June 2026

Top 40

ML / AI methods

Recovering single-cell kinetics and trajectories from static and noisy measurements

Genomics

Ancient and phased human genomes test selection and imputation inference methods

Tools & infrastructure

New tools speed variant annotation, GWAS, and epigenomic and cytometry analysis

Tools & infrastructure

Reproducible tools for assessing and streamlining omics data analysis workflows

Systems biology

Noise, feedback and network structure shape emergent states in biological models

6 Learned predictors score protein interactions, variant effects, and peptide activityML / AI methods

7 Comparative genomics partitions duplication, transfer and synteny across lineagesGenomics

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

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

10 Correcting RNA degradation and hashtag demultiplexing in single-cell transcriptomicsSystems biology

11 Internal ground truths benchmark RNA-seq transcript and expression inference toolsGenomics

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

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

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

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

16 Benchmarking segmentation and synthetic ground truth for 3D tissue and organoid imagingML / AI methods

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

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

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

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

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

22 Benchmarks and learned models expose gaps in metagenomic analysis softwareML / AI methods

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

24 Biophysical models link ion channel, noise and modulation to network firing dynamicsSystems biology

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

26 Generative models map mass spectra directly to candidate molecular structuresML / AI methods

27 Multi-omic and structural data are converted into drug and target prioritization outputsGenomics

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

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

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

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

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

33 Benchmarks and packaged pipelines standardize omics analysis toolsTools & infrastructure

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

35 Transformer and deep learning tools benchmarked against established genomics methodsGenomics

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

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

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

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

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