1197 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.
This paper proposes a stability-driven multi-omics integration framework that combines sparse generalized canonical correlation analysis with rigorous cross-validation to identify reproducible latent structures, demonstrating its effectiveness in a thyroid cancer cohort by revealing consistent disease associations and temporal changes in metabolomic and proteomic profiles.
This paper presents a comprehensive benchmark of segmentation model robustness across 30 biological imaging datasets and 36 corruption types, revealing that high performance on clean images does not guarantee corruption resilience and that older methods like StarDist can outperform modern foundation models, with failures primarily occurring in the early encoding layers.
This study introduces the HuMonoAg-Bench benchmark to evaluate ten antibody-antigen co-folding protocols, revealing that while recent methods have significantly improved CDRH3 modeling and achieved medium-or-better accuracy for about half of post-cutoff complexes, challenges persist in sampling correct binding modes and modeling flexible loops, limiting the prediction of many structurally heterogeneous complexes.
Kiosc is a self-hosted, user-friendly web platform built on Django that simplifies the orchestration, organization, and access control of Docker containerized bioinformatics web applications, offering a lower-overhead alternative to Kubernetes for sharing interactive analysis results.
The paper introduces BIOCURRENT, a causal inference framework that reconstructs donor-specific pseudotime geometry to quantify how microenvironmental contexts distort transcriptional progression intervals, thereby identifying stage-specific deviations and potential intervention checkpoints in complex biological systems like T-cell development and COVID-19 immune dysregulation.
CodonMamba is a state-of-the-art foundation model for mRNA coding sequence design that, through pretraining on large-scale corpora, achieves superior performance in prediction tasks and enables programmable, inference-time steering of codon optimization to meet specific host or application preferences without retraining.
This paper introduces a resolution-aware, calibrated Minimum Uncertainty criterion and a discriminative power evaluation framework to enhance the robustness, interpretability, and statistical calibration of Bayesian network structure learning from heterogeneous data.
Delta-Marches is a generative AI framework that decodes disease mechanisms by simulating idealized morphological transitions between tissue classes to pinpoint subcellular features driving pathophysiological changes, as demonstrated in renal carcinoma grading and colorectal dysplasia.
KRAKEN is a scalable, provenance-tracked knowledge graph that addresses the underrepresentation of multiomic and wellness data by integrating diverse biomedical sources into a modular, 15-million-node system featuring standardized semantics, built-in analytical tools, and multi-modal interfaces for both human and AI-driven research.
This study demonstrates that explicit Pfam-domain content outperforms or matches ESM-2 sequence-derived representations for retrieving biosynthetic gene clusters, indicating that sequence embeddings do not currently improve the recovery of alternative biosynthetic pathways beyond established domain-based metrics.