1012 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.
PathwaySeeker is an evidence-grounded AI system that integrates proteomic and metabolomic data to reconstruct organism-specific metabolic networks, utilizing an "Oracle-in-the-Loop" inference method to generate and verify condition-specific biological hypotheses with explicit experimental provenance.
This paper introduces UDAL, a budget-constrained active learning strategy that combines uncertainty estimation and diversity selection to significantly reduce the cost and time of wet-lab TCR--pMHC validation while achieving predictive performance comparable to models trained on much larger random datasets.
The paper introduces FairTCR, a group distributionally robust optimization framework that significantly reduces performance disparities across HLA alleles and ancestry cohorts in TCR--pMHC binding prediction while maintaining competitive overall accuracy.
This study demonstrates that applying machine learning techniques to impute missing entomological data significantly enhances the accuracy of predictive models for *Plasmodium vivax* malaria incidence in Bolivar State, Venezuela, despite failing to improve predictions for *Plasmodium falciparum*.
The paper introduces ORION, an agentic AI framework that automates the end-to-end analysis of complex immune profiling data by integrating statistical modeling and literature review, successfully compressing months of manual interpretation into hours while generating reproducible, biologically coherent hypotheses for both known and novel autoimmune conditions.
This paper presents a generative framework using conditioned protein language models to design intrinsically disordered proteins with specific conformational ensembles, demonstrating that accurate control of their properties is achievable only through large-scale training data.
The paper introduces vcfilt, a zero-allocation, streaming VCF filter implemented in Go that achieves up to a 12.2x speedup over existing tools like bcftools by specializing in high-frequency filtering criteria and eliminating dynamic overhead while maintaining byte-for-byte output compatibility.
The paper introduces ABB4-STEROIDS, a generative deep learning model trained on millions of molecular dynamics frames that achieves state-of-the-art accuracy in sampling and predicting the conformational ensembles of antibodies, offering a robust resource for investigating antibody flexibility and function.
This study utilizes computational bioinformatics and comparative modeling to demonstrate that temperature is the primary driver of EndoG protein diversification in *Xenopus* frogs, with *X. laevis* (cold-adapted) and *X. tropicalis* (heat-adapted) exhibiting distinct structural and physicochemical adaptations to their respective thermal environments.
ProteomeScan is an open-source, cloud-scale computational toolkit that performs proteome-wide molecular docking to systematically identify and validate protein-ligand interactions, demonstrating superior accuracy in ranking known targets compared to random baselines while enabling the discovery of hidden binding sites.