1012 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.
DeSCOPE is a lightweight conditional variational autoencoder framework that outperforms existing baselines in predicting single-cell responses to genetic perturbations across unseen genes, cell types, and combinatorial multi-gene scenarios, serving as a versatile multi-modal virtual cell model for therapeutic target design.
The paper introduces spatialFDA, an open-source Bioconductor R package that integrates spatial statistics and functional data analysis to accurately quantify and test for differences in cellular co-localization across multiple conditions in spatial omics data, demonstrating its effectiveness through simulations and biological applications in type-1 diabetes.
This study presents an end-to-end deep learning framework that fuses fine-tuned protein language models with AlphaFold-derived 3D structural graphs to accurately predict antibody self-association (measured by CSI-BLI), demonstrating that integrating sequence and spatial context significantly outperforms sequence-only baselines and provides interpretable insights into key developability drivers like charge and hydrophobicity.
This paper introduces deepHTL, a deep semiparametric regression framework that effectively tests for and estimates causal treatment effect heterogeneity in observational studies, demonstrating its superiority over existing methods in simulations and revealing that younger, lower-risk lung transplant recipients derive significantly greater functional benefits from bilateral compared to single lung transplantation.
This study validates the ActTrust(R) accelerometer as a simple, cost-effective tool for estimating energy expenditure and classifying physical activity intensity in young adults by demonstrating its strong correlation with treadmill speed and metabolic equivalents, as well as its high accuracy in predicting activity levels comparable to the widely used ActiGraph(R) GT3X+.
This paper introduces a framework that validates and aggregates the distinct outputs of multiple Active Module Identification algorithms using spectral clustering and Greedy Conductance-based Merging to overcome the limitations of single-algorithm approaches and reveal cohesive biological modules, including hidden genes, in genetic interaction networks.
The paper introduces GRASP, a parameter-efficient framework that utilizes gene- and relation-specific soft prompts to enhance the scalability and generalizability of large language models for inferring diverse gene network interactions.
This study identifies ABCA3 and SCN4B as exercise-related biomarkers for Stanford type A aortic dissection, demonstrating their diagnostic potential through a nomogram while elucidating their involvement in circadian rhythm and immune regulation pathways and suggesting zonisamide and MRS1097 as possible therapeutic agents.
This study identifies five novel ZINC compounds (Z1–Z5) as potential antivirulence inhibitors against the *M. tuberculosis* EccA1 enzyme through virtual screening, docking, and molecular dynamics simulations, demonstrating their superior binding affinity compared to known inhibitors and favorable drug-like properties.
MAJEC is a unified, fast Expectation-Maximization framework that accurately quantifies genes, isoforms, and individual transposable element loci from RNA-seq data by leveraging splice junction evidence to resolve read overlaps, thereby significantly reducing false attribution artifacts compared to existing tools like TEtranscripts and Telescope.