Discover, rate, and discuss biology preprints.

This paper introduces a simple, fast, and adaptable cluster-based event detection algorithm for nanopore signals, showing improved efficiency and reduced noise in biological nanopore data.

The paper introduces a model-free permutation method to estimate P-values in genetic studies, reducing computational demands while maintaining accuracy.

Seq2Pocket improves binding site prediction by combining pLM fine-tuning, data enhancement, and structure-aware post-processing, achieving state-of-the-art performance on benchmark datasets.

SPIFEE is a pipeline that improves the analysis of live-cell fluorescence microscopy data by reducing noise and extracting features more accurately and objectively.

This study investigates miR-128's role in regulating vascular remodeling in SHRs through PPAR-γ and the TLR4/NF-κB pathway, showing that RDN restores miR-128 and improves vascular function.

SRSA-VAE improves single-cell clustering by using self-attention and residual connections, outperforming existing methods on scRNA-seq and CITE-seq data.

Comparing local point neighborhoods before and after dimensionality reduction reveals that standard scRNA-seq pipelines, t-SNE, and UMAP often distort local neighborhoods by more than 95%, altering downstream clustering.

This paper applies conformal prediction to molecular retrieval from mass spectra, producing reliable, spectrum-specific candidate sets with controlled reliability.

G-SPRI predicts cancer driver events from missense mutations by integrating structural and mutation-specific features using a graph model.

The paper introduces plyxp, an R package that provides a grammar for efficiently manipulating annotated matrix data in the Bioconductor ecosystem.

This paper develops two codon optimization tools using classical and AI methods, showing they outperform existing tools in protein expression.

This paper introduces a deep learning framework for designing proteins with site-specific post-translational modifications using latent diffusion models and a PTM-aware language model.

This study performs a meta-analysis of bovine transcriptomic datasets to identify immune-related differentially expressed genes and signaling pathways associated with disease resistance in cattle.

This paper introduces a computational framework for true 3D spatial reconstruction of tissues, demonstrating improved fidelity over 2D methods across multiple spatial omics modalities.

The paper develops epiVIP, a deep learning framework to predict HLA-I peptide abundance from (sc)RNA-seq data, showing that peptide abundance and non-selfness jointly determine antigenicity and predict tumor reactivity and survival.

scLASER simulates and detects time-dependent cellular dynamics in longitudinal studies, showing higher sensitivity than traditional methods in rare cell types and non-linear patterns.

This paper applies geographically weighted regression to spatial proteomics data to better model protein dispersion in tumors, showing improved accuracy over global regression methods.

LIVIA is a browser-based tool that assesses and visualizes predicted protein interactions by computing confidence metrics and providing 3D visualization options.

The paper characterizes the maturation of temperate phages in the infant gut microbiome and finds that viral microdiversity, not abundance, better predicts age, with stunting not delaying maturation.

The paper develops a machine learning framework to identify cell type-specific gene targets for IBD using a harmonized single-cell atlas and validates them experimentally.