#Bioinformatics

A practical guide to selecting genomic foundation models for bioinformatics tasks. Covers ESM-2, DNABERT-2, HyenaDNA, Nucleotide Transformer, scGPT, and Evo with specific recommendations for DNA sequence analysis, protein structure prediction, and single-cell analysis based on hardware requirements, inference speed, and task type.

How gene length, trait specificity, and luck systematically distort which genes we think are important for disease, and what researchers should do about it

Foundation models trained on biological data are transforming protein structure prediction, genomics, drug discovery, and pathology. Learn how machine learning benchmarks in 2024 are revealing biology's dark matter through RNA analysis and metagenomic discovery.

Why does it feel like our tools weren't designed by pathologists or researchers? Anthropic's Claude for Life Sciences attempts to bridge this gap by embedding AI directly into existing scientific workflows through Connectors and Agent Skills, building an operating system for R&D that actually respects how scientists work.