1180 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 TCRmeta, an antigen-aware framework that establishes a shared analytical space for T cell receptor repertoires to overcome individual and technical variability, enabling the identification of conserved immune patterns and the development of robust metrics like Energy Shift for quantitative cross-individual and longitudinal immune monitoring.
The paper introduces SCOTE, a scalable computational framework that unifies spatial omics analysis across diverse platforms and resolutions by quantifying spatial correlations at all length scales, thereby distinguishing biological signals from platform-specific artifacts and enabling efficient, high-throughput processing of massive datasets.
HighFold-Lasso is a novel computational framework that integrates custom covalent connectivity with scaffold-guided adaptive initialization to achieve 100% success in predicting the complex mechanically interlocked structures of all four lasso peptide classes, significantly outperforming general-purpose models in accuracy and reliability.
This paper introduces the Cancer Tree Biopsy Framework (CTBF), a method for reconstructing cancer phylogenies from longitudinal single-cell copy-number profiles that explicitly accounts for temporal sampling order and distinguishes between observed profiles and their ancestral states, demonstrating superior accuracy and genome-scale feasibility compared to existing pooled or topology-only approaches.
This paper introduces HiRepEat, a fast and memory-efficient computational framework that combines the TACO repeat compressor and SALSA coordinate mapping system with an adapted wavefront alignment algorithm to precisely identify, cluster, and analyze LTR-retrotransposon families across diverse vertebrate and Drosophila genomes.
This paper introduces the IMEx binding domain dataset, a large-scale, manually curated collection of experimentally derived protein interaction regions mapped to UniProtKB sequences, which serves as a gold standard for benchmarking interface prediction methods and elucidating the mechanistic basis of disease-associated mutations.
This paper introduces PatientPheX, a novel full-text biomedical corpus comprising 200 case reports with detailed annotations for patient-specific phenotype entities and their associations, designed to advance rare disease research and support the development of patient-centered information extraction methods.
This study demonstrates that in community-dwelling Japanese adults, habitual plant-food-related dietary information is not captured by a single biological state but is instead represented as traceable dimensions distributed across complementary phenotypic layers, with serum Raman spectra providing unique predictive insights into specific dietary variables like beta-carotene that complement clinical and metabolomic data.
By evaluating 2,234 harmonization approaches across 87 orthogonal metrics on a multi-platform B-cell lymphoma dataset, this study demonstrates that the optimal method is highly contingent on the chosen metric class, with ComBat emerging as superior specifically for cross-batch prediction quality while other methods fail to preserve biological integrity.
The paper introduces NoiTox, a multi-model machine learning strategy that integrates physicochemical descriptors, protein language model embeddings, and graph neural networks to predict peptide toxicity with state-of-the-art performance while revealing and analyzing label noise in benchmark datasets.