💻 Category

bioinformatics

1197 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.

💻 bioinformatics

Interrogating contrastive learning embeddings for structure-based virtual screening: a case study on DrugCLIP

This paper systematically evaluates DrugCLIP's contrastive learning embeddings for structure-based virtual screening, revealing that while its pocket embeddings offer fast and robust structural similarity search and its ligand embeddings capture pocket-aware chemical patterns with strong generalization to unseen targets, performance is significantly limited by conformational variations and inaccuracies in predicted protein pockets.

Sanchez Utges, J., Jones, D. T., Orengo, C.2026-09-18
💻 bioinformatics

Targeted ortholog search in unannotated genome assemblies with fDOG-Assembly

The paper introduces fDOG-Assembly, a novel tool that enables targeted, feature architecture-aware ortholog searches directly in unannotated genome assemblies, thereby overcoming the limitations of traditional methods that rely on pre-annotated proteomes and revealing new biological insights such as widespread β\beta-lactam biosynthesis potential in springtails.

Muelbaier, H., Arthen, F., Tran, V., Schaefer, I., Balint, M., Ebersberger, I.2026-09-16
💻 bioinformatics

Structured cross-omics interaction discovery with a triple-graph model

The paper introduces TriGer, a triple-graph framework that overcomes the limitations of fragmented pairwise associations in multi-omics data by identifying interpretable, coordinated many-to-many modules across molecular layers, demonstrating its effectiveness in recovering structured signals in simulations and revealing disease-specific biological patterns in inflammatory bowel disease and colorectal cancer.

YU, J., Lin, H., Chen, S.2026-09-16
💻 bioinformatics

Flow Orchestrated Regulatory Genomics Engine (FORGE): A Configurable Nextflow Pipeline for End-to-End snMultiome Analysis

FORGE is a configurable, containerized Nextflow pipeline that automates end-to-end single-nucleus multiome analysis by integrating gene expression and chromatin accessibility data for regulatory network inference and differential testing, thereby enhancing scalability, reproducibility, and biological interpretation across diverse datasets.

Solano, L. E., Rahimzadeh, N., Shi, Z., Swarup, V.2026-09-16
💻 bioinformatics

ML4SD: Leveraging Machine Learning and High-Throughput Search Algorithms for an Iterative Growth-Coupled Design Innovation

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.

Gargantilla Becerra, A., Nogales Enrique, J.2026-09-16
💻 bioinformatics

A Turing-Style Test for In-Silico Antibodies: How Sampling Mode Makes WGAN-GP Beat VAE in the Wet Lab

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.

Kummer, A., Mahmoudinobar, F., Liu, W., Davis, J. W., Ma, E. J., Kumar, S.2026-09-16
💻 bioinformatics

Uncertainty-Aware Model Selection with a Calibrated Probability-Generating-Function-Based Bayesian Information Criterion

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.

Wang, Y., Shu, Z., Gao, F., Cao, Z.2026-09-16
💻 bioinformatics

Interpretable Machine Learning Reveals Complementary Age-Related Signatures in the Oral and Gut Microbiome

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.

Ruthbah, C. A., Sadi, T. H., Jahan, N. E. S., Adib, A. N. M. T.2026-09-15
💻 bioinformatics

Hierarchical temporal transformer for cancer grade prediction and cross cancer transfer learning from pathology reports

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.

Brimo, N., Anand, R., Harb, H., Serdaroglu, D. C.2026-09-15