September 2026

Top 40

ML / AI methods

Interpretability Tools Recover Known Visual Selectivity in Neural Models

Genomics

Comparative genomics partitions duplication, transfer and synteny across lineages

ML / AI methods

Shared embeddings fuse tissue images, spatial transcriptomes and molecular profiles

Systems biology

Biophysical models link ion channel, noise and modulation to network firing dynamics

Epigenomics

Isoform-resolved single-cell and single-molecule methods quantify transcript variants

6 Learned multimodal and graph representations for protein and RNA predictionML / AI methods

7 MD ensembles and replica-exchange sampling map folding and binding pathwaysStructural bio & protein design

8 Protein language model embeddings enable sequence-based prediction of protein traitsML / AI methods

9 MD and cofolding models probe conformational states in kinases, Aβ, and HIV-1 EnvStructural bio & protein design

10 Learned models link histology and spatial transcriptomics to predict expression and nichesTools & infrastructure

11 Spatial models couple influenza dispersal, defective particles and interferon signallingML / AI methods

12 MD simulations link conformational states to membrane binding and organizationStructural bio & protein design

13 Simulations Link RNA Modifications to Conformation and Electronic StructureStructural bio & protein design

14 Fitted mechanistic models link single-cell and patient omics to regulatory dynamicsML / AI methods

15 Regional brain organization metrics test normative models, criticality, and morphometrySystems biology

16 Models infer microbial and viral traits from large genomic and metagenomic datasetsGenomics

17 Computational models link germinal center B cell states to antibody affinity maturationGenomics

18 Benchmark baselines and transfer conditions reshape reported gains in biological MLML / AI methods

19 Ensemble and free-energy methods now resolve RNA, protein and ligand binding physicsStructural bio & protein design

20 Physical and quantum-derived references inform learned models of molecular propertiesML / AI methods

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21 Simulation methods extend to cyclic peptides, non-canonical residues and polypeptoidsStructural bio & protein design

22 LLM agents and multimodal models convert literature and spectra into molecular knowledgeTools & infrastructure

23 Reproducible tools for assessing and streamlining omics data analysis workflowsTools & infrastructure

24 Computational models link genetic variants to RNA binding and cell-type expressionTools & infrastructure

25 Context-aware models refine variant calling, eQTL mapping and polygenic predictionGenomics

26 Bifurcation structure and heterogeneity determine persistence beyond threshold indicesSystems biology

27 Graph and language-model encoders advance drug-target and synergy predictionStructural bio & protein design

28 Integrating diverse variant and annotation data to improve genetic risk predictionGenomics

29 Benchmarks and tools expose technical biases in sequencing-based genomic assaysEpigenomics

30 Spatial and single-cell models infer multicellular motifs, regulators and disease linksSingle-cell

31 Benchmarks and packaged pipelines standardize omics analysis toolsTools & infrastructure

32 Generative models steered by RL, fragments, and SMARTS for molecule and linker designStructural bio & protein design

33 Pretrained generative models extend to fMRI forecasting, diagnosis, and imagerySystems biology

34 Graph storage and streaming parsers reshape population-scale variant computationTools & infrastructure

35 Adhesion, noise, and discretization shape collective and single-cell migration modelsSystems biology

36 Updated tools for microbial pangenomics, plasmid recovery and phylogenetic inferenceSystems biology

37 Network models nominate interventions from omics and screening dataSystems biology

38 Sequencing and inference methods resolve nucleic acid sequence, structure, and usageGenomics

39 Encoding spatial and relational structure in high-dimensional omics analysisSingle-cell

40 Design-aware statistical models for differential analysis of omics and imaging dataSystems biology