1005 papers
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.