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.
The paper introduces ML4SD, an active-learning Design-Build-Test-Learn cycle that integrates machine learning with a novel high-throughput algorithm (gcSwarms) to efficiently identify growth-coupled microbial strain designs, achieving significantly higher carbon yields with far fewer experimental iterations than traditional search methods.
This study demonstrates that while Wasserstein GANs outperformed Variational Autoencoders in generating experimentally viable de novo antibodies (99% vs. 5% success), this disparity was primarily driven by the sampling strategy—unconditional generation versus latent seeding with known antibodies—rather than inherent architectural superiority.
This paper introduces an uncertainty-aware model selection rule for stochastic gene-expression models that enhances the conventional PGF-BIC by incorporating a data-driven threshold, derived via influence functions and Cantelli's inequality, to account for sampling uncertainty and prevent the over-selection of complex models without sacrificing computational efficiency.
By employing SHAP-based interpretability on paired oral and gut microbiome data, this study reveals that while combined models do not surpass the perfect predictive accuracy of gut data alone for distinguishing newborns from adults, they uncover complementary, non-redundant biological signatures from the oral cavity that would otherwise be missed by conventional accuracy metrics.
This paper introduces the Hierarchical Temporal Transformer (HTT), a two-level architecture that leverages longitudinal pathology reports and cancer-type embeddings to achieve state-of-the-art cancer grade prediction and demonstrate effective zero-shot transfer learning to unseen cancer types without performance penalties.
The paper introduces POME, a self-supervised graph-based embedding model designed to generate low-dimensional representations for partially observed mixed-type biomedical data, achieving state-of-the-art imputation performance and enabling effective downstream tasks such as patient subgroup discovery, predictive modeling, and therapy recommendation.
The paper introduces LAMINA, an interpretable deep learning model that accurately predicts peptide-HLA binding by aggregating soft motif interactions, achieving state-of-the-art performance while emergently learning binding energetics and structural features from sequence data alone.
The paper introduces pydreg, a fast and maintainable Python reimplementation of the dREG algorithm for identifying active cis-regulatory elements from nascent transcription, which significantly reduces runtime and memory usage while preserving the original method's accuracy and compatibility with modern computing infrastructure.
By comparing matched full-UDG and non-UDG ancient DNA libraries from medieval Mongolian individuals, this study demonstrates that while various computational damage correction methods (trimming, rescaling, and masking) differentially balance site retention against error reduction, the optimal choice depends on the specific downstream analysis, with corrected data proving essential for accurate kinship inference in non-UDG libraries.
This paper presents a scalable framework integrated into the DMRcaller package that leverages native Oxford Nanopore sequencing to quantify single-molecule DNA methylation heterogeneity and detect co-methylated loci, revealing epigenetic patterns and regulatory interactions obscured by traditional site-level average analyses.