1200 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 presents and validates a simple, fast, and adaptable cluster-based event detection (CBED) algorithm that outperforms existing schemes in efficiency and noise reduction for biological nanopore data while highlighting the necessity of adaptive baseline correction for solid-state nanopore data.
This study introduces TmProt 1.0, a fine-tuned ESM-2 embedding model that outperforms existing state-of-the-art predictors in identifying thermostable proteins across heterogeneous biophysical datasets, addressing the critical challenge of cross-domain generalization in protein melting temperature prediction.
This paper introduces a diffusion-based generative model with sparsity-preserving mechanisms that achieves parametric-level sparsity preservation and competitive ecological distance metrics for human microbiome data, representing the first deep learning approach to match such sparsity fidelity while remaining competitive on standard ecological benchmarks.
The paper introduces conMItion, an R package that utilizes conditional mutual information to robustly adjust for confounding factors like tumor purity and mutation burden in multi-omics association analyses, thereby improving the accuracy of identifying cancer-related genes and cellular interactions.
The paper introduces DipGenie, a scalable tool that jointly optimizes genotyping and phasing on pangenome graphs using a biologically motivated recombination budget, achieving significantly lower switch error rates and higher structural variant F1-scores compared to existing graph-based methods.
WSInsight is an open, cloud-native platform that enables scalable, agent-callable single-cell phenotyping of whole-slide H&E images from diverse storage sources, delivering validated, standards-compliant outputs for translational tumor microenvironment research.
LIVIA is a freely available, browser-based tool that locally computes local confidence metrics, identifies interface residues, and visualizes predicted protein-protein interactions across multiple platforms using an interactive 3D viewer and exportable scripts for ChimeraX and PyMOL.
This study develops a machine learning framework to impute cross-platform proteomic data between SomaScan and Olink, thereby resolving persistent non-replication issues, enabling the recovery of platform-exclusive signals, and establishing a protein fidelity index to enhance the reliability of epidemiological biomarker discovery.
This study utilizes a cross-dataset integrative transcriptomic analysis to identify oxidative stress and inflammation-related hub genes in Parkinson's disease and reveals how specific bioactive food compounds may modulate these gene networks through nutrigenomic interventions.
This paper introduces **bcrnnoise**, an open-source Python framework that unifies deterministic drift, Gaussian fluctuations, and additive sporadic jumps into a single stochastic differential equation model to efficiently generate accurate transcription traces with up to two orders of magnitude faster computational speed than exact stochastic sampling.