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.
The paper introduces DeepTMHMM2, a novel predictor that simultaneously achieves accurate transmembrane topology prediction—including previously unmodeled re-entrant regions and interfacial helices—and subcellular localization across 17 biological membranes, revealing the widespread presence of non-crossing segments in the proteome.
This study presents an updated XMAn database that integrates extensive COSMIC mutation data to create specialized peptide repositories, enabling the mass spectrometry-based identification of cancer-associated mutated peptides that are typically missed by standard canonical protein databases.
This paper presents a hybrid framework that combines a Variational Autoencoder to generate synthetic longitudinal trajectories from cross-sectional kidney cancer data with a symbolic rule-induction system to produce interpretable, probabilistic forecasts of disease progression that match deep learning accuracy while offering transparent, human-readable insights into the underlying molecular mechanisms.
HIDE-Deconv is an open-source hierarchical deconvolution framework that jointly optimizes cellular composition estimates across multiple resolution levels to reveal biologically relevant cellular remodeling in diseases like lung adenocarcinoma and sepsis, outperforming existing single-resolution methods.
The paper introduces evoPLM-Tree, a tree-aware conditional language model that integrates phylogenetic context to accurately predict viral mutational patterns and lineage-specific evolutionary trajectories, demonstrating strong alignment with experimental functional constraints.
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.