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.
labapi is a Python library that bridges computational workflows and the LabArchives electronic lab notebook by translating its flat API into an intuitive object model, thereby automating the seamless reading and writing of experimental data to eliminate manual uploads.
PharmCast is a feedforward neural network that rapidly and accurately predicts 3D pharmacophore fingerprints directly from 2D SMILES strings without conformer generation, enabling efficient virtual screening and scaffold hopping at a fraction of the computational cost of traditional methods.
This paper identifies and addresses the critical issue of systematic prediction bias in machine learning-based aging clocks, which can distort downstream association analyses, by proposing a constrained optimization framework to ensure valid biological age estimation and inference.
RevPert is a gallery-native reverse perturbation model that prioritizes candidate genetic drivers of transcriptomic state transitions by combining signed Pearson connectivity with a learned residual, demonstrating superior performance in recovering held-out interventions and identifying disease-relevant anchors compared to existing baselines.
This paper demonstrates that a simple, sequence-only pipeline combining 330 interpretable descriptors with the TabPFN foundation model outperforms complex, structure-conditioned deep learning approaches in multi-label antimicrobial peptide profiling on the ESCAPE benchmark, achieving state-of-the-art accuracy without the need for gradient-based training or structural data.
This study demonstrates that covariance non-stationarity is a prevalent and biologically significant feature in spatial transcriptomics that violates common Gaussian process assumptions, thereby necessitating a re-evaluation of how spatially varying genes are characterized and categorized.
The paper introduces Kompot, a cluster-free statistical framework that enables single-cell resolution comparative analysis of multi-condition data by modeling cell density and gene expression as continuous functions to detect both differential abundance and localized, heterogeneous transcriptional changes without relying on predefined cell types.
This study utilizes a multilayered bioinformatics approach to characterize the robust, sex-specific transcriptional signature of human skeletal muscle in response to repeated shivering, revealing how these molecular adaptations diminish with cold acclimation and offering new mechanistic insights into improved metabolic health.
This study integrates experimental biophysical analyses and computational simulations to characterize the pH-dependent structural dynamics and conformational plasticity of the Dengue virus NS2B-NS3 protease, providing critical insights for the rational design of pH-specific antiviral inhibitors.
CoTRA is an open-source, modular R/Shiny framework that unifies transparent and reproducible workflows for both bulk and single-cell RNA-seq analysis, offering extensive functionality, cross-platform compatibility, and validated performance comparable to or exceeding existing tools.