AI in science, biology, medicine, and bioinformatics.
A peer-reviewed Nature study trains on 21 million RNA sequence–structure pairs and tests whether structural supervision improves RNA prediction. The results are promising, but benchmark success is not experimental validation.
A Nature study combines multi-ancestry genetics, transcriptomics, and computational gene prioritization to map 127 Alzheimer's risk loci. Its microglial signal is stronger than its neuron-specific claims.
I compared scGPT and Geneformer with PCA, Harmony, and scVI on two single-cell RNA-seq datasets. The results were useful, but not the sweeping foundation-model win one might expect.
A Nature study from Google DeepMind shows that detectable watermarks can be embedded in designed protein sequences and predicted structures while preserving tested function, but robustness and deployment remain open problems.
Anthropic reports that autonomous Claude agents surfaced ART, a repeat-associated reverse-transcriptase system in phages, but the preprint also exposes important limits in reproducibility and biological validation.
A Nature study turns papers and their code into MCP-backed agents; 74 of 100 computational-biology papers were successfully agentified, showing both the promise and the dependency problem.
