💻 Category

bioinformatics

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.

💻 bioinformatics

Real-World Progression-Free Survival with Erlotinib versus Osimertinib in EGFR L858R+T790M Compound Mutation Non-Small Cell Lung Cancer: An Exploratory Analysis of the MSK-CHORD Dataset

This exploratory analysis of the MSK-CHORD dataset suggests that, unlike in L858R-only or T790M-only contexts where osimertinib is superior or equivalent, the EGFR L858R+T790M compound mutation in non-small cell lung cancer may represent a distinct pharmacological entity where erlotinib numerically outperforms osimertinib, warranting prospective validation.

Dalloul, Z., Abboud, A., Dalloul, I., Abdelsalam, M.2026-06-30
💻 bioinformatics

NPTX2-Centered Cognitive Resilience Mechanisms in the Context of AD Pathology

This study reveals that cognitive resilience to Alzheimer's disease pathology is characterized by a distinct molecular state centered on preserved NPTX2 expression, which maintains core synaptic and inhibitory programs while selectively recruiting adaptive proteostasis, trafficking, and immune pathways in high-pathology individuals, a coordination that is lost in symptomatic disease.

Lao, Y., Xiao, M.-F., Ji, S., Piras, I. S., Kim, K., Bonfitto, A., Song, S., Aldabergenova, A., Sloan, J., Trejo, A., Ge (…)2026-06-29
💻 bioinformatics

Context-dependent correlations mislead transcriptomic network inference in bulk and single-cell data

This study demonstrates that pooled correlation coefficients in bulk and single-cell transcriptomic data frequently mislead network inference by reversing direction due to Simpson's paradox driven by biological heterogeneity, necessitating the reporting of context-specific correlations and heterogeneity statistics rather than relying on single global estimates.

Asiaee, A., Bombina, P., McGee, R. L., Reed, J., Abrams, Z. B., Abruzzo, L. V., Coombes, K. R.2026-06-29
💻 bioinformatics

Retention, not flux: endpoint confounding caps computational prediction of peptide skin penetration, with a delivery-aware reframing

The paper argues that the stalled predictive performance in modeling peptide skin penetration stems from an ill-posed reliance on conflated transdermal flux labels that ignore delivery vehicles and endpoints, proposing instead a reframed approach that separates intrinsic barrier-crossing potential from delivery-specific retention and risk.

Komianos, N., Prakash, P.2026-06-29
💻 bioinformatics

Bamsnap-LRS: an automated batch visualization tool for long-read sequencing alignments

Bamsnap-LRS is an automated command-line tool designed to overcome the scalability and optimization limitations of existing visualization software by enabling high-throughput, publication-ready batch visualization of long-read sequencing alignments with support for long-read-specific features, phased SNP inspection, and diverse genomic analyses.

Chen, W., Yang, C., Qiu, L., Hu, J., Zhou, Y.2026-06-25
💻 bioinformatics

Development of Deep-Learning Models that Predict Quantitative Protein-Ligand Interac-tions in Glycobiology as a part of a Capstone Course

As part of a University of Alberta capstone course, this paper introduces three deep-learning models (ProMax, APEX, and UltraMax) trained on a hybrid dataset of approximately one million protein-ligand pairs to predict quantitative glycan-protein binding strengths, while highlighting the challenges posed by long-tail data distributions and insufficient chiral feature utilization.

Yin, H., Liu, W., Zhou, W., Chang, Z., Carpenter, E. J., Satyajith, A., Haregu, S., Greiner, R., Derda, R.2026-06-24
💻 bioinformatics

ComplexDesign: sequence-hallucination design of protein binders bridging multiple proteins

ComplexDesign is a hallucination-based approach that utilizes structure-prediction-guided sequence optimization and a specialized masking mechanism to successfully design multichain protein complexes and flexible binders bridging multiple targets, outperforming existing methods in both unconditional multimer design and ternary complex generation.

Xu, J., Ren, M., Qi, N., Zhang, X., He, Z., Yu, C., Bu, D.2026-06-24