949 papers
The paper introduces scLongTree, a scalable and accurate computational tool designed to infer subclonal evolutionary trees from longitudinal single-cell DNA sequencing data, demonstrating superior performance over existing methods on both simulated and real-world cancer datasets.
The paper introduces **rnaends**, an R package designed to facilitate the analysis of exact RNA ends at nucleotide resolution by providing a comprehensive workflow for processing, quantifying, and interpreting RNA-end sequencing data to study diverse aspects of RNA metabolism such as transcription start sites, degradation dynamics, and post-transcriptional modifications.
This paper introduces Coherent Denoising, a novel ensemble-based diffusion framework that synthesizes missing biomedical modalities from available data to overcome dataset sparsity, thereby enabling high-fidelity multimodal integration, robust predictive modeling, and counterfactual analysis for precision oncology using a large-scale TCGA cohort.
COMPASS is a deterministic, ontology-free web-based framework that converts gene expression into stable, interpretable pathway activity scores without requiring permutation or reference cohorts, offering improved robustness and discrimination compared to existing single-sample enrichment methods for precision medicine applications.
AEGIS is an open-source, user-friendly Python toolkit that standardizes inconsistent genome annotation files, extracts specific genomic features, and facilitates robust comparative genomics by integrating multiple evidence lines to resolve gene ID correspondences and structural variations.
This study identifies and characterizes orphan genes in *Meloidogyne* root-knot nematodes, revealing that they constitute 18% of the genome, originate through both high divergence and *de novo* birth, and possess distinct molecular features suggesting a critical role in the nematodes' global parasitism.
The paper proposes DyGraphTrans, a memory-efficient and interpretable dynamic graph representation learning framework that models patient Electronic Health Records as temporal graphs to effectively predict disease progression and mortality while capturing both local temporal dependencies and global trends.
PRIZM is a data-efficient, two-phase workflow that leverages small experimental datasets to select optimal zero-shot foundation models for ranking and prioritizing protein variants, successfully identifying improved thermostability and activity in enzyme engineering case studies.
This paper presents a unified, high-quality spatial transcriptomic dataset generated via the Stereo-seq platform across ten mouse organs, providing matched histological images, cell-type annotations, and multi-resolution expression matrices to serve as a standardized resource for developing and benchmarking deep learning models in spatial biology.
The paper introduces scMultiPreDICT, a computational framework that systematically benchmarks single-cell transcriptomic and epigenetic features to reveal that while RNA-derived data generally offers superior predictive power for gene expression, the added value of multimodal integration is gene-specific and context-dependent.
This paper introduces LYM-1, the first large-scale, multi-crop model trained on over 4.7 million observations to predict and optimize crop yields across the Canadian prairies by analyzing complex interactions between genetics, environment, and management.
This study introduces new nearest neighbor parameters derived from 208 optical melting experiments to accurately model the stabilizing effects of 1-methyl-pseudouridine on RNA folding, which are now integrated into the RNAstructure software to improve the prediction of secondary structures for both natural and therapeutic mRNA sequences.
The paper introduces IDiom, an autoregressive protein language model trained on millions of intrinsically disordered region sequences that enables the generative design of diverse, biologically relevant disordered proteins and regions conditioned on structural context or subcellular localization.
This paper proposes a novel graph-based deep learning model featuring a Connectivity Attention Block and parallel processing branches to effectively predict age, MMSE scores, and sex from structural brain connectivity graphs, demonstrating superior performance over existing machine learning methods on the PREVENT-AD and OASIS3 datasets.
The paper introduces SimpleFold-Turbo, an open-source method that adapts TeaCache to achieve up to 14-fold inference acceleration in flow-matching protein structure prediction by skipping approximately 93% of redundant neural network evaluations without retraining or significant quality loss.
This study demonstrates that while irreducible batch effects and outlier removal can introduce errors, preprocessing steps like regularization to comprehensive databases do not significantly alter unsupervised classification patterns, suggesting that non-repeatability in high-content assays is an uncorrectable feature that does not necessarily compromise classification outcomes.
To address the fragmentation of tuberculosis research, the authors present MTB-KB, a curated knowledgebase that systematically integrates over 75,000 associations from 1,246 publications into an interactive platform to support global TB research, clinical practice, and elimination efforts.
This study presents a robust, multi-criteria computational framework integrating meta-ensemble QSAR, structure-based docking, and molecular dynamics to identify and validate novel BACE1 inhibitors, ultimately prioritizing Mol-2 as a promising lead for Alzheimer's disease therapy with confirmed stability and favorable CNS drug-like properties.
This paper introduces TCMCard, a high-confidence digital infrastructure that utilizes a Multi-Dimensional Evidence Integration framework to filter low-quality data and provide a reliable, interactive platform for analyzing the synergistic mechanisms of Traditional Chinese Medicine.
This study addresses the lack of curated trnL reference databases by systematically comparing OBITools3/ecoPCR, RESCRIPt, and MetaCurator to generate and evaluate high-quality plant DNA metabarcoding resources, demonstrating that the optimal curation tool varies depending on the specific trnL region analyzed.