1014 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 demonstrates that a hybrid Random Forest-SVM framework utilizing TF-IDF-based k-mer features outperforms deep learning methods in classifying imbalanced SARS-CoV-2 genomic data, achieving superior macro-averaged F1-scores and robust detection of rare variants.
This study systematically benchmarks 86 SNP heritability estimation configurations across six tool families and ten method groups, revealing that while heritability estimates vary substantially based on algorithmic choices and standardization, this upstream variability has minimal impact on downstream polygenic risk score performance, suggesting heritability should be treated as a configuration-sensitive parameter rather than a stable scalar.
This study introduces a novel mathematical model that integrates intensity-dose susceptibility bioassay data to account for resistance heterogeneity in mosquito populations, enabling more accurate predictions of insecticide-treated net effectiveness and the public health impact of insecticide resistance across Africa.
This paper presents a highly efficient convolutional neural network (CNN) model that utilizes TFxIDF vectorization of amino acid sequences derived from Human Genome Build 38 to accurately predict gene locations, achieving state-of-the-art performance in identifying genes associated with genetic disorders.
This study rigorously benchmarks seven state-of-the-art computational methods for removing ambient RNA from single-cell and single-nucleus RNA sequencing data, revealing that while no single method excels across all scenarios, CellBender, DecontX, and SoupX generally demonstrate superior performance.
This paper introduces the DGIdb Model Context Protocol (MCP) server, which enables large language models to access up-to-date drug-gene interaction data from the DGIdb API via natural language, thereby significantly enhancing their ability to provide accurate biomedical answers.
This study introduces Adaptive Cluster-Count Autoencoders with Dirichlet Process Priors, which significantly enhance the geometric compactness and separation of single-cell latent spaces at a modest cost to label-recovery accuracy, thereby establishing a task-dependent trade-off where nonparametric priors are optimal for trajectory analysis and manifold visualization rather than strict cluster counting.
The paper introduces Topic-FM, a novel family of neural topic VAEs that enforces simplex constraints via a logistic-normal Dirichlet prior and employs a conditional optimal-transport flow to simultaneously enhance clustering performance, supervised discrimination, and biological interpretability of gene programs across diverse single-cell RNA sequencing datasets.
This study identifies distinct serum metabolic signatures, including specific acylcarnitines, diet-derived compounds like piperine and lutein, and altered drug metabolism, that serve as molecular predictors of exceptional cognitive resilience in a longitudinal aging cohort.
This study demonstrates that integrating subcellular localization with RNA expression data significantly improves the prediction of protein detectability across human cancers, revealing that widespread RNA-protein discordance is driven by structured biological processes rather than stochastic variation.