💻 Category

bioinformatics

1200 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

Predicting Antibody Self-Association with Sequence Structure Fusion Models: The Central Role of CSI-BLI in Early Developability Screening

This study presents an end-to-end deep learning framework that fuses fine-tuned protein language models with AlphaFold-derived 3D structural graphs to accurately predict antibody self-association (measured by CSI-BLI), demonstrating that integrating sequence and spatial context significantly outperforms sequence-only baselines and provides interpretable insights into key developability drivers like charge and hydrophobicity.

Ahmed, S., Devalle, F., Leisen, L., Pham, T., Amofah, B., Lee, A., Hutchinson, M., Chakiath, C., DiChiara, J., Farzandh (…)2026-04-15
💻 bioinformatics

Testing and Estimating Causal Treatment Effect Heterogeneity in Observational Studies via Revised Deep Semiparametric Regression: A Lung Transplant Case Study

This paper introduces deepHTL, a deep semiparametric regression framework that effectively tests for and estimates causal treatment effect heterogeneity in observational studies, demonstrating its superiority over existing methods in simulations and revealing that younger, lower-risk lung transplant recipients derive significantly greater functional benefits from bilateral compared to single lung transplantation.

Yuan, S., Zou, F., Zou, B.2026-04-15
💻 bioinformatics

From Movement to METs: A Validation of ActTrust(R) for Energy Expenditure Estimation and Physical Activity Classification in Young Adults

This study validates the ActTrust(R) accelerometer as a simple, cost-effective tool for estimating energy expenditure and classifying physical activity intensity in young adults by demonstrating its strong correlation with treadmill speed and metabolic equivalents, as well as its high accuracy in predicting activity levels comparable to the widely used ActiGraph(R) GT3X+.

dos Santos Batista, E., Basilio Gomes, S. R., Bruno de Morais Ferreira, A., Franca, L. G. S., Fontenele Araujo, J., Mort (…)2026-04-14
💻 bioinformatics

Beyond Single Algorithms: A Framework for Validating and Aggregating Active Modules in Genetic Interaction Networks

This paper introduces a framework that validates and aggregates the distinct outputs of multiple Active Module Identification algorithms using spectral clustering and Greedy Conductance-based Merging to overcome the limitations of single-algorithm approaches and reveal cohesive biological modules, including hidden genes, in genetic interaction networks.

Liu, J., Xu, M., Xing, J.2026-04-14
💻 bioinformatics

A correlational study of ABCA3 and SCN4B as exercise-related biomarkers of patients with Stanford type A aortic dissection

This study identifies ABCA3 and SCN4B as exercise-related biomarkers for Stanford type A aortic dissection, demonstrating their diagnostic potential through a nomogram while elucidating their involvement in circadian rhythm and immune regulation pathways and suggesting zonisamide and MRS1097 as possible therapeutic agents.

Qiao, S., Chen, T., Xie, B., Han, Y., Wang, B., Li, Y., Jia, B., Wu, N.2026-04-14
💻 bioinformatics

Identification of the novel inhibitors against M. tuberculosis ESX-1 secretion system EccA1 enzyme using virtual screening, docking and dynamics simulation techniques

This study identifies five novel ZINC compounds (Z1–Z5) as potential antivirulence inhibitors against the *M. tuberculosis* EccA1 enzyme through virtual screening, docking, and molecular dynamics simulations, demonstrating their superior binding affinity compared to known inhibitors and favorable drug-like properties.

Kumar, R., saxena, a. K.2026-04-14
💻 bioinformatics

MAJEC: unified gene, isoform, and locus-level transposable element quantification from RNA-seq

MAJEC is a unified, fast Expectation-Maximization framework that accurately quantifies genes, isoforms, and individual transposable element loci from RNA-seq data by leveraging splice junction evidence to resolve read overlaps, thereby significantly reducing false attribution artifacts compared to existing tools like TEtranscripts and Telescope.

Lim, T.-Y., Firestone, A. J.2026-04-14✓ Author reviewed ⓘ
💻 bioinformatics

A Hierarchy-aware Gene Exploration Platform for Multi-layered Toxicogenomic Analysis: A Case Study on Acetaminophen-induced Hepatotoxicity

This paper presents a hierarchy-aware gene exploration platform that integrates HGNC biological knowledge into a hyperdiffusion-based similarity kernel to significantly enhance the interpretability and functional coherence of transcriptomic analysis, as demonstrated by its successful application in identifying key toxicological modules in acetaminophen-induced hepatotoxicity.

Kim, M., Cui, Y., Kim, M. G.2026-04-14
💻 bioinformatics

Predicting Pre-treatment Resistance or Post-treatment Effect? A Systematic Benchmarking of Single-Cell Drug Response Models

This study systematically benchmarks single-cell drug response models across diverse datasets, revealing that while scDEAL demonstrates superior robustness to class imbalance, most current methods struggle to predict intrinsic pre-treatment resistance despite effectively capturing post-treatment transcriptional changes, thereby highlighting the need for next-generation models with greater clinical relevance.

Shen, L., Sun, X., Zheng, S., Hashmi, A., Eriksson, J., Mustonen, H., Seppänen, H., Shen, B., Li, M., Vähä-Koskela, M (…)2026-04-14