949 papers
The paper introduces GeoARG, a geometry-enhanced protein language modeling framework that overcomes the limitations of sequence homology to successfully identify thousands of evolutionarily distant and structurally conserved antibiotic resistance genes in metagenomic data.
This study reveals that the UBL3 UBL domain exhibits unique, high C-terminal flexibility and distinct helix-centered dynamic control compared to other ubiquitin-like proteins, providing a structural basis for its specific regulatory functions and potential as a therapeutic target.
This paper introduces a geometry-aware framework that distinguishes interface-associated enrichment from true spatial localization in ligand-receptor interactions, revealing that tumor cell communication is better characterized as a continuum of spatial constraint rather than discrete communication regimes.
This study demonstrates that simultaneously correcting for both expression-based surrogate variables and genotype-based principal components in differential expression analysis significantly improves cross-dataset replicability and biological recall of known disease genes compared to using either method alone, establishing a combined correction framework as a standard practice for transcriptomic studies with matched genotype data.
This paper introduces *sosta*, an open-source Bioconductor package that enables the direct analysis of multicellular anatomical structures in spatial omics data, shifting the focus from single-cell arrangements to biologically relevant tissue-level organization.
This paper proposes a novel four-layer integrative framework that constructs gene regulatory networks directly from DNA sequences by leveraging information entropy, evolutionary conservation, and deep learning embeddings to bridge nucleotide-level constraints with network-level regulatory logic.
STDrug is a novel computational framework that leverages spatial transcriptomics, graph-based modeling, and multimodal learning to overcome the limitations of single-cell approaches by capturing tissue microenvironment context, thereby enabling more accurate, patient-specific drug repurposing for cancers like hepatocellular and prostate carcinoma.
This paper argues that co-expression network analysis offers a more effective and parsimonious framework than traditional differential expression analysis for characterizing microglia, as it reveals context-dependent, concurrent molecular programs that better explain the cell type's functional heterogeneity.
The paper introduces Locat, a novel framework that identifies highly specific marker genes in single-cell transcriptomics by jointly testing for expression enrichment within compact cellular regions and depletion elsewhere, thereby enabling robust, interpretable, and batch-free cross-condition comparisons of cell populations and developmental trajectories.
This paper introduces CASPA, an automated, end-to-end pipeline for single-cell proteomics that overcomes existing analytical limitations through adaptive quality control, entropy-guided batch correction, and a refined large language model framework for context-aware cell type annotation, achieving high accuracy validated against orthogonal ground truth across diverse biological datasets.
The paper introduces FLASH, an interpretable deep learning framework that directly analyzes raw sequencing reads to accurately predict microbial phenotypes and identify novel genetic determinants across diverse organisms, overcoming key limitations of traditional GWAS and existing machine learning methods.
This study demonstrates that ultra low-coverage whole genome sequencing (ulcWGS) serves as a cost-effective and reliable alternative to high-coverage methods for accurately estimating canine inbreeding levels across diverse breeds.
The paper introduces DrugPlayGround, a novel framework designed to objectively benchmark and evaluate the performance of large language models in generating accurate descriptions of drug characteristics and interactions while collaborating with domain experts to validate their chemical and biological reasoning capabilities for advancing drug discovery.
This study demonstrates that machine learning models trained on metatranscriptomic data can successfully predict final volatile compound profiles in cheese and link specific gene signatures to the underlying biochemical pathways responsible for flavor generation.
REBEL is a framework that ensures long-term, FAIR-compliant reproducibility in bioinformatics by using deep code inspection and curated knowledge to resolve missing system dependencies and generate self-contained, deterministic environments without requiring containerization expertise.
By integrating AlphaFold modeling, fragment mapping, and microsecond molecular dynamics simulations, this study reveals how the human urotensin II receptor exhibits ligand-specific structural plasticity, where subtle differences between hUII and URP induce distinct conformational constraints and signaling outcomes through unique interaction networks.
MitoChontrol is a novel, cell-type-aware probabilistic framework that improves single-cell RNA sequencing quality control by dynamically defining mitochondrial filtering thresholds based on cluster-specific distributions, thereby more accurately distinguishing compromised cells from biologically viable populations than traditional fixed-threshold methods.
This study utilizes flow molecular dynamics simulations to demonstrate how hydrodynamic forces drive von Willebrand factor from an autoinhibited compact state to an activated extended conformation, revealing the specific roles of glycan-modulated autoinhibitory modules in mechanosensitive regulation.
The Cell Positioning System (CPS) is a novel context-aware implicit neural representation framework that leverages privileged multi-scale context distillation to map physical coordinates to high-fidelity spatial transcriptomics, effectively overcoming data sparsity and noise to achieve state-of-the-art imputation, denoising, super-resolution, and interpretability with linear computational scalability.
Leveraging the 2021 release of AlphaFold2 as a quasi-experiment, this study demonstrates that AI predictions can expand scientific frontiers by reversing the decline in research on novel proteins and redirecting collective attention toward understudied genes and targets, thereby challenging concerns that AI merely reinforces established scientific canons.