| Deep learning extends variant, regulatory and chromatin interaction predictionDeep Learning Genomic Regulation Models | Systems biology | |
| GWAS associations are integrated into genomic and phenotype prediction frameworksGwas Informed Genotype Prediction | Metabolomics | |
| New computational methods for single-cell, spatial, and imaging data analysisSingle Cell Computational Methods | Single-cell | |
| Benchmark baselines and transfer conditions reshape reported gains in biological MLBenchmark Validity In Biological Ml | ML / AI methods | |
| Benchmarking computational omics methods against defined references and scenariosComputational Method Benchmarking | Proteomics | |
| Computational models resolve context-specific RNA regulation and technical artifactsComputational Rna Regulation Methods | Epigenomics | |
| Protein language models applied to stability, cysteine states, and genome annotationProtein Language Model Applications | Tools & infrastructure | |
| Statistical methods link non-coding variants, methylation, and gene-environment effectsStatistical Methods In Omics | Structural bio & protein design | |
| Ensemble and free-energy methods now resolve RNA, protein and ligand binding physicsBiomolecular Ensemble Modeling Methods | Structural bio & protein design | |
| Protein and nucleic acid design tools benchmarked for fitness and specificityComputational Sequence Design Models | Genomics | |
| Simulation and graph learning map conformational routes in proteins and complexesComputational Molecular Systems Modeling | Structural bio & protein design | |
| Coarse-grained and deep-learning tools model protein flexibility and interfacesProtein Structure Simulation Methods | Structural bio & protein design | |
| Benchmarks test ML models for single-cell perturbation and cancer predictionBenchmarking Machine Learning In Biology | Genomics | |
| Augmenting protein language models with structure, dynamics, and graph-based inputsProtein Language Model Prediction | ML / AI methods | |
| Learned models stratify disease risk from blood, imaging, and omics dataMl For Biomedical Data Stratification | ML / AI methods | |
| Learned representations recover structure in omics and pathology data across platformsRepresentation Learning For Omics | ML / AI methods | |
| Protein language models and generative models predict and design biomolecular propertiesLearned Protein Property Prediction | ML / AI methods | |
| Host-linked genomic structure across coral, plant, protist, and viral systemsHost Associated Genomic Diversity | Genomics | |
| Batch correction, imputation and splicing benchmarks advance single-cell omics analysisSingle Cell Transcriptomic Analysis Methods | Tools & infrastructure | |
| Open computational tools for reproducible omics and chromatin sequencing analysisComputational Tools For Omics Visualization | ML / AI methods | |
| Statistical methods for omic data: mediation, network inference, and variable selectionStatistical Models For Omics | Genomics | |
| Models recover cell-state transitions from snapshot single-cell and spatial profilesSingle Cell Dynamics Modeling | Single-cell | |
| Statistical methods integrate chromatin and perturbation data at single-cell resolutionSingle Cell Regulatory Genomics Methods | Epigenomics | |
| Learned models now predict binding, design and specificity from structure and sequenceMachine Learning Protein Prediction | Structural bio & protein design | |
| Computational tools for cell annotation, marker discovery and differential expressionSingle Cell Omics Computational Methods | Tools & infrastructure | |
| Updated tools for microbial pangenomics, plasmid recovery and phylogenetic inferenceMicrobial Genomics Tools And Databases | Systems biology | |
| Graph neural networks model molecular interactions, drug synergy and disease driversGraph Learning For Molecular Prediction | Systems biology | |
| Structure-derived objectives guiding deep models for binder and compound prioritizationStructure Guided Deep Learning | Systems biology | |
| Dynamical models separate measurement noise from growth and metabolic variabilityDynamical Modeling Under Noise | ML / AI methods | |
| Computational frameworks now score missense, splicing and promoter variant effectsVariant Effect Prediction Frameworks | Genomics | |
| Spatial omics resolves niche communication, recruitment, and multiscale tissue topologySpatial Single Cell Profiling | Transcriptomics | |
| Computational frameworks couple perturbation screens to single-cell multimodal readoutsPerturbation Single Cell Omics Tools | Single-cell | |
| Long-read simulators and pipelines are benchmarked against real sequencing dataSequencing Analysis Tool Benchmarking | Transcriptomics | |
| Interactive software tools for genomic, proteomic, and multi-omics data explorationInteractive Omics Visualization Tools | Systems biology | |
| Neural and graph models recover finer spatial gene expression and tissue domainsSpatial Transcriptomics Computational Methods | ML / AI methods | |
| Heterogeneous-model and raw-read phylogenetics tools advance evolutionary inferencePhylogenomic Inference Tools | ML / AI methods | |
| Standardized benchmarks probe generalization of ML models in perturbation biologyBenchmarking Ml Biomolecular Prediction | Structural bio & protein design | |
| Graph-based models of cell-cell interactions and gene regulation in single-cell dataSingle Cell Spatial Transcriptomics Methods | Tools & infrastructure | |
| Learned embeddings transfer across protein, mRNA, promoter and genomic sequence tasksLearned Biological Sequence Representations | ML / AI methods | |
| Learned models predict variant effects, regulatory activity and peptide-TCR specificityMachine Learning For Biomolecular Prediction | ML / AI methods | |
| LLM agents and structured models are assessed for biomedical reproducibilityAi Agents For Biomedical Research | ML / AI methods | |
| Deep models and epigenome annotation infer regulatory elements from chromatin dataComputational Models Of Regulatory Chromatin | Tools & infrastructure | |
| ML frameworks infer cell, tissue, and repertoire states from omics and imaging dataMachine Learning Omics Frameworks | ML / AI methods | |
| Sequence models link viral sequence features to host origin and immune escapeViral Genomic Sequence Modeling | ML / AI methods | |
| Learned models link histology and spatial transcriptomics to predict expression and nichesLearned Representations For Spatial Transcriptomics | Tools & infrastructure | |
| Interpretability Tools Recover Known Visual Selectivity in Neural ModelsInterpretable Neural Encoding Models | ML / AI methods | |
| LLM agents generate and code-verify biological analysis methods and discoveriesVerified Llm Research Agents | Tools & infrastructure | |
| Learned models couple generation and verification for small molecules and peptidesLearned Models For Molecular Prediction | ML / AI methods | |
| Generative models now design biomolecules under explicit multi-objective constraintsGenerative Biomolecular Design Models | Structural bio & protein design | |
| Protein language models predict immune recognition across TCR, pMHC and B-cell epitopesMachine Learning Immune Recognition | ML / AI methods | |
| Conformational ensembles guiding ligand and allosteric site selection for drug designMolecular Dynamics Conformational Landscapes | Structural bio & protein design | |
| Learned models recover structure lost to sparsity and modality gaps in transcriptomicsLearned Spatial Transcriptomics Methods | Single-cell | |
| Genome-scale and hybrid models reconstruct microbial metabolism and community dynamicsMetabolic Network Modeling Methods | Systems biology | |
| Biology-informed representations sharpen protein and molecular property predictionBiology Informed Protein Representation Learning | Structural bio & protein design | |
| Long-read pangenomes resolve mobile and repetitive variation in human genomesHuman Pangenome Variant Discovery | Genomics | |
| Mechanistic models link reproduction numbers, contact structure and heterogeneityMechanistic Inference For Epidemic Dynamics | Structural bio & protein design | |
| Learned models infer tumor-cell and spatial expression from histology, MRI, bulk assaysLearned Multimodal Prediction In Cancer | ML / AI methods | |
| Shared embeddings fuse tissue images, spatial transcriptomes and molecular profilesMultimodal Omics Representation Learning | ML / AI methods | |
| New algorithms advance viral alignment, RNA structure, protein comparison, and k-mer setsAlignment And Comparison Algorithms | Tools & infrastructure | |
| Benchmarks now test single-cell and sequencing analysis tools head-to-headBenchmarked Sequencing Analysis Pipelines | Structural bio & protein design | |
| Chromosome-scale and haplotype-phased genome assemblies across fungi and plantsChromosome Level Genome Assemblies | Genomics | |
| Adaptive feedback and modularity in computational models of neural and metabolic systemsHomeostatic And Adaptive Computational Models | Systems biology | |
| Benchmarks and new callers refine variant, indel, and fusion detectionGenomic Variant Detection Methods | Genomics | |
| Deep sequence models predict gene expression, splicing and variant effectsDeep Learning Genomic Sequence Models | Genomics | |
| Deep models gain experimental restraints and geometric priors for biomolecular designDeep Learning Biomolecular Design | Structural bio & protein design | |
| Benchmark choices and inductive biases shape ML drug-response and design modelsMl Models For Drug Discovery | Tools & infrastructure | |
| Bayesian models infer dengue and malaria transmission from genomic and surveillance dataBayesian Inference For Transmission Dynamics | Systems biology | |
| Genomic comparisons trace duplication, selection, and incompatibility in animalsComparative Evolutionary Genomics | Genomics | |
| GPU and memory-bounded methods speed up sequence alignment and variant callingGpu Accelerated Bioinformatics Tools | Tools & infrastructure | |
| Deep learning models for variant effect and genome annotation across speciesDeep Learning Genomic Variant Prediction | Systems biology | |
| Structural variants, polygenic axes and mtDNA regulation across genomic scalesComparative Regulatory Genomics Evolution | Genomics | |
| Comparisons of biomolecular AI models expose thermodynamic and conformational determinantsBiomolecular Ai Model Evaluation | ML / AI methods | |
| Simulation and neural surrogates recover interpretable dynamics in biological systemsSimulation Driven Biological Dynamics Modeling | Systems biology | |
| Multimodal and graph-based models link enzymes, reactions, E3 ligases, and PPIsStructure Aware Multimodal Biomolecular Learning | ML / AI methods | |
| Benchmarks expose leakage and inconsistent evaluation in biological prediction toolsBenchmarking Computational Biology Prediction Tools | ML / AI methods | |
| Physics-based and generative models resolve protein conformational dynamics and designProtein Structural Dynamics And Design | Genomics | |
| Protein language model embeddings drive sequence-based functional prediction modelsProtein Embedding Based Prediction | Structural bio & protein design | |