bioinformatics papers | Gist.Science

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

Discovery of Selective Nrf2 Activators from Natural Products: AComputational Screening Approach to Minimize Off-Target Effects on PXR and CYP2D6

This study presents a large-scale computational screening of nearly 630,000 natural products using a novel three-tier selectivity strategy to identify 10 ultraselective Nrf2 activators that effectively bind KEAP1 while minimizing off-target interactions with PXR and CYP2D6, thereby offering a promising pathway for developing safer therapies for oxidative stress-related diseases.

Wang, Y., Gong, Y., Li, R., Li, Z., Cai, H., Fan, L., Ma, H.2026-04-15

💻 bioinformatics

Benchmarking precision matrix estimation methods for differential co-expression network analysis

This paper benchmarks various precision matrix estimation methods for differential co-expression network analysis using simulated data, revealing that performance is highly dependent on specific data characteristics and identifying GLassoElnetFast as the most accurate method while emphasizing the need for comprehensive evaluation frameworks to avoid misleading conclusions.

Overmann, M., Grabert, G., Kacprowski, T.2026-04-15

💻 bioinformatics

Sex-biased gene expression shapes sex differences in gene essentiality

This study integrates sex-biased transcriptomic profiles with CRISPR screens to reveal that while sex-biased gene expression can influence sex differences in gene essentiality, sex-biased functional dependency is primarily driven by direct, expression-independent effects, particularly on the X chromosome where dosage plays a dominant role.

Rocca, C., DeCasien, A. R.2026-04-15

💻 bioinformatics

Beyond Structure and Affinity: Context-Dependent Signals for de novo Binder Success

This study demonstrates that integrating biology-informed sequence features—specifically those capturing aggregation propensity, topology, and context-dependent signals—significantly improves the experimental success rate of de novo protein binder designs beyond traditional structure- and affinity-based evaluations.

Bozkurt, C.2026-04-15

💻 bioinformatics

Decoding Single-Cell Omics of Perturbation Responses Using DeSCOPE

DeSCOPE is a lightweight conditional variational autoencoder framework that outperforms existing baselines in predicting single-cell responses to genetic perturbations across unseen genes, cell types, and combinatorial multi-gene scenarios, serving as a versatile multi-modal virtual cell model for therapeutic target design.

Wu, P., Wei, H., Li, Y., Zheng, X., Zhou, C., Hu, X., Wang, C.2026-04-15

💻 bioinformatics

Differential co-localisation analysis of multi-sample and multi-condition experiments with spatialFDA

The paper introduces spatialFDA, an open-source Bioconductor R package that integrates spatial statistics and functional data analysis to accurately quantify and test for differences in cellular co-localization across multiple conditions in spatial omics data, demonstrating its effectiveness through simulations and biological applications in type-1 diabetes.

Emons, M., Scheipl, F., Gunz, S., Purdom, E., Robinson, M. D.2026-04-15

💻 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

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