991 papers
This study presents an AI-driven decision-support tool that utilizes respiratory microbiome profiles and machine learning, specifically XGBoost, to accurately triage COVID-19 patients by identifying dysbiotic microbial signatures associated with severe outcomes.
The paper introduces plsMD, a novel computational tool that significantly improves the reconstruction of full plasmid sequences from short-read whole-genome sequencing data by integrating Unicycler assemblies with replicon and plasmid databases, thereby outperforming existing methods in accuracy and enabling more robust phylogenetic and antimicrobial resistance tracking studies.
The paper introduces DOTSeq, a comprehensive statistical framework that enables genome-wide detection of differential ORF usage and translation efficiency in both bulk and single-cell ribosome profiling experiments, overcoming the limitations of gene-level analysis to uncover translational control events at the ORF level.
This paper addresses the lack of a standardized definition for "sequences of concern" in nucleic acid synthesis by developing and validating a science-based rubric through stakeholder review and testing on a large dataset, which significantly reduces categorization disputes and provides a concrete foundation for future biosecurity screening standards.
This study evaluates eight differential abundance analysis methods using permutation-based null hypothesis testing across multiple datasets, revealing that while complex compositional and negative binomial models often produce biased p-values, simpler classical tests like the t-test and Wilcoxon test demonstrate superior robustness and reliability.
This paper introduces an ultra-fast, coarse-grained generative framework using Flow Matching to predict protein contact maps and Circuit Topology from amino acid sequences in milliseconds, achieving high accuracy and providing a physically interpretable view of conformational flexibility to facilitate large-scale exploration of the genotype-phenotype map.
The paper introduces usiGrabber, a scalable and portable framework that automates the extraction and curation of large-scale proteomics spectra data from public repositories like PRIDE, demonstrating its effectiveness by rapidly constructing a massive phosphorylation dataset to retrain a machine learning classifier with performance comparable to manually curated models.
The paper introduces DeSCENT, a framework that reconstructs single-cell RNA-seq profiles from bulk data to enable multimodal transcriptome-based survival analysis, demonstrating consistent improvements in cancer survival prediction accuracy across eight TCGA cohorts compared to existing models.
Ryder is a flexible Python package that normalizes diverse epigenomic sequencing data by leveraging stable internal reference regions to correct technical variability, thereby enhancing the accuracy of genome-wide signal alignment and the detection of genuine biological changes.
This paper introduces HKS, a data structure built on the Spectral Burrows-Wheeler Transform that enables exact, lossless hierarchical annotation of variable-length k-mers by resolving multi-matches through a user-defined category hierarchy and enhancing specificity with a context-aware smoothing algorithm, achieving high accuracy in genomic feature assignment while maintaining performance comparable to existing tools like Kraken2.
The paper introduces HARVEST, an agentic AI pipeline that autonomously extracts 3.36 million previously inaccessible bioactivity records from pharmaceutical patents, revealing significant generalization gaps in current AI models when evaluated on this newly recovered data.
The paper introduces PREMISE, a high-performance, quality-aware probabilistic framework that utilizes alignment-based Expectation-Maximization to overcome the limitations of k-mer methods, enabling accurate identification of viral subtypes, estimation of relative abundances, and detection of complex events like reassortment and recombination in Influenza A viruses from metagenomic sequencing data.
The paper introduces Zigo, a novel machine learning-based method that accurately and efficiently infers genetic sex from standard VCF files using a reference-free, threshold-free polynomial equation derived from X-chromosome genotype distributions, demonstrating robust performance across diverse genomic data types and limited variant sets.
This paper introduces a novel method for comparing pangenome diversity by interpolating and extrapolating node counts in colored compacted de Bruijn graphs, utilizing Hill numbers to adjust for varying genome numbers and mitigate the disproportionate influence of rare genomic sequences.
GOTFlow is a novel framework that leverages graph-constrained optimal transport in a learned latent space to infer directed, interpretable population transitions and molecular drivers from cross-sectional biomedical data, overcoming limitations of existing methods in modeling non-linear, heterogeneous, and unbalanced biological dynamics.
SpeciefAI is a transformer-based model that generates multi-species, mRNA-level antibody framework regions tailored to specific host species and input CDRs, ensuring both high sequence similarity to natural repertoires and optimized codon adaptation for in vivo therapeutic expression.
This paper introduces homology-based metrics to evaluate and compare pangenome graphs (specifically variation graphs and whole genome alignments), proposes transformations between these models, and provides a software package to implement these methods.
The paper proposes scRGCL, a neighbor-aware graph contrastive learning framework that enhances single-cell RNA sequencing clustering by integrating cluster-level guidance and a re-weighting strategy to preserve intra-cluster compactness, thereby outperforming state-of-the-art methods across multiple datasets.
This paper introduces faster, space-efficient subset rank data structures requiring less than 3 bits per k-mer, which enable new Pareto-optimal k-mer lookup structures for genomic analysis at the low-memory end of the space-time spectrum.
This paper introduces 10-minimizers, a new class of constant-space sampling schemes that provably achieve lower density than random minimizers in the non-asymptotic regime and offer competitive k-mer retrieval speeds through a specific variant called "spacers."