856 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 proposes a tensor-derived similarity network framework that decomposes colorectal cancer spatial transcriptomics data to reveal how tissue architecture constrains molecular connectivity, offering a quantitative approach to characterizing spatial heterogeneity and discovering biomarkers.
This study presents a network-based framework that integrates heterogeneous proteomic datasets by analyzing the concordance of differential protein fold-change directions, successfully identifying biologically meaningful clusters such as breast cancer profiles associated with tumor stage and lipid-related pathways.
BioEngine is a scalable execution and adaptation layer that bridges curated bioimage AI models with diverse hardware, enabling scientists to run, fine-tune, and deploy complex analysis workflows through natural language interactions with an AI agent.
This paper demonstrates that an automated agent-guided policy search outperforms traditional weighted-sum, NSGA-II, and random-weight approaches in prioritizing peptide drug candidates, capturing 65% of the best options in its top-20 shortlist across a public antimicrobial benchmark.
The paper introduces RepurAgent, a human-supervised hierarchical multi-agent AI system that successfully automates and supports the entire drug repurposing lifecycle—from hypothesis generation and experimental design to data analysis and candidate refinement—across diverse disease scenarios including Acute Myeloid Leukemia, COVID-19, and Multiple Sulfatase Deficiency.
The paper introduces Surleton, a novel inverse protein folding framework that jointly models backbone geometry and protein surface organization to overcome the limitations of backbone-only representations, thereby significantly improving sequence recovery and modeling balance for both buried and exposed residues.
By employing a phylogenetic comparison of proteomes between cyst-forming Sarcocystidae and non-cyst-forming Eimeriidae parasites, this study identifies distinct conservation patterns of *Toxoplasma gondii* proteins and pinpoints a specific cluster of bradyzoite-conserved proteins as a promising source for discovering novel genes involved in bradyzoite differentiation.
This paper introduces HMCVelo, a deterministic ODE-based framework that leverages Joint-snhmC-seq data to model hydroxymethylation dynamics and infer high-confidence cellular trajectories, demonstrating superior performance over repurposed RNA velocity methods and providing theoretical insights into trajectory resolution within cyclic biochemical systems.
This paper presents a closed-loop in silico framework that integrates generative modeling, molecular simulations, and multi-objective Bayesian optimization to successfully design cell-penetrating peptides with receptor-selective binding profiles, as validated by experimental imaging showing preferential enrichment in CXCR4-positive regions over NRP1-positive regions.
ProNA3D is a unified computational tool and UCSF ChimeraX plug-in designed to analyze protein-nucleic acid and nucleic acid-only interfaces by integrating structural data, AlphaFold3 scoring, and interactive visualization to reveal functional connectivity and interaction modes in both experimental and predicted biomolecular complexes.