974 papers
FlashDeconv is a scalable, structure-preserving deconvolution method that enables accurate, atlas-scale spatial analysis of Visium HD data at multiple resolutions, revealing critical biological insights such as tissue-specific resolution horizons and previously undetectable immune microdomains that are missed by current classification-based approaches.
Col-Ovo is a smartphone-based artificial intelligence tool that enables rapid, automated counting of *Aedes aegypti* eggs from field images with over 95% accuracy, effectively overcoming the manual processing bottleneck in large-scale mosquito surveillance programs.
RiboPipe is a computationally efficient framework that jointly optimizes transcript-level mean ribosome load prediction and codon-level coverage modeling using a peak-weighted loss to accurately impute ribosome profiling coverage for low-coverage transcripts, even when trained on limited data.
MiCBuS is a computational method that identifies marker genes for unknown cell types by generating Dirichlet-pseudo-bulk RNA-seq data to overcome the limitations of heterogeneous bulk and incomplete single-cell RNA-seq datasets where traditional differential analysis fails.
To address the lack of comprehensive metabolomics resources for the model organism *Pseudomonas putida* KT2440, the authors developed the publicly available PPMDB v1, a reference database that integrates curated and computationally predicted metabolites with analytical properties and pathway annotations to facilitate untargeted metabolomics and biological interpretation.
The paper introduces PACMON, a scalable Bayesian latent factor model that integrates multi-omics data from large-scale perturbation screens to interpret gene function by jointly inferring pathway-level programs and their specific modulation by experimental perturbations.
The paper proposes ERFMTDA, an enhanced rotative factorization machine framework that integrates explicit biological attributes, complex feature interactions, and motif-based negative sampling to accurately predict tsRNA-disease associations, outperforming existing state-of-the-art methods.
TCRseek is a scalable, two-stage retrieval framework that combines biologically informed windowed k-mer embeddings with approximate nearest neighbor indexing and exact reranking to enable efficient, high-sensitivity search of large T-cell receptor repertoires, achieving significant speedups over brute-force methods while maintaining near-optimal accuracy.
This study demonstrates that relying on a single SNP annotation tool or gene model leads to significant discrepancies and incomplete pathway inference, whereas a multi-tool, multi-model strategy maximizes coverage and ensures robust, reproducible genomic interpretation.
dreampy is a Python implementation of the R/Bioconductor dreamlet framework that enables pseudobulk mixed-model differential expression analysis for single-cell RNA-seq data by integrating voom precision-weighted linear mixed models with empirical Bayes moderation within the scverse ecosystem.
The paper introduces Micro16S, a deep learning framework that generates phylogenetically informed, region-invariant 16S rRNA embeddings to improve microbiome representation, though its current performance on classification tasks remains inferior to classical machine learning baselines due to challenges like class imbalance.
ExposoGraph is an interactive knowledge-graph platform that unifies data from major carcinogen and pharmacogenomic resources to visualize and explore the complex pathways linking carcinogenic exposures, metabolic activation/detoxification, DNA damage, and genetic variations for improved gene-environment interaction research.
The NIH Bridge2AI Standards Working Group proposes a comprehensive framework of seven core dimensions—extending beyond FAIR principles to include ethics, provenance, and explainability—to define actionable criteria for ensuring the ethical, robust, and machine-actionable AI-readiness of biomedical data before model training.
This study identifies remarkably long, conserved Entomopoxvirus-like DNA sequences spanning up to 140 kb within human centromeric and pericentromeric regions, revealing them as integral, transcriptionally active components of eukaryotic chromosomes.
This study evaluates the performance of bisulfite sequencing methods (RRBS and WGBS) and mapping software in genetically diverse stickleback populations, revealing significant variations in methylation detection and tool accuracy to provide methodological recommendations for improving the reliability of DNA methylation profiles in non-model organisms.
The paper introduces ChEA-KG, an interactive web server that presents a high-quality, signed, and directed human gene regulatory network derived from ChEA3 enrichment analysis, featuring tools for network visualization, transcription factor queries, and specialized atlases covering cell types, cancers, mechanisms of action, and aging.
This paper introduces "Identification of Distinct topological structures" (ID), a novel method that constructs alternative low-dimensional parametrizations and applies finite perturbations to identify gene sets governing simultaneous biological processes in high-dimensional single-cell RNA sequencing data, thereby revealing structures and cellular responses that are otherwise missed.
The paper introduces VINE, a novel variational inference method that leverages node embeddings and distance-based decoders to achieve orders-of-magnitude faster and scalable Bayesian reconstruction of both species and cell-lineage phylogenies while maintaining accuracy comparable to traditional Markov chain Monte Carlo approaches.
SuperFocus is a modality-agnostic computational platform that integrates histopathology with single-cell spatial multi-omics by accurately projecting genome-scale molecular data onto tissue morphology without external references, thereby enabling scalable, cell-resolved molecular pathology analyses across diverse disease contexts.
This study demonstrates that a single AI agent can achieve statistical equivalence with human-extracted meta-analysis data across five diverse agricultural datasets, effectively resolving the primary bottleneck of data extraction while reducing costs by one to two orders of magnitude.