Awesome Genomics

Key papers

• An AI system to help scientists write expert-level empirical software (2025)
  Eser Ayg\"un et al.
  12.64
• DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA (2024)
  Aman Patel et al.
  11.97
• InternAgent-1.5: A Unified Agentic Framework for Long-Horizon Autonomous Scientific Discovery (2026)
  Shiyang Feng et al.
  11.14
• BixBench: a Comprehensive Benchmark for LLM-based Agents in Computational Biology (2025)
  Ludovico Mitchener et al.
  10.71
• Towards an AI co-scientist (2025)
  Juraj Gottweis et al.
  9.77
• DeepScientist: Advancing Frontier-Pushing Scientific Findings Progressively (2025)
  Yixuan Weng et al.
  9.55
• Accurate RNA 3D structure prediction using a language model-based deep learning approach (2022)
  Tao Shen et al.
  9.35
• Artificial Intelligence and Deep Learning Algorithms for Epigenetic Sequence Analysis: A Review for Epigeneticists and AI Experts (2025)
  Muhammad Tahir et al.
  8.64
• MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research (2025)
  James Burgess et al.
  8.49
• Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design (2025)
  Tong Chen et al.
  8.29
• HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data (2025)
  Hiren Madhu et al.
  7.70
• RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks (2024)
  Rafael Josip Peni\'c et al.
  7.62
• drGT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network (2024)
  Yoshitaka Inoue et al.
  7.38
• A Text-guided Protein Design Framework (2023)
  Shengchao Liu et al.
  7.32
• Gumbel-Softmax Flow Matching with Straight-Through Guidance for Controllable Biological Sequence Generation (2025)
  Sophia Tang et al.
  7.24
• Contextualizing biological perturbation experiments through language (2025)
  Menghua Wu et al.
  7.19
• BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments (2024)
  Yusuf Roohani et al.
  7.00
• Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry (2025)
  Tsz Wai Ko et al.
  6.89
• Understanding protein function with a multimodal retrieval-augmented foundation model (2025)
  Timothy Fei Truong Jr et al.
  6.75
• PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis (2024)
  Yan Wu et al.
  6.50
• Kosmos: An AI Scientist for Autonomous Discovery (2025)
  Ludovico Mitchener et al.
  6.40
• RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design (2024)
  Rishabh Anand et al.
  6.39
• Molecular-driven Foundation Model for Oncologic Pathology (2025)
  Anurag Vaidya et al.
  6.12
• Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony (2025)
  James Bagrow and Josh Bongard
  6.12
• DualEquiNet: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules (2025)
  Junjie Xu et al.
  6.12
• scDrugMap: Benchmarking Large Foundation Models for Drug Response Prediction (2025)
  Qing Wang et al.
  6.07
• Machine Learning Methods for Gene Regulatory Network Inference (2025)
  Akshata Hegde et al.
  6.01
• Interpretable Graph Kolmogorov-Arnold Networks for Multi-Cancer Classification and Biomarker Identification using Multi-Omics Data (2025)
  Fadi Alharbi et al.
  5.96
• Whole-Genome Phenotype Prediction with Machine Learning: Open Problems in Bacterial Genomics (2025)
  Tamsin James et al.
  5.90
• Iterative Distillation for Reward-Guided Fine-Tuning of Diffusion Models in Biomolecular Design (2025)
  Xingyu Su et al.
  5.87
• AI-driven multi-omics integration for multi-scale predictive modeling of causal genotype-environment-phenotype relationships (2024)
  You Wu (1) et al.
  5.78
• GBDTSVM: Combined Support Vector Machine and Gradient Boosting Decision Tree Framework for efficient snoRNA-disease association prediction (2025)
  Ummay Maria Muna et al.
  5.76
• CellVerse: Do Large Language Models Really Understand Cell Biology? (2025)
  Fan Zhang et al.
  5.76
• Benchmarking AI scientists for omics data driven biological discovery (2025)
  Erpai Luo et al.
  5.76
• Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing (2025)
  Zijie Qiu et al.
  5.76
• LOCO-EPI: Leave-one-chromosome-out (LOCO) as a benchmarking paradigm for deep learning based prediction of enhancer-promoter interactions (2025)
  Muhammad Tahir et al.
  5.70
• Multi-modal AI for comprehensive breast cancer prognostication (2024)
  Jan Witowski et al.
  5.68
• Democratizing AI scientists using ToolUniverse (2025)
  Shanghua Gao et al.
  5.63
• Protein Large Language Models: A Comprehensive Survey (2025)
  Yijia Xiao et al.
  5.59
• Hyperbolic Genome Embeddings (2025)
  Raiyan R. Khan et al.
  5.52
• ProtChatGPT: Towards Understanding Proteins with Large Language Models (2024)
  Chao Wang et al.
  5.51
• JanusDNA: A Powerful Bi-directional Hybrid DNA Foundation Model (2025)
  Qihao Duan et al.
  5.40
• MAMMAL -- Molecular Aligned Multi-Modal Architecture and Language (2024)
  Yoel Shoshan et al.
  5.37
• On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing (2025)
  Samantha Petti et al.
  5.35
• Diffusion on language model encodings for protein sequence generation (2024)
  Viacheslav Meshchaninov et al.
  5.29
• Differentiable Folding for Nearest Neighbor Model Optimization (2025)
  Ryan K. Krueger et al.
  5.29
• Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents (2025)
  Shuo Ren et al.
  5.29
• HybriDNA: A Hybrid Transformer-Mamba2 Long-Range DNA Language Model (2025)
  Mingqian Ma et al.
  5.24
• Learning to Discover Regulatory Elements for Gene Expression Prediction (2025)
  Xingyu Su et al.
  5.24
• LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? (2025)
  Rushil Gupta et al.
  5.21
• Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge (2023)
  Gilchan Park et al.
  5.12
• Benchmarking and Evaluation of AI Models in Biology: Outcomes and Recommendations from the CZI Virtual Cells Workshop (2025)
  Elizabeth Fahsbender et al.
  5.10
• Virtual Cells: Predict, Explain, Discover (2025)
  Emmanuel Noutahi et al.
  4.98
• PROTOCOL: Late Interaction Retrieval for Protein Homolog Search (2026)
  Gabrielle Cohn et al.
  4.95
• PLM-eXplain: Divide and Conquer the Protein Embedding Space (2025)
  Jan van Eck et al.
  4.93
• Hallucination, reliability, and the role of generative AI in science (2025)
  Charles Rathkopf
  4.93
• A Phylogenetic Approach to Genomic Language Modeling (2025)
  Carlos Albors et al.
  4.87
• SciHorizon: Benchmarking AI-for-Science Readiness from Scientific Data to Large Language Models (2025)
  Chuan Qin et al.
  4.87
• BAnG: Bidirectional Anchored Generation for Conditional RNA Design (2025)
  Roman Klypa et al.
  4.82
• Distribution-Conditioned Transport (2026)
  Nic Fishman et al.
  4.81