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.
The paper introduces *biomes*, an R package that provides harmonized global raster layers for 31 distinct biome schemes and a reproducible, data-driven workflow to help researchers select the most suitable scheme and classify species occurrence records for ecological and evolutionary studies.
SFUMATO is a GPU-accelerated Bayesian probabilistic framework that overcomes the limitations of hard-label clustering in spatial transcriptomics by generating uncertainty-aware, color-blended visualizations and quantitative maps that capture gradual transcriptional transitions and tissue heterogeneity without requiring prior cell segmentation.
This paper reveals that the predictive performance of antidiabetic peptide classifiers is often inflated by homology leakage in standard benchmarks, demonstrating that when homologous sequences are properly separated, accuracy drops significantly and simpler models can achieve comparable results with far greater efficiency.
This paper introduces the Instability Quotient Criterion (IQC), a novel metric leveraging ensemble modeling and Shannon's entropy to quantify and improve the reproducibility of feature selection in high-dimensional biological analyses, specifically addressing the instability issues inherent in Elastic Net regression.
vartracker is an open-source, pathogen-agnostic tool that streamlines the analysis and visualization of longitudinal pathogen evolution by integrating diverse genomic inputs, tracking variant emergence and persistence, and generating reproducible results with interactive visualizations.
This study presents a framework that integrates omics data with experimental protein-RNA structures to identify binding motif cores and benchmark AlphaFold3, revealing both its predictive promise and its current limitations, such as signs of memorization and challenges with alternative binding modes.
CHACAM is a supervised machine learning framework that integrates gene expression, ligand-receptor interactions, and cell contact maps to resolve ambiguous cell identities in early *C. elegans* embryogenesis by leveraging cell-cell interaction signatures for high-precision annotation.
The paper introduces PeTriPPI2, a hybrid framework that fuses ESM-2 sequence embeddings with PeTriBERT structural representations to predict protein-protein interactions, achieving high accuracy and precision on the Pinder dataset while demonstrating the complementary value of both sequence and structural features through ablation studies.
This study demonstrates that the usage of MDM2 P1 versus P2 promoters serves as a molecular switch distinguishing between chromosomal instability-driven and environment-adaptive, gastric-metaplastic colorectal cancer phenotypes, thereby enabling the stratification of patients by therapeutic responsiveness.
The paper introduces Murmurent, an open-source, shared software environment designed to support collaborative biomedical research by providing specialized agents, tiered memory, traceability, and enforced standard operating procedures that lower technical barriers for academic labs using agentic AI, demonstrated through its application in identifying inhibitors for the Pin1 protein.