💻 Category

bioinformatics

855 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

Testing the reliability of AI-generated protein structures

This study demonstrates that AlphaFold2 and ColabFold exhibit a very low false positive rate when predicting structures for non-protein sequences, while serendipitously revealing that some high-scoring predictions in noncoding human genomic regions correspond to previously unannotated pseudogenes, suggesting potential errors in existing gene annotations and validating the utility of high-scoring structural predictions for further investigation.

Xu, A., Salzberg, S.2026-06-13
💻 bioinformatics

ADMETron: An AI-driven SaaS platform for comprehensive ADMET prediction and compound prioritisation

ADMETron is an AI-driven SaaS platform that integrates RNN-derived molecular embeddings with gradient boosting machines to predict 34 ADMET endpoints and features an interactive radar graph visualization tool, enabling comprehensive, high-throughput compound prioritization and data-driven decision-making in drug discovery.

Nair, D. N., Yadav, R. S., Jondhale, P. M., Didhate, S., Gunjal, G., Ranjit, A., Patil, P., Dawande, A., Shisode, A., Bh (…)2026-06-13
💻 bioinformatics

Revealing trajectories of multi-modal voxel-level changes in neurodegenerative diseases using latent event mapping

This paper introduces Latent Event Mapping (LEMING), a scalable and interpretable unsupervised modeling technique that reconstructs voxel-level trajectories of multi-modal neuroimaging changes in Alzheimer's disease, revealing new insights into progression-dependent mechanisms such as the late-stage association between acetylcholine receptor density and amyloid pathology.

Pinnawala, S., Hartanto, A., Jairamani, M., Simpson, I. J. A., Wijeratne, P. A.2026-06-11
💻 bioinformatics

STITCH links cellular morphology and gene expression in spatial transcriptomics

The paper introduces STITCH, an interpretable method based on tangent principal component analysis in a Kendall shape manifold that effectively links size-independent cellular morphology to gene expression in spatial transcriptomics, outperforming existing deep learning approaches in identifying biologically relevant shape-transcriptome relationships across diverse datasets.

Kumar, S., Shi, Y., Vallius, T., Day, C.-P., Absil, P.- A., Srivastava, A., Hannenhalli, S., Gopalan, V.2026-06-11
💻 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