974 papers
This paper introduces CosMxScope, an open-source Python framework that bridges the gap between CosMx Spatial Molecular Imager data and digital pathology tools by stitching image tiles, converting spatial coordinates into GeoJSON for QuPath compatibility, and enabling interactive visualization of gene expression alongside cell morphology.
The Track Hub Quickload Translator is a freely available Python-based web application that converts data between UCSC Genome Browser track hubs and Integrated Genome Browser Quickload formats, enabling researchers to visualize tens of thousands of published genome assemblies in either browser.
This paper presents BrainYears, a portable and cost-effective EEG-based machine learning model that accurately predicts brain age and demonstrates its utility as an intervention-ready biomarker by showing a significant reduction in predicted brain age following a neuromodulation program.
This study utilizes AI protein structure predictions to demonstrate that the hydrophobic ligand-binding cavity in vertebrate TLR2 is an evolutionarily conserved feature essential for pathogen recognition, while revealing that similar cavities in invertebrate TLRs and other LRR domains arose through independent convergent evolution rather than shared ancestry.
This paper presents an evolutionary AI framework that integrates generative models, reaction-based substructure searching, and iterative fine-tuning to efficiently navigate ultra-large chemical spaces, successfully identifying and validating novel, synthetically accessible ligands for the -opioid receptor within the Enamine REAL Space.
ClumPyCells is a statistical framework that accurately quantifies cell and marker aggregation in complex tissues by correcting for cell size biases and filtering out interfering cell types, thereby enabling more reliable spatial pattern analysis across various tumor types and omics technologies.
By constructing a transcriptomic atlas of 3,713 samples to identify four distinct molecular subtypes of sepsis, this study elucidates subtype-specific pathophysiological mechanisms and proposes targeted drug repurposing strategies, such as corticosteroids for C1 and methylene blue for the high-mortality C4 subtype, to advance precision medicine and explain the failure of previous broad-spectrum clinical trials.
The paper introduces methbiome, a reference-based Snakemake pipeline designed to profile DNA methylation signals in long-read metagenomic data from both Oxford Nanopore and PacBio platforms, enabling comparative epigenomic analysis across microbial communities and isolates.
The paper introduces the mod-minimizer, a simple and efficient two-step sampling algorithm that achieves provably lower and asymptotically optimal density compared to existing methods like random minimizers, resulting in significant space savings for k-mer indexing applications such as whole-genome storage.
This study benchmarks seven computational tools for single-cell transcriptome matching across ten organ systems to reveal their complementary strengths and establish a framework for the incremental growth and harmonization of cell atlases.
DeepBranchAI introduces a novel cascade workflow that overcomes the annotation bottleneck in 3D branching network segmentation by iteratively refining sparse labels through a positive feedback loop of random forests and expert input, ultimately enabling the training of a robust, topology-preserving 3D nnU-Net model that achieves high accuracy across diverse biological and medical datasets while significantly reducing manual annotation time.
This paper introduces a dynamic, run-length-compressed skiplist data structure for the graph Burrows-Wheeler transform (GBWT) that enables time and space-efficient pangenome operations on syncmer graphs, successfully building a 5.8 GB lossless representation of 92 human genomes in under an hour and supporting rapid sequence matching.
This study utilizes single-cell transcriptomics of human intracerebral hemorrhage patients to reveal that transient, TNF-driven activation of hematoma-infiltrating monocytes by microglia via TNFR2 signaling is associated with improved neurological recovery.
This study introduces a machine learning framework called Inverse Signal Importance (ISI) to analyze how medaka fish dynamically prioritize multiple environmental signals for gonadal development in natural settings, revealing complex adaptive mechanisms that are often missed in controlled laboratory studies.
This paper details the implementation of a novel method by Urzhumtsev & Lunin within CCTBX and Phenix that generates variable resolution maps from atomic models, thereby accounting for local resolution variations in cryoEM data to improve model-to-map matching accuracy.
This paper introduces Openfish, an open-source GPU-accelerated decoding library, and Slorado, a fully open-source basecalling framework, to provide a hardware-independent, scalable, and accurate alternative to Oxford Nanopore Technologies' proprietary Dorado software, thereby eliminating hardware lock-in and enhancing accessibility for nanopore sequencing.
scMagnifier is a consensus clustering framework that leverages gene regulatory network-informed in silico perturbations and a novel visualization method to amplify subtle transcriptional differences, thereby enabling the high-resolution identification and spatial mapping of fine-grained cell subtypes in both single-cell and spatial transcriptomics data.
This paper introduces StructureDCA and StructureDCA[RSA], sparse extensions of Direct Coupling Analysis that integrate structural information to significantly improve the prediction of protein mutational landscapes while enhancing computational efficiency and interpretability.
NETSCOPE is an open-source, multi-platform toolbox that utilizes information-theoretic methods, including mutual information and the data processing inequality, to infer network structures and convert them into metric spaces for unified analysis across diverse biological modalities ranging from molecular interactions to brain connectivity.
This paper introduces and evaluates a new iterative inference framework that combines random forests with multivariate Gaussian mixture models to improve the coverage properties of confidence intervals and estimator precision in population genetic simulations compared to non-iterative ABC-RF and sequential neural likelihood methods.