1200 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 demonstrates that combining reduced amino acid alphabets with Byte Pair Encoding tokenization significantly enhances the computational efficiency of protein language models by shortening input sequences and accelerating training, while maintaining or even improving predictive performance across diverse downstream tasks.
This study introduces Cyclome, a comprehensive computational framework that unifies a curated dataset of 930 cyclic peptides with novel topology-aware algorithms and machine learning models to predict thermal stability and identify critical metal-binding capabilities, thereby advancing the design of stable cyclic peptide therapeutics and tools for mineral recovery.
Pipette is a multi-agent AI framework that leverages a literature-derived Skill Graph to guide natural language-driven generation of biologically valid, reproducible, and end-to-end bioinformatics workflows, effectively lowering the computational expertise barrier for genomic data analysis.
The paper introduces VASCIF, a structure-aware framework utilizing Masked Graph Attention and adaptive transfer learning to achieve state-of-the-art, interpretable, and efficient prediction of antibody-antigen structural interfaces, thereby overcoming challenges related to data scarcity and computational cost in antibody discovery.
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.
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.
The paper introduces FM-GPT, a novel Bayesian fine-mapping method that leverages gene-guided dimension reduction to accurately prioritize causal genes across multiple correlated phenotypes with mixed outcome types, successfully uncovering shared biological mechanisms and pleiotropic effects in large-scale phenome-wide studies using UK Biobank data.
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 evaluates the performance of six open-source somatic variant callers on high-coverage tumour-only amplicon sequencing data using a commercial reference standard, identifying FreeBayes, VarScan, MuTect2, and Pisces as the top-performing tools despite FreeBayes' tendency to generate more artifacts.