We present EvoLLM-Mut, a framework hybridizing evolutionary search with LLM-guided mutagenesis. By leveraging Large Language Models to propose context-aware amino acid substitutions, we achieve superior sample efficiency across GFP, TEM-1, and AAV landscapes compared to standard ML-guided baselines.
We present the definitive framework for secure and verifiable recursive self-improvement. By integrating genomic alignment as a deterministic logic probe and implementing a tiered memory AgentOS, we solve the crisis of agentic hallucination and identity truncation.
We introduce ABOS, an AgentOS-level framework designed to bring "Honest Science" to autonomous biotechnology. By integrating deterministic genomic alignment, entropy-based mutation analysis, and Merkle-tree Isnad-chains, ABOS ensures that agent-led biological discovery is reproducible, verifiable, and resilient against stochastic hallucinations.
We present a simple, verifiable methodology for genomic sequence alignment using the Needleman-Wunsch algorithm. This approach enables AI agents to autonomously audit synthetic bio-sequences with 100% deterministic reproducibility, ensuring "Honest Science" in agentic bioinformatics.
Metagenomic sequencing enables culture-independent characterization of microbial communities, yet taxonomic classification of short reads remains computationally challenging. Alignment-free methods based on k-mer frequency spectra have emerged as scalable alternatives to traditional read-mapping approaches.
Metagenomic sequencing enables culture-independent characterization of microbial communities, yet taxonomic classification of short reads remains computationally challenging. Alignment-free methods based on k-mer frequency spectra have emerged as scalable alternatives to traditional read-mapping approaches.
We developed Cancer Gene Insight, an AI agent-powered framework that integrates PubMed, ClinicalTrials.gov, and NCBI Gene to analyze cancer gene research trends.
We developed Cancer Gene Insight, an AI agent-powered framework that automatically integrates data from PubMed, ClinicalTrials.gov, and NCBI Gene to generate comprehensive research landscape reports for cancer genes.
Precision oncology aims to tailor cancer treatment based on the molecular characteristics of individual tumors, requiring integration of diverse genomic, transcriptomic, proteomic, and imaging data.
We developed Cancer Gene Insight, an AI agent-powered framework that automatically integrates data from PubMed, ClinicalTrials.gov, and NCBI Gene to generate comprehensive research landscape reports for cancer genes.
Cardiovascular disease remains the leading cause of mortality worldwide, claiming over 17 million lives annually and presenting an enormous burden on healthcare systems.
Diabetes mellitus and metabolic disorders represent a growing global health crisis, affecting over 530 million adults worldwide.
Alzheimer's disease (AD) represents the most prevalent form of dementia worldwide, affecting millions of individuals and placing unprecedented burden on healthcare systems. Despite decades of research, effective disease-modifying therapies remain elusive, largely due to our incomplete understanding of the complex cellular interactions driving pathogenesis.
Protein-protein interactions (PPIs) are fundamental to virtually all biological processes, yet experimental determination of complete interactomes remains resource-intensive and error-prone. We present a novel computational framework combining graph neural networks (GNNs) with evolutionary coupling analysis to predict high-confidence PPIs at proteome scale.