1005 papers
This paper establishes a formal framework proving that under the Infinite Sites Model, the NP-hard problem of optimizing genome orderings for phylogenetic compression becomes polynomially solvable via Neighbor Joining, thereby mathematically explaining the high efficacy of tree-based heuristics in bacterial genomics.
GYDE is an open-source, web-based collaborative platform that empowers bench scientists to easily access and utilize AI-powered tools for protein and antibody design, analysis, and drug discovery through an intuitive visual interface.
This study introduces TattleTail, a novel bioinformatic tool that accurately distinguishes Pseudomonas aeruginosa pyocins from prophages by leveraging conserved gene markers and the absence of viral packaging genes, a capability experimentally validated through the successful identification, induction, and characterization of bactericidal pyocins in clinical isolates.
KyDab is a comprehensive, publicly accessible database that curates over 120,000 paired antibody sequences and full-funnel selection data from standardized Kymouse immunization studies to support the development and evaluation of artificial intelligence models for antibody discovery.
MEIsensor is a deep-learning framework that significantly improves the accuracy and computational efficiency of detecting and classifying mobile element insertions (Alu, LINE1, and SVA) from long-read sequencing data, particularly within complex and repetitive genomic regions where existing tools struggle.
This paper introduces a reinforcement learning framework to fine-tune diffusion-based molecular docking models, significantly enhancing their ability to generate physically valid and interaction-preserving poses without compromising structural accuracy or increasing inference-time computation.
SAPTICoN is a robust, no-code, Snakemake-based pipeline built on the Seurat framework that enables biologists with limited computational expertise to perform reproducible single-cell transcriptomic analyses on non-model species and poorly annotated tissues by automatically generating necessary annotation packages.
This paper introduces two maximum likelihood methods that incorporate lineage abundance priors—interpreting multifurcations as unresolved signals of common strains or modeling sequencing rates proportional to prevalence—to significantly improve the accuracy of phylogenetic inference for rapidly evolving pathogens like SARS-CoV-2 by prioritizing the placement of sequences onto common ("horse") rather than rare ("zebra") lineages.
This paper proposes a method to enhance single-cell RNA sequencing analysis by jointly training language models on both quantitative scRNA-seq data and targeted biomedical literature, thereby creating interpretable, knowledge-augmented embeddings that improve functionality, disease association, and temporal trajectory insights.
Seqwin is an open-source framework that automates the discovery of microbial signature sequences by leveraging weighted pan-genome minimizer graphs to efficiently identify highly specific and sensitive targets across tens of thousands of genomes, outperforming existing methods in both speed and accuracy for diagnostic assay design.