💻 Category

bioinformatics

1199 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

Calibrated Uncertainty Quantification for Patient-Level AML Drug Sensitivity Prediction Using Split Conformal Prediction

This study introduces a split conformal prediction framework applied to the BeatAML 2.0 cohort that successfully generates statistically calibrated uncertainty intervals for patient-level AML drug sensitivity predictions, revealing distinct uncertainty patterns across drug classes while demonstrating that such uncertainty is independent of standard molecular risk classifications.

Shokrzadeh, A. J., Shokrzadeh, P.2026-06-11
💻 bioinformatics

Robust semi-supervised scRNA-seq integration from virtual adversarial learning

The paper introduces scCRAFT+, a robust semi-supervised scRNA-seq integration model that leverages Virtual Adversarial Training to incorporate marker gene information, thereby overcoming the limitations of existing methods in preserving fine-grained cell subtype distinctions and improving annotation accuracy even with noisy or incomplete marker sets.

He, C., Filippidis, P., Xing, J., Kleinstein, S., Guan, L.2026-06-11
💻 bioinformatics

Pillbox: A Leakage-Aware Foundation-Model Predictor and Lineage-Ceiling Diagnostic for Cancer Drug Response

Pillbox is a leakage-audited foundation-model predictor that integrates CpGPT, CLAMP, and gene-expression embeddings via FiLM-conditioned graph attention to achieve high accuracy in cancer drug response prediction while using cross-architecture residual correlation to diagnose feature-stack saturation and confirm that tissue lineage remains the dominant factor in drug response.

Hill, J. J. K., Ryoo, H. J., Ghanta, A., Singh, S., Anders, D., Jiao, E., Jeong, J.2026-06-11
💻 bioinformatics

GermRL: Alleviating The Germline Bias In Autoregressive Antibody Language Models Through Reinforcement Learning

This paper introduces GermRL, a lightweight reinforcement learning framework that effectively alleviates germline bias in autoregressive antibody language models, enabling the one-shot generation of structurally plausible, diverse antibody candidates with high mutation thresholds from germline sequences for therapeutic discovery.

Ludwig, L., Chungyoun, M., Gray, J. J.2026-06-11
💻 bioinformatics

EditorForge: An Active-Site-Aware Framework for Inverse-Folding-Based Protein Redesign

EditorForge is a modular framework that enhances inverse-folding-based protein redesign by integrating explicit design masks, active-site shielding, and structural quality control to safely generate constrained, high-confidence sequence candidates for enzyme and genome-editor domains.

Chen, A., Siddiqui, J., Taucar, W., Tiralongo, L., Tkachenko, M., Xu, A., Bawa, S., Guo, S., Pinska, O., Rim, J., Shi, J (…)2026-06-11
💻 bioinformatics

OMIO: A policy-driven Python library for reproducible microscopy image I/O

OMIO is a lightweight, open-source Python library that ensures reproducible microscopy image analysis by separating low-level file reading from a centralized, policy-driven layer that enforces canonical axis conventions and robust metadata normalization across heterogeneous file formats.

Musacchio, F., Antony, H., Crux, S., Fuhrmann, F., Gockel, N., Hoffmann, D. M., Mercan, D., Nebeling, F. C., Fuhrmann, M (…)2026-06-11