974 papers
This paper introduces FuzzyClusTeR, a web server designed to identify, visualize, and analyze both classical tandem and "diffuse" (fuzzy) DNA repeat clusters in genomic sequences, demonstrating its utility by revealing non-random, potentially functional telomeric-like repeat patterns in the human T2T-CHM13 genome.
The paper introduces GraPhens, a simulation framework that generates realistic synthetic phenotype-gene pairs using Human Phenotype Ontology structure and empirical priors to train GenPhenia, a graph neural network that achieves state-of-the-art performance in gene prioritization for rare diseases despite being trained entirely on synthetic data.
The paper introduces REGEN, a Graph Neural Network framework that simultaneously reconstructs latent gene interaction networks and predicts patient outcomes from bulk transcriptomic data, demonstrating superior performance across multiple cancer types and providing practical guidelines for applying GNNs to gene network discovery.
EvoMut is a computational framework that integrates structural, chemical, functional, and evolutionary data to distinguish between oxidation-prone residues that are suitable for engineering and those that are functionally constrained, thereby enabling the rational design of oxidation-resistant proteins.
This study introduces a harmonized, implementation-aware benchmarking framework that evaluates 46 polygenic risk score tools across diverse phenotypes and model configurations, revealing that no single method is universally optimal and that performance is significantly influenced by a combination of statistical, computational, and practical implementation factors.
The paper introduces Helicase, a Rust library that leverages SIMD vectorization and finite state machines to deliver the fastest known general-purpose FASTA/Q parsing and bitpacking performance, significantly outperforming existing state-of-the-art tools across diverse CPU architectures.
The paper introduces aaKomp, a scalable, alignment-free tool that utilizes amino acid k-mer matching and multi-index Bloom filters to assess genome completeness with significantly faster execution, lower memory consumption, and greater flexibility than existing methods, making it ideal for large-scale and diverse genomic projects.
This paper introduces ATHILAfinder, a specialized computational pipeline that outperforms existing tools in accurately identifying and annotating ATHILA LTR retrotransposons across diverse plant genomes, thereby enabling high-resolution analysis of their evolutionary dynamics.
The paper introduces miRBind2, a parameter-efficient deep learning model that predicts miRNA target sites and gene-level repression using sequence-only data, outperforming existing state-of-the-art methods without relying on engineered biological features.
This study presents a novel 4D tensor decomposition approach that successfully integrates transcriptome, translatome, and proteome data to identify functionally relevant gene clusters and distinct regulatory mechanisms, such as ribosome stacking and translational buffering, underlying cell fate transitions during amino acid starvation.
By applying long-read metagenomic sequencing to urban soil samples from two major Chinese cities, this study reconstructed thousands of novel microbial species and uncovered extensive functional diversity, including thousands of biosynthetic gene clusters and millions of small protein families, thereby significantly expanding our understanding of urban soil microbiomes and their implications for public health.
This study demonstrates that integrating codon-level constraints with protein-intrinsic features reveals that variant pathogenicity is driven by both the resulting protein product and the translation process, with the relative importance of these factors varying by variant type and experimental context.
This study presents a robust computational workflow that integrates diverse tuberculosis transcriptomic signatures with multiple connectivity scoring methods to systematically identify 64 FDA-approved drugs as promising host-directed therapeutics and 12 novel bridging genes for future experimental validation.
This paper introduces SVPG, a novel approach that leverages haplotype-resolved pangenome references to achieve superior structural variant detection accuracy and accelerate pangenome graph augmentation by nearly 10-fold compared to existing methods.
PyrMol is a knowledge-structured pyramid graph framework that mimics expert chemists' hierarchical cognition by integrating functional groups, pharmacophores, and retrosynthetic fragments across multiple scales to significantly improve the generalizability and accuracy of molecular property prediction in AI-aided drug discovery.
This paper introduces Dingent, a novel and easily deployable framework that bridges the gap in existing solutions by providing a configurable, web-based agent system for natural language data retrieval and integration across diverse sources, demonstrating its versatility through successful applications in multiple scenarios and potential for fields like earth sciences.
RNAGAN is a multipurpose AI tool based on a generative adversarial network that leverages large-scale human transcriptomic data to enable patient stratification, marker analysis, pseudo-data generation, and feature vectorization with enhanced interpretability and small-sample capability through a single training procedure.
TriGraphQA is a novel triple graph learning framework that improves protein complex quality assessment by explicitly decoupling intra-chain folding and inter-chain binding through three geometric views and an interface context aggregation module, thereby outperforming state-of-the-art methods in identifying near-native assemblies.
GenBio-PathFM is a state-of-the-art, 1.1B-parameter open-weight foundation model for histopathology that achieves superior performance on public benchmarks using exclusively public data, driven by an automated diversity-focused curation pipeline and a novel dual-stage JEDI learning strategy.
This paper introduces a novel Pareto optimization method for masked superstrings that simultaneously balances superstring length and mask complexity, achieving 12–19% better compression of pan-genome k-mer sets compared to existing approaches when combined with neural-network compressors.