AI Briefing#bioinformatics
The latest AI and developer news about #bioinformatics, with the original source and a short summary.
The latest AI and developer news about #bioinformatics, with the original source and a short summary.
The benchmark uses 5,800 labeled combinations where every test peptide is at least four edits away from any publicly reported TCR specificity.
The dataset includes 300B+ tokens of genomes, 200K SFT conversations, and 98K RL prompts, with code and recipes available for other models.
The 14.8B model averages 66.7% on five DNA benchmarks, outperforming Qwen3-14B by 27 points and running on a single 80 GB GPU.
The pipeline reduces crop genetics analysis from years of field trials to hours of computation by using evolutionary constraints to break genetic ties.
Apple has released SimpleDesign, a protein design model that trains directly in the data space without multi-stage training.
Google has released AlphaGenome Atlas, a 1-petabyte database that pre-computes the impact of 9 billion variants in the human genome.
This is a summary of a survey paper that organizes 25 deep learning methodologies applied to scRNA-seq analysis into 6 categories.
LG AI Research has developed a new AI model based on a Conditional Attention mechanism that effectively predicts the interaction between HLA and peptides.
LG AI Research has developed MOIRA, a multimodal integration framework that maintains high accuracy even in clinical settings with missing data.
Anthropic has launched a beta of Claude Science, an AI workbench that helps researchers with data analysis and paper writing.
Developed in collaboration with Microsoft, Talos increases rare disease diagnosis rates and reduces the workload of medical staff through automated genomic reanalysis.
Improving the performance of biology agents requires redesigning human-centric data infrastructure to be agent-friendly.
Anthropic has improved Claude's performance for life sciences research and introduced scientific platform Connectors and Agent Skills.
A community-driven research project is launching to accelerate food allergy research using AI technology.
Intel Gaudi 2 accelerators significantly improved the inference and fine-tuning performance of the protein language model ProtST compared to A100.