Bioinformatics (Oxford, England)最新文献

{"title":"TSEDTA: A Transformer-based neural network with SMILES Transformer and ESM2 embeddings for drug-target binding affinity prediction.","authors":"Xu Sun, Xiaoying Liu, Juanjuan Huang, Jiageng Wu, Yuchen Sun, Jiwei Jia","doi":"10.1093/bioinformatics/btag298","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag298","url":null,"abstract":"<p><strong>Motivation: </strong>Drug-target binding affinity (DTA) prediction plays a vital role in drug repositioning. The emergence of large language models (LLMs) has introduced new perspectives for predicting DTA. Herein, we present TSEDTA, a Transformer-based neural network with SMILES Transformer and ESM2 embeddings for predicting DTA. It leverages pre-trained LLMs (SMILES Transformer and ESM2) to extract deep evolutionary representations from drug SMILES and protein sequences. The representations are directly fused with raw sequence embeddings and processed via dual Transformer encoders to capture complex local and global dependencies.</p><p><strong>Results: </strong>The experiments demonstrate that TSEDTA outperforms ten advanced models on the Davis and KIBA datasets, and seven on the BindingDB dataset. Ablation studies show that incorporating LLM embeddings significantly improves the performance of TSEDTA. Furthermore, a practical case study demonstrates its real-world applicability. Ultimately, TSEDTA provides a highly accurate, robust tool for DTA prediction, offering new insights into the application of LLMs for DTA tasks.</p><p><strong>Availability: </strong>The source code and data are available at: https://github.com/SunXu24Math/TSEDTA. The version of record is archived in Zenodo with the DOI: 10.5281/zenodo.19103249.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147864807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IDAP: An integrated literature- and knowledge-graph-driven evidence prioritization pipeline for precision oncology.","authors":"Yebin Ryu, Haeun Jung, Joon-Yong An","doi":"10.1093/bioinformatics/btag300","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag300","url":null,"abstract":"<p><strong>Motivation: </strong>Advances in tumor sequencing enable routine detection of dozens to hundreds of somatic alterations per patient, yet only a minority can be linked to established therapeutic evidence. Curated resources such as OncoKB provide high-quality variant-drug annotations but remain limited in coverage, particularly for rare or low-frequency variants. This coverage gap motivates computational frameworks that can integrate curated, literature-derived, graph-based, and clinical-trial evidence to prioritize therapeutic hypotheses for expert review.</p><p><strong>Results: </strong>We developed the Integrated Drug Annotation Pipeline (IDAP), a modular framework that combines four complementary evidence streams: curated variant-drug associations from OncoKB, literature-derived gene-drug mention counts from PubMed abstracts, graph-based drug prioritization using a TxGNN-derived biomedical knowledge graph, and cancer-specific clinical-trial evidence from ClinicalTrials.gov. Given a cancer type and a MAF file, IDAP generates patient-level reports summarizing detected variants, ranked therapeutic hypotheses, supporting evidence layers, and relevant clinical trials. Evaluated across five cancer types (n = 50 samples), IDAP expanded evidence-linked therapeutic hypotheses beyond curated databases alone. Among patients without OncoKB recommendations (26/50), IDAP identified a median of 87 candidate drugs (range: 2-473). To reduce cross-source scale imbalance, the final ranking used within-sample percentile normalization with fixed bonuses for curated evidence, multi-source support, and trial linkage. Under this revised ranking, 24/50 top-ranked candidates were supported by at least two evidence sources and 44/50 had associated ClinicalTrials metadata. In an exploratory external CIViC comparison, IDAP recovered at least one matched CIViC-supported therapy in 28/41 eligible samples, with 13/41 appearing within the top 10 candidates. These outputs are intended to support evidence triage and translational interpretation rather than direct treatment recommendation.</p><p><strong>Availability and implementation: </strong>IDAP is freely available at https://github.com/joonan-lab/IDAP-pipeline, with full documentation at https://joonan-lab.github.io/IDAP-pipeline. An archived snapshot of the code used in this study is deposited on Zenodo (DOI: https://doi.org/10.5281/zenodo.19301367).</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147864599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Protein Language Model Embeddings Improve HIV Drug Resistance Prediction: A Comprehensive Benchmark with Attention-Based Interpretability.","authors":"Hayden Farquhar","doi":"10.1093/bioinformatics/btag260","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag260","url":null,"abstract":"<p><strong>Motivation: </strong>Accurate prediction of HIV drug resistance from viral sequences is critical for optimising antiretroviral therapy. Traditional machine-learning approaches using binary mutation encoding achieve strong accuracy but may fail to capture epistatic interactions and structural features relevant to resistance mechanisms. Protein language models (PLMs) offer learned representations encoding evolutionary and structural information, but have not been systematically benchmarked for HIV resistance prediction across the modern antiretroviral drug set.</p><p><strong>Results: </strong>We evaluated ESM-2 (650 M parameters) with attention-weighted pooling for predicting resistance to 18 drugs across three classes (protease inhibitors, NRTIs, NNRTIs) on the Stanford HIVDB dataset (n=6,308 sequences). Attention-weighted ESM-2 embeddings significantly outperformed XGBoost baselines with binary mutation encoding (mean AUC 0.968 vs 0.955, P=0.0017), with gains across 15 of 18 drugs and the largest improvements for drugs with complex resistance patterns. Attention weights showed 2.48-fold enrichment at known drug-resistance-mutation positions (P<0.05 for 63% of drugs; NRTIs strongest at 4.20-fold). External validation on a 20% holdout showed minimal degradation (AUC 0.934). Benchmarking against ESM C 600M and ESM-1v confirmed performance is robust to PLM choice (mean AUC 0.942-0.946 across backbones). Performance was maintained across HIV-1 subtypes (B 0.924; B-divergent 0.900; non-B 0.884) and a temporal holdout (AUC 0.930).</p><p><strong>Availability and implementation: </strong>Source code is available at https://github.com/hayden-farquhar/HIV-ESM-2 under an MIT licence and archived at https://doi.org/10.5281/zenodo.19466629. Stanford HIVDB genotype-phenotype data are publicly available at https://hivdb.stanford.edu/.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147864636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"KG-Bench: Benchmarking Graph Neural Network Algorithms for Drug Repurposing.","authors":"Siqi Wei, Christo Sasi, Jelle Piepenbrock, Martijn A Huynen, Peter A C 't Hoen","doi":"10.1093/bioinformatics/btag159","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag159","url":null,"abstract":"<p><strong>Motivation: </strong>Drug repurposing leverages existing drugs for new indications, accelerating drug development. Computational methods integrating diverse biological and chemical data can systematically prioritize repurposing candidates, but standardized benchmarks for deep learning evaluation are lacking. We present KG-Bench, a GNN benchmarking framework designed to systematically compare the performance of different graph neural network (GNN) architectures on drug-disease association prediction using the Open Targets dataset. We constructed a knowledge graph (KG) of drugs, diseases, and targets, including annotations such as therapeutic area and molecular pathway, and ensured retrospective validation by leveraging regular dataset updates. To avoid data leakage, we removed redundant entities across splits.</p><p><strong>Results: </strong>Benchmarking six GNN architectures, RGCN achieved the highest ranking performance (AUC: 0.91), while TransformerConv showed superior robustness under class imbalance (F1: 0.28 at 1:100 positive: negative ratio), characteristic of real drug repurposing datasets. KG-Bench also assesses bias, node/feature importance, and uses GNNExplainer for interpretability. Our open-source framework enables fair, reproducible evaluation of graph-based drug repurposing algorithms.</p><p><strong>Availability and implementation: </strong>Data and codes are available at https://github.com/cmbi/Benchmark_GNN_OpenTargets.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147857865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drug Response Profile-Based Machine Learning Enables Strategic Cell Line and Compound Selection for Drug Development.","authors":"Abbi Abdel-Rehim, Emma Tate, Larisa N Soldatova, Ross D King","doi":"10.1093/bioinformatics/btag293","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag293","url":null,"abstract":"<p><strong>Motivation: </strong>Early-stage drug discovery relies on testing compounds across a limited set of cell lines, making it challenging to capture biological diversity while maintaining experimental efficiency. Current predictive approaches often depend on high-dimensional omics data, which can be costly and difficult to interpret. We therefore evaluated whether drug-response panel (DRP) descriptors, which capture sensitivity profiles to a reference set of compounds, can provide an efficient and informative alternative for modelling drug response.</p><p><strong>Results: </strong>Using gradient boosting models across GDSC and CCLE datasets, DRP descriptors consistently outperformed mRNA expression features in predicting drug sensitivity (-log10(IC50)), although performance varied across compounds. Model interpretation recovered known MAPK-associated sensitivity signatures and identified potential biomarkers for MEK1/2 and BTK/MNK inhibitors. Extending this framework, we demonstrated its utility in compound prioritisation by distinguishing between tumourigenic MCF7 and non-tumourigenic MCF10A cells, successfully identifying compounds with selective activity. Together, these results show that DRP-based representations, derived from compact screening panels, support efficient cell line selection, biomarker discovery, and compound prioritisation in early-stage drug development.</p><p><strong>Availability: </strong>Code and data uploaded to https://github.com/abbiAR/-Strategic-Cell-Line-and-Compound-Selection-Using-Drug-Response-Profiles.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147857793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilization of Long-Read Sequencing for the Detection of Structural Rearrangements with AgileStructure.","authors":"Carolina Lascelles, Morag Raynor, Laura A Crinnion, Ailsa M S Rose, Christine P Diggle, James A Poulter, Christopher M Watson, Ian M Carr","doi":"10.1093/bioinformatics/btag294","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag294","url":null,"abstract":"<p><strong>Motivation: </strong>Changes in genome organisation contribute to genetic disease when they disrupt gene function or regulation. Structural rearrangements may interrupt coding sequence or alter expression through promoter loss or gain, chromatin changes, copy-number variation, or disruption of short-range regulatory elements. Although short-read sequencing excels at detecting small variants, it performs poorly at resolving breakpoints of large rearrangements, especially in repetitive or low-complexity regions. Long-read sequencing overcomes these limitations, but analytical tools have not kept pace, making accurate identification and annotation of large structural variants challenging.</p><p><strong>Results: </strong>We developed AgileStructure, a desktop application for locating and annotating large‑scale genomic rearrangements using aligned long‑read data. The software enables user‑guided exploration of breakpoint‑spanning reads, supporting accurate interpretation of complex events and filling a key gap in current structural variant analysis workflows.</p><p><strong>Availability and implementation: </strong>Source code, binaries, user guide, and example aligned read data, are available on GitHub: https://github.com/msjimc/AgileStructure. An archived version is also available on Zenodo at https://doi.org/10.5281/zenodo.18610110.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147857806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"dAMN: a genome-scale neural-mechanistic hybrid model to predict bacterial growth dynamics.","authors":"Jean-Loup Faulon, Danilo Dursoniah, Paul Ahavi, Antoine Raynal, Enrique Asin-Garcia","doi":"10.1093/bioinformatics/btag230","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag230","url":null,"abstract":"<p><strong>Summary: </strong>This study presents dAMN, a genome-scale neural-mechanistic hybrid model that combines neural networks with dynamic flux balance analysis to predict bacterial growth dynamics across diverse nutrient environments. Using a residual network architecture, dAMN predicts reaction fluxes and lag-phase parameters from initial medium composition, then integrates these predictions under stoichiometric constraints derived from genome-scale metabolic models. Trained on Escherichia coli and Pseudomonas putida growth datasets across combinatorial media, dAMN accurately forecasts temporal growth dynamics and generalizes to unseen media conditions, with mean R² ≥ 0.9. The model also reproduces biologically relevant behaviors including substrate depletion, acetate overflow, and diauxic shifts, while explicitly modeling lag phases usually absent from standard dFBA.</p><p><strong>Availability and implementation: </strong>The dAMN software, associated models, and datasets are available at https://github.com/brsynth/dAMN-main-release and via Zenodo DOI: 10.5281/zenodo.17908125.</p><p><strong>Supplementary information: </strong>Supplementary methods and data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147857783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EMTscore infers divergent EMT pathways from omics data and enables rapid screening for EMT-associated gene sets.","authors":"Haimei Wen, Leonidas Bleris, Tian Hong","doi":"10.1093/bioinformatics/btag286","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag286","url":null,"abstract":"<p><strong>Motivation: </strong>Quantitative analyses of epithelial-mesenchymal transition (EMT) have been widely used in several areas of biomedical sciences due to its importance in development and cancer progression, but its multi-contextual nature requires standardization and implementation of gene set scoring methods beyond capacities of conventional tools.</p><p><strong>Results: </strong>We developed EMTscore, a package that provides an efficient implementation of unbiased scoring methods for multiple EMT pathways using individual single-cell or bulk omics data, and the package allows rapid screening for cellular processes correlated with EMT.</p><p><strong>Availability: </strong>EMTscore is available from GitHub https://github.com/wenmm/EMTscore under the GNU General Public License, and is uploaded on Zenodo with a DOI 10.5281/zenodo.19487376.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147847508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PyFgsea: A Rust-Powered, fgseaMultilevel-Aligned GSEA Framework with Rolling-Window Enrichment along Single-Cell Trajectories.","authors":"Kuanghao Wang, Hong Shi","doi":"10.1093/bioinformatics/btag257","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag257","url":null,"abstract":"<p><strong>Summary: </strong>GSEA is a standard approach for pathway interpretation, yet Python ecosystems lack a high-performance implementation aligned with the fgseaMultilevel rare-event estimator target, especially for trajectory-aware rolling-window analysis. Under matched inputs, PyFgsea remains near-identical for normalized enrichment scores (NES; Pearson r>0.999), machine-precision identical for enrichment scores (ES), and statistically faithful for nominal p values relative to the R fgseaMultilevel reference. Its stateful rolling-window engine further reduces repeated trajectory-analysis overhead, yielding approximately 1.9-fold end-to-end wall-time speedup in a conservative stress test and, in a narrower 100-window component benchmark, up to 7.47-fold acceleration. Rolling-window significance is controlled only by within-window Benjamini-Hochberg correction across pathways rather than by trajectory-wide global error control, so these profiles are intended primarily for local trend exploration and candidate-pathway prioritization.</p><p><strong>Availability and implementation: </strong>Source code is available at https://github.com/shayuanxukuang/pyfgsea and via PyPI (pip install pyfgsea). An archival snapshot of the code and benchmark data is available on Zenodo (DOI: 10.5281/zenodo.19446446).</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147847475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Plant Phenotypic and Genotypic Data in the AGENT Project: A BrAPI Service Implementation.","authors":"Matthijs Brouwer, Jens Bauernfeind, Gouripriya Davuluri, Jorge Garcia-Brizuela, Patrick König, Suman Kumar, Matthias Lange, Stephan Weise, Erik Wijnker, Cyril Pommier, Joseph Ruff, Paul J Kersey","doi":"10.1093/bioinformatics/btag287","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag287","url":null,"abstract":"<p><strong>Motivation: </strong>The AGENT project established a network of actively cooperating European genebanks, integrating genomic and phenotypic data from accessions of wheat and barley. Due to specific storage demands for phenotypic and genotypic data, the project used separate database instances and backend technologies to manage integrated phenotypic and genotypic data.</p><p><strong>Results: </strong>We discuss the challenges encountered when integrating dispersed data to serve through a single interface such as the Plant Breeding Application Programming Interface, BrAPI. We examine how the consistent mappability of genebank data to the BrAPI model can enable the implementation of effective services. The advantages of BrAPI in transparently linking distributed data entities through embedded, unique identifiers are highlighted. We present a technical solution involving a BrAPI proxy, which combines and merges separate BrAPI endpoints. Finally, we demonstrate the AGENT BrAPI implementation with an illustrative example that validates a suggested SNP for a trait from the literature by linking phenotypic, genotypic and passport data.</p><p><strong>Availability and implementation: </strong>The BrAPI proxy implementation and documentation is available at the Python Package Index (https://pypi.org/project/brapi-proxy) and archived in Zenodo (doi : 10.5281/zenodo.19436445).</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147847460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}