

Meuwissen, T. H., Hayes, B. J., and Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829. doi: 10.1093/genetics/157.4.1819

PubMed Abstract | Crossref Full Text | Google Scholar

Meyer, R. S., DuVal, A. E., and Jensen, H. R. (2012). Patterns and processes in crop domestication: an historical review and quantitative analysis of 203 global food crops. New Phytol. 196, 29–48. doi: 10.1111/j.1469-8137.2012.04253.x

PubMed Abstract | Crossref Full Text | Google Scholar

Pal, T., Jaiswal, V., and Chauhan, R. S. (2016). DRPPP: A machine learning based tool for prediction of disease resistance proteins in plants. Comput. Biol. Med. 78, 42–48. doi: 10.1016/j.compbiomed.2016.09.008

PubMed Abstract | Crossref Full Text | Google Scholar

Qiao, B., Wang, S., Hou, M., Chen, H., Zhou, Z., Xie, X., et al. (2024). Identifying nucleotide-binding leucine-rich repeat receptor and pathogen effector pairing using transfer-learning and bilinear attention network. Bioinformatics 40, btae581. doi: 10.1093/bioinformatics/btae581

PubMed Abstract | Crossref Full Text | Google Scholar

Sang, Y., Long, Z., Dan, X., Feng, J., Shi, T., Jia, C., et al. (2022). Genomic insights into local adaptation and future climate-induced vulnerability of a keystone forest tree in East Asia. Nat. Commun. 13, 6541. doi: 10.1038/s41467-022-34206-8

PubMed Abstract | Crossref Full Text | Google Scholar

Song, Q., Lee, J., Akter, S., Rogers, M., Grene, R., and Li, S. (2020). Prediction of condition-specific regulatory genes using machine learning. Nucleic Acids Res. 48, e62. doi: 10.1093/nar/gkaa264

PubMed Abstract | Crossref Full Text | Google Scholar

Sun, W., Zhang, H., Yang, S., Liu, L., Xie, P., Li, J., et al. (2023). Genetic modification of Gγ subunit AT1 enhances salt-alkali tolerance in main graminaceous crops. Natl. Sci. Rev. 10, nwad075. doi: 10.1093/nsr/nwad075

PubMed Abstract | Crossref Full Text | Google Scholar

Tanha, J., Abdi, Y., Samadi, N., Razzaghi, N., and Asadpour, M. (2020). Boosting methods for multi-class imbalanced data classification: an experimental review. J. Big Data 7, 70. doi: 10.1186/s40537-020-00349-y

Crossref Full Text | Google Scholar

Van Dijk, A. D. J., Kootstra, G., Kruijer, W., and de Ridder, D. (2021). Machine learning in plant science and plant breeding. iScience 24, 101890. doi: 10.1016/j.isci.2020.101890

PubMed Abstract | Crossref Full Text | Google Scholar

Wallace, J. G., Rodgers-Melnick, E., and Buckler, E. S. (2018). On the road to breeding 4.0: unraveling the good, the bad, and the boring of crop quantitative genomics. Annu. Rev. Genet. 52, 421–444. doi: 10.1146/annurev-genet-120116-024846

PubMed Abstract | Crossref Full Text | Google Scholar

Wang, Y., Dai, X., Fu, D., Li, P., and Du, B. (2022). PGD: a machine learning-based photosynthetic-related gene detection approach. BMC Bioinf. 23, 183. doi: 10.1186/s12859-022-04722-x

PubMed Abstract | Crossref Full Text | Google Scholar

Wang, X., Wang, H., Liu, S., Ferjani, A., Li, J., Yan, J., et al. (2016). Genetic variation in ZmVPP1 contributes to drought tolerance in maize seedlings. Nat. Genet. 48, 1233–1241. doi: 10.1038/ng.3636

PubMed Abstract | Crossref Full Text | Google Scholar

Wray, N. R., Yang, J., Hayes, B. J., Price, A. L., Goddard, M. E., and Visscher, P. M. (2013). Pitfalls of predicting complex traits from SNPs. Nat. Rev. Genet. 14, 507–515. doi: 10.1038/nrg3457

PubMed Abstract | Crossref Full Text | Google Scholar

Xie, X., Gui, L., Qiao, B., Wang, G., Huang, S., Zhao, Y., et al. (2024). Deep learning in template-free de novo biosynthetic pathway design of natural products. Brief Bioinform. 25, bbae495. doi: 10.1093/bib/bbae495

PubMed Abstract | Crossref Full Text | Google Scholar

Yan, J. and Wang, X. (2023). Machine learning bridges omics sciences and plant breeding. Trends Plant Sci. 28, 199–210. doi: 10.1016/j.tplants.2022.08.018

PubMed Abstract | Crossref Full Text | Google Scholar

Yang, W., Duan, L., Chen, G., Xiong, L., and Liu, Q. (2013). Plant phenomics and high-throughput phenotyping: accelerating rice functional genomics using multidisciplinary technologies. Curr. Opin. Plant Biol. 16, 180–187. doi: 10.1016/j.pbi.2013.03.005

PubMed Abstract | Crossref Full Text | Google Scholar

Yu, W., Xue, Z., Zhao, X., Zhang, R., Liu, J., and Guo, S. (2022). Glyphosate-induced GhAG2 is involved in resistance to salt stress in cotton. Plant Cell Rep. 41, 1131–1145. doi: 10.1007/s00299-022-02844-3

PubMed Abstract | Crossref Full Text | Google Scholar

Zafar, M. M., Razzaq, A., Chattha, W. S., Ali, A., Parvaiz, A., Amin, J., et al. (2024). Investigation of salt tolerance in cotton germplasm by analyzing agro-physiological traits and ERF genes expression. Sci. Rep. 14, 11809. doi: 10.1038/s41598-024-60778-0

PubMed Abstract | Crossref Full Text | Google Scholar

Zhang, X., Gong, X., Yu, H., Su, X., Cheng, S., Huang, J., et al. (2023). The proline-rich protein MdPRP6 confers tolerance to salt stress in transgenic apple (Malus domestica). Scientia Hortic. 308, 111581. doi: 10.1016/j.scienta.2022.111581

Crossref Full Text | Google Scholar

Zhang, H., Zhu, J., Gong, Z., and Zhu, J. K. (2022). Abiotic stress responses in plants. Nat. Rev. Genet. 23, 104–119. doi: 10.1038/s41576-021-00413-0

PubMed Abstract | Crossref Full Text | Google Scholar

Zhao, Y., Gui, L., Hou, C., Zhang, D., and Sun, S. (2024). GwasWA: A GWAS one-stop analysis platform from WGS data to variant effect assessment. Comput. Biol. Med. 169, 107820. doi: 10.1016/j.compbiomed.2023.107820

PubMed Abstract | Crossref Full Text | Google Scholar