1018 papers
Bioinformatics sits at the exciting intersection where biology meets data science, using powerful computer tools to decode the vast complexity of living systems. From mapping the human genome to tracking how viruses evolve, this field transforms raw biological information into actionable insights that drive modern medicine and research forward without requiring a supercomputer to understand the basics.
On Gist.Science, we ensure you never miss a breakthrough by processing every new preprint in this category directly from bioRxiv. Our team provides both plain-language explanations and detailed technical summaries for each paper, making cutting-edge discoveries accessible to everyone regardless of their background.
Below are the latest bioinformatics papers added from bioRxiv, ready for you to explore with clarity and depth.
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.
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.
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 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 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.