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The paper uses flow molecular dynamics simulations to study how mechanical forces regulate von Willebrand factor's conformational changes through glycan-modulated autoinhibitory modules.

This paper evaluates CPU and GPU-based workflows for high-accuracy fungal ITS metabarcoding using machine learning, showing GPU processing improves species-level accuracy while CPU workflows achieve genus-level concordance.

The paper compares differential expression and co-expression network analysis for microglia transcriptomics, finding co-expression analysis better identifies functional ontologies.

FAVABEAN and FALAPhyl are open-source pipelines for processing and analyzing 16S rRNA microbiome data, validated through three case studies on oral microbiomes.

The paper uses genomic context analysis to investigate the functional roles of different CODH clades, revealing distinct gene clustering patterns and regulatory associations.

REBEL resolves dependency issues in bioinformatics by providing a reproducible environment framework that improves installation success rates and enables deterministic builds.

FLASH predicts phenotype from sequence using deep learning, achieving high accuracy on unseen variants and identifying new virulence predictors across microbes.

This study uses computational methods to analyze how different peptide ligands interact with the human urotensin II receptor, revealing structural differences in receptor activation.

RNApdbee 3.0 provides a unified web server for RNA secondary structure annotation using 3D coordinates, integrating 2D and 3D data and standardizing inputs to PDBx/mmCIF.

MAVISp provides a modular structural framework for analyzing protein variant effects, offering over eight million variants across 1000 proteins with curated data and a web server for accessibility.

This paper proposes a framework using information entropy and evolutionary conservation to construct gene regulatory networks from sequence data, demonstrating improved edge discrimination and directionality resolution.

MitoChontrol improves single-cell RNA sequencing quality control by using a probabilistic framework to filter out compromised cells while preserving biologically relevant mitochondrial content.

The paper estimates volatile metabolite levels in cheese using metatranscriptomic data and machine learning to link gene expression to biochemical pathways.

This study shows that ultra low-coverage whole genome sequencing can reliably estimate canine inbreeding using runs of homozygosity and inbreeding coefficients.

The paper introduces a pipeline for single-cell proteomics analysis that addresses challenges in data processing and annotation using context-aware methods and large language models.

STDrug improves drug repurposing by integrating spatial transcriptomics with machine learning to prioritize patient-specific therapies.

This study benchmarks RNA modification detection models using synthetic and biological data, finding high reliability but issues with false positives and cross-reactivity.

MolClaw is an autonomous agent that evaluates, screens, and optimizes drug molecules using a hierarchical skill system, achieving state-of-the-art performance in complex workflows.

COFFEE-PRESC is a fast pre-screening method that uses fragment pairs to efficiently filter large compound libraries with higher accuracy than existing tools.

PrefixProt uses prefix tuning to design proteins with controllable properties, outperforming other methods in efficiency and multi-objective control.