857 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.
This study establishes a standardized, expression-based annotation framework that enables the systematic detection and reproducible quantification of abundant small vault RNAs (svtRNAs) in human cells, revealing their consistent processing and potential miRNA-like regulatory roles across diverse datasets.
The Descriptron-GBIF Annotator is a zero-installation, browser-based crowdsourcing platform that leverages AI-assisted segmentation and standardized ontologies to enable the public to generate structured morphological annotations from GBIF images, thereby addressing the scarcity of annotated biodiversity data through a two-tier architecture that feeds citizen science contributions into expert AI model training.
Cycle-Extractor is a fast and accurate mixed-integer linear programming tool that reconstructs ecDNA structures from both short- and long-read sequencing data, outperforming existing methods in speed and achieving superior resolution of large, high-copy oncogenic circles validated by experimental evidence.
This paper introduces SuperSurv, an open-source R package that provides a unified framework for building, evaluating, and interpreting machine learning ensembles for right-censored survival data by integrating heterogeneous learners, implementing IPCW-weighted stacking, and offering comprehensive tools for hyperparameter tuning, benchmarking, and clinical interpretability.
The paper introduces DNA-MGC+, a versatile codec that significantly outperforms existing solutions by enabling reliable and resource-efficient data retrieval on synthetic DNA across diverse sequencing platforms, even under high error rates and low sequencing depths.
BioPipelines is an open-source Python framework that simplifies the adoption of deep learning tools for protein and ligand design by enabling chemical biologists to define, prototype, and execute complex, multi-step computational workflows with minimal coding effort and without requiring specialized infrastructure.
This study provides biochemical evidence that orally ingested dsRNA in the Colorado potato beetle is processed into functional siRNAs within the central nervous system, confirming systemic RNAi transport and activity in neural tissues.
The paper introduces SpliceSelectNet, a hierarchical Transformer-based deep learning model that achieves state-of-the-art accuracy and biological interpretability in predicting splice sites and detecting aberrant splicing by effectively capturing both local and long-range (up to 100 kb) regulatory dependencies in DNA sequences.
Fleming is an integrative AI agent that combines discriminative and generative models with molecular optimization and ADMET prediction to successfully identify and design novel lead compounds for *Mycobacterium tuberculosis* inhibition with high in vitro hit rates and favorable safety profiles.
FLAMESv2 is a highly modular, protocol-agnostic R/Bioconductor package designed to process and analyze long-read single-cell and spatial RNA-seq data, enabling comprehensive characterization of RNA isoforms, alternative splicing, and cellular heterogeneity across diverse experimental workflows.