v261: Proceedings of MLCB 2024

2026-06-10 · Source: Proceedings of Machine Learning Research · Field: Science & Research — Life Sciences & Biology, Artificial Intelligence & Machine Learning, Mathematics & Computational Sciences · Depth: Expert, quick

Summary

This volume, "Machine Learning in Computational Biology," presents research from the 19th meeting held in Seattle, WA, USA, on September 5-6, 2024. The papers showcase diverse applications of machine learning, including TransformerBeta for peptide binder design, Wave-LSTM for somatic whole genome copy number analysis, and graph learning for protein structures. Other contributions address improving ribosome density prediction, context-specific network embedding with CONE, and joint trajectory and network inference. Critical discussions also include the challenge of data leakage in Protein LLM pretraining and the development of models like QuickBind for molecular docking and PathoLM for identifying DNA sequence pathogenicity. MedGraphNet further demonstrates the utility of multi-relational graph neural networks and text knowledge for biomedical predictions.

Key takeaway

The 19th Machine Learning in Computational Biology meeting (PMLR Vol 261) presents significant advances in applying ML to diverse biological problems. Key contributions include novel deep learning architectures like TransformerBeta for peptide design, Wave-LSTM for genomic analysis, and Graph Neural Networks for protein structure and biomedical predictions. This volume offers critical insights for researchers and practitioners in bioinformatics, drug discovery, and genomics, addressing challenges from molecular docking to identifying pathogenicity from DNA.

Topics

• Computational Biology
• Transformer Models
• Graph Neural Networks
• Genomic Data Analysis
• Protein Structure Prediction

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

Related on AIssential

• v165: Proceedings of MLCB 2021 — Volume 165 of the "Proceedings of the Machine Learning in Computational Biology meeting," held in November 2021, presents several research papers applying advanced computational techniques to biological problems.
• v311: Proceedings of MLCB 2025 — Machine learning, particularly generative AI, is rapidly advancing computational biology applications.
• A physics-informed graph neural network to approximate docking-based binding affinity for DYRK2 in Alzheimer’s drug repurposing — Integrating physical interaction terms into GNNs can create efficient, interpretable surrogates for computationally intensive molecular docking.
• Predicting and interpreting cell-type-specific drug responses in the small-data regime using inductive priors — PrePR-CT uses graph-based deep learning with cell-type-specific co-expression networks to predict drug responses in data-limited settings.
• Convolutional neural networks quantify antibiotic resistance in Mycobacterium tuberculosis with diagnostic grade accuracy and predict treatment response — CNNs can predict antibiotic MICs from genomic data with clinical accuracy, leveraging diverse biological features.
• Is AI Drug Discovery Becoming a Data Infrastructure Race? — AI drug discovery's primary bottleneck is the availability of diverse, relevant biological training data, not model architecture or compute.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Proceedings of Machine Learning Research.