1197 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 paper introduces EvoBind-multimer, a deep learning framework that enables the de novo design of macrocyclic molecular glues from protein sequences alone to induce proximity and drive targeted protein degradation, while also revealing that the functional outcome of these glues can vary context-dependently between different patient-derived models.
The paper introduces EnzymARC, a novel benchmark dataset of structurally disrupted enzyme decoys, to demonstrate that current state-of-the-art enzyme function predictors rely heavily on phylogenetic shortcuts and fail to distinguish catalytically incompetent variants from functional enzymes, thereby highlighting the critical need for structure-aware negative examples in model training and evaluation.
The paper introduces antigen-prime, a forward-time epidemic simulator that links genetic sequences to antigenic phenotypes under host selection to generate ground-truth data for benchmarking influenza variant assignment and growth rate estimation methods, revealing both their accuracy and specific failure modes.
This study reveals that standard degree-based gene prioritization systematically overlooks non-hub connector genes essential for coordinating biological processes, and proposes EDVS, a partition-free, information-theoretic centrality measure that successfully recovers these omitted genes without relying on functional annotations or community detection.
SVlog is a transparent, declarative logic programming framework built on Souffle that enables fast, deterministic, and explainable analysis, annotation, and prioritization of structural variants for clinical disease curation by converting genomic data into relational facts and applying composable logical rules.
This paper proposes a comparative *in silico* framework demonstrating that while plant and human mitochondrial stress signaling networks have diverged in complexity, they share fundamental organizational principles, suggesting that the simpler, resilience-oriented plant systems can serve as a conceptual model to generate testable hypotheses about human mitochondrial disease vulnerability.
The paper introduces arteMIS, a systematic framework that optimizes molecular networking parameters through multi-metric evaluation and Latin Hypercube Sampling to overcome the limitations of default settings, thereby producing more robust, chemically meaningful, and stable networks across diverse datasets and scoring methods.
This paper introduces a new PyMOL plugin that integrates various Circuit Topology methodologies with a user-friendly interface and reproducible environment to analyze the topology of both structured and disordered proteins.
This paper introduces SPIRAL, a sparse autoencoder framework that successfully decodes the hidden states of the BiRNA-BERT RNA language model into interpretable, nucleotide-aligned features representing secondary structure and RNA family types, thereby enhancing downstream prediction performance despite byte-pair tokenization challenges.
This paper introduces scDblFinderPy, a Python implementation of the R-based scDblFinder doublet detection tool that replicates its high performance while adding optional GPU support to accelerate processing for large-scale single-cell sequencing data.