💻 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

From Plants to Patients: Mitochondrial Stress Signaling as a Systems Framework for Human Disease Vulnerability

This paper proposes a comparative *in silico* framework demonstrating that while plant and human mitochondrial stress signaling networks have diverged in complexity, they share fundamental organizational principles, suggesting that the simpler, resilience-oriented plant systems can serve as a conceptual model to generate testable hypotheses about human mitochondrial disease vulnerability.

Gokdemir, F. S., Eyidogan, F., Kubat, G. B., Singh, K. K.2026-08-21
💻 bioinformatics

Beyond the Default: Optimizing Molecular Networking with arteMIS

The paper introduces arteMIS, a systematic framework that optimizes molecular networking parameters through multi-metric evaluation and Latin Hypercube Sampling to overcome the limitations of default settings, thereby producing more robust, chemically meaningful, and stable networks across diverse datasets and scoring methods.

Torres Ortega, L. R., Charria Giron, E., Huber, F., Simone, M., Sosio, M., van der Hooft, J. J. J.2026-08-21
💻 bioinformatics

Sparse Autoencoders Reveal Structural and Family-level Features in BiRNA-BERT

This paper introduces SPIRAL, a sparse autoencoder framework that successfully decodes the hidden states of the BiRNA-BERT RNA language model into interpretable, nucleotide-aligned features representing secondary structure and RNA family types, thereby enhancing downstream prediction performance despite byte-pair tokenization challenges.

Hossain, M. S., Sojib, M. R., Tahmid, M. T., Rahman, M. S.2026-08-20
💻 bioinformatics

Relational Graph Convolutional Networks for Glioblastoma Biomarker Discovery via ceRNA and Copy Number Variation Analysis

This study introduces a novel late fusion relational graph convolutional network (RGCN) ensemble that integrates competing endogenous RNA (ceRNA) and copy number variation (CNV) data to identify five prognostic biomarkers, including hsa-miR-196a and hsa-miR-224, for improved survival prediction and therapeutic targeting in glioblastoma.

Khandelwal, S., Jarvis, N., Zhan, J.2026-08-20
💻 bioinformatics

PandaDock: An Open-Source Molecular Docking Platform with Flexible-Ligand Search and Equivariant Neural Scoring

PandaDock is an open-source molecular docking platform that integrates flexible-ligand conformational search with analytic gradients, specialized modules for complex binding scenarios, and a scalable SE(3)-equivariant neural network scoring function to achieve competitive pose recovery and affinity prediction performance across diverse protein-ligand targets.

Panda, P. K.2026-08-20