Week 28, 2026 · Jul 6 – Jul 12

Top 40

ML / AI methods

Interpretability Tools Recover Known Visual Selectivity in Neural Models

ML / AI methods

Learned latents steer protein conformations and encode evolutionary structure

ML / AI methods

Learned multimodal and graph representations for protein and RNA prediction

ML / AI methods

Spatial models couple influenza dispersal, defective particles and interferon signalling

ML / AI methods

Benchmarks and learned models expose gaps in metagenomic analysis software

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

7 Biological foundation models face scrutiny over pretraining, benchmarks, and baselinesML / AI methods

8 Comparative genomics partitions duplication, transfer and synteny across lineagesGenomics

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

10 Graph neural networks model molecular interactions, drug synergy and disease driversSystems biology

11 Cross-cohort tests expose limits of EEG, vocal, and transcriptomic biomarkersML / AI methods

12 Benchmarks and packaged pipelines standardize omics analysis toolsTools & infrastructure

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

14 Network models nominate interventions from omics and screening dataSystems biology

15 Chromosome-level assemblies link genome gigantism and gene loss to lineage evolutionGenomics

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

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

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

19 Reduced recurrent models reproduce complex dynamics across neural and molecular systemsSystems biology

20 Reproducible environments, DNA library design and standard formats for biology toolsStructural bio & protein design

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

22 New tools speed variant annotation, GWAS, and epigenomic and cytometry analysisTools & infrastructure

23 Noise-driven dynamics shape neuronal firing, calcium states, and gait fluctuationsSystems biology

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

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

26 Models link non-genetic B cell state dynamics to antibody affinity maturationSystems biology

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

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

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

30 Isoform-resolved single-cell and single-molecule methods quantify transcript variantsEpigenomics

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

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

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

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

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

36 Transformer and deep learning tools benchmarked against established genomics methodsGenomics

37 Benchmarks probe AlphaFold model accuracy across peptides, RNA, and antibody complexesStructural bio & protein design

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

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

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