1199 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 employs pseudoperplexity to demonstrate that the protein language model ProtT5 exhibits detectable but limited memorization of its training data, suggesting it primarily generalizes the statistical grammar of proteins rather than simply rote-learning sequences.
This study presents ECMME, a comprehensive web atlas analyzing per-residue selection pressures across 272 human extracellular matrix proteins using 228 mammal species, revealing pervasive purifying selection alongside distinct patterns of episodic positive selection in collagens to provide an open-access resource for investigating ECM evolutionary dynamics.
The authors present FLAG-X, a Python package that bridges the gap between manual and automated flow cytometry analysis by integrating state-of-the-art machine learning methods with expert annotations into hybrid workflows, thereby addressing the lack of standardization and efficiency in routine clinical gating.
The paper introduces cryoPARES, a supervised deep learning method that accelerates Cryo-EM image alignment and enables real-time, automated structural determination for drug discovery by leveraging prior pose information and eliminating manual intervention.
This study addresses the reproducibility challenges of colorimetric Loop-Mediated Isothermal Amplification (LAMP) by developing a machine learning classification model that utilizes thermodynamic and sequence features, particularly from F1c and B1c primers, to predict assay success and improve primer design fidelity.
This study demonstrates that intra-slide calibration technology, when combined with computational analysis, significantly improves the harmonization and reproducibility of p53 immunohistochemical assays across different anatomic pathology laboratories, thereby supporting more objective diagnostic decision-making in neuro-oncology.
DDI_single is a novel single-sequence-based algorithm that leverages the ESM-1b protein language model and a gated cross-attention module to accurately predict inter-domain residue interactions, thereby achieving superior accuracy in assembling multi-domain protein structures compared to existing methods like trRosettaX_single.
This study introduces a deep-learning model that successfully translates individualized brain topography maps into functional connectomes, thereby establishing a direct link between spatial organization and network connectivity to unify diverse rsfMRI research perspectives.
The paper introduces an updated version of the TRACEY resource that significantly improves SNARE protein domain annotation by incorporating a vastly expanded, non-redundant dataset of nearly 19,000 curated sequences to generate enhanced HMM profiles and a redesigned web interface for more accurate detection of divergent and lineage-specific paralogs.
This study demonstrates that acute fasting induces small, systematic shifts in specific epigenetic clocks—particularly PC-based and SystemsAge variants—driven by immune cell redistribution, whereas mortality-trained clocks remain stable, highlighting that clock reliability is context-dependent and requires perturbation-specific assessment beyond standard ICC metrics.