857 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.
The paper introduces SC-BIG, a hierarchical Bayesian model that leverages bulk sequencing data to jointly estimate cancer cell fractions and improve the accuracy and uncertainty quantification of somatic SNV detection in single cells, outperforming existing methods in the presence of copy-number alterations and clonal admixtures.
This paper presents a comprehensive benchmark of 26 spatial domain detection methods across diverse real and semi-synthetic datasets, revealing that performance is primarily driven by data resolution and cellular heterogeneity rather than architectural novelty, and introduces a modular framework to guide future tool development and selection.
This paper introduces ITEC, a fully unsupervised method that achieves high-fidelity, long-term reconstruction of complete cell lineages and fate maps across multiple species by iteratively tracking and correcting errors in terabyte-scale embryonic imaging data.
This paper introduces scPloidyR, a hidden Markov model that jointly analyzes sequencing read depth and B-allele frequency from targeted single-cell DNA panels to significantly improve the accuracy of copy number variation detection compared to depth-only methods, provided that allelic information is available.
This paper introduces a simulation framework demonstrating that hierarchical reinforcement learning effectively optimizes antibiotic prescribing policies under antimicrobial resistance dynamics by leveraging temporal abstraction and risk stratification to outperform fixed rules and flat RL approaches in complex, partially observable environments.
This paper demonstrates that applying conformal prediction to LC-MS/MS molecular retrieval generates spectrum-specific candidate sets with guaranteed coverage probabilities, effectively balancing reliability and efficiency across both in-distribution and shifted data scenarios.
This study pioneers the integration of replication-timing proxies derived from methylation data and morphological embeddings into personalized LIONESS gene networks, demonstrating that these epigenetic and structural features significantly enhance the robustness and classification accuracy of basal-like versus classical subtypes in pancreatic adenocarcinoma.
The Grass Expression Atlas (GExA) is a freely available, interactive web resource that integrates and uniformly processes thousands of RNA-seq datasets from four millet species, along with barley and sorghum, to facilitate rapid exploration of gene expression across diverse tissues, developmental stages, and conditions.
Stoic is a fast and accurate method that leverages protein language model embeddings and graph neural networks to predict the stoichiometry of protein complexes by identifying interface residues, thereby overcoming the computational limitations of current brute-force approaches.
PepCABO is a novel latent-space Bayesian optimization framework that leverages a dual variational autoencoder with contrastive alignment to efficiently discover high-affinity peptide-MHC binders by enabling structured knowledge transfer across alleles and improving sample efficiency in both low- and high-budget experimental settings.