856 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.
GraphPop is a graph-native engine that decouples population genomics complexity from sample count by pre-aggregating allele counts into a graph database, enabling O(V x K) analysis with massive speedups and constant memory usage while facilitating complex, annotation-conditioned queries across diverse species.
This study presents TB-Bench, a systematic benchmark evaluating 20 machine learning and deep learning models across 14 second-line tuberculosis drugs on a large WHO dataset, revealing that traditional machine learning methods often outperform deep learning in internal tests while both struggle with cross-dataset generalization compared to catalogue-based approaches.
The paper introduces IMAS, a target-aware integrative framework that leverages pan-cancer single-cell resources to augment sparse tumour multiomic data, reconstruct structured regulatory networks, and prioritize communication-guided mechanisms for interpretable discovery in data-limited cancer systems.
To address interoperability challenges caused by proprietary dPCR software, this paper introduces the Digital PCR Data Essentials Standard (DDES), a community-developed, cross-platform file format designed to harmonize data structures and enable FAIR, reproducible, and collaborative clinical and research applications.
DyME is a distributed platform that integrates high-throughput mutagenesis, automated molecular dynamics simulations, and comprehensive comparative analysis tools to systematically investigate and engineer protein recognition mimicry across thousands of molecular systems.
The paper introduces MIMIQ, an adaptive binning method that enables fast and accurate mutual information calculation and significance testing for single-cell RNA sequencing data, demonstrated through its application to studying gene rewiring in CD4+ naive T-cells during SARS-CoV-2 infection.
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.