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TriStageHLA improves epitope prioritization by integrating peptide screening, binding prediction, and immunogenicity assessment with high accuracy and efficiency.

LinearKnot approximates CParty's hierarchical pseudoknot analysis with linear time complexity, achieving comparable accuracy for RNA structure prediction and ensemble calculations.

The paper develops machine learning models to predict artemisinin resistance in Plasmodium falciparum K13 mutants using physicochemical and structural features of mutations.

TurboANI is a fast, homology-aware ANI estimator with up to ~40,000x speedup over alignment-based methods and better accuracy than FastANI and skani.

This paper introduces π-SPECFormer, a foundation model for mass spectrometry-based proteomics that decodes the dark proteome by pre-training on unannotated spectra and outperforms baselines in peptide sequencing and modification detection.

This paper uniformly re-analyzes public omics data from exercise studies in humans and rodents, providing standardized effect sizes and meta-analyses across multiple tissues and molecular layers.

This paper introduces GeneEmbedLLM, a framework using large language models to generate gene embeddings from multi-source textual annotations, achieving competitive performance in various downstream tasks.

TideCluster is a pipeline for clustering and classifying tandem repeats across genomes, demonstrated on Arabidopsis and Solanum species.

scPolyA provides a framework for analyzing alternative polyadenylation in single-cell and spatial transcriptomes, revealing APA patterns in cancer tissues.

This paper evaluates how much improvement in cross-cancer survival prediction is due to model selection methods versus actual concordance gains, finding evaluation effects can be as significant as reported gains.

AOPTK automates toxicological literature analysis using AI, achieving high accuracy in identifying chemical-biological effect relationships.

mFrag improves lung cancer early detection using fragmentomics by integrating DNA methylation data and combining two models to enhance prediction accuracy.

BEHAV3D-Explorer provides a user-friendly platform for analyzing 3D live-cell imaging data using natural language guidance to select analysis strategies and configure workflows.

BioMeme predicts cell responses to new perturbations by recalling measured experiments, outperforming neural models in accuracy and speed.

Ok-k-mer selects optimal k-mer length ensembles to improve genomic distance estimation by maximizing correlation with phylogenetic branch distances.

GraffiTE v1.1 is a Nextflow pipeline that genotypes structural variants, annotates polymorphic mobile elements, and maps epigenetic data to transposable elements using pangenome graphs, revealing hypomethylated elements and age-related demethylation.

The paper introduces a PyTorch-based tool for fast proteome-scale local frustration analysis, achieving 70-13,000x speedup over the original FrustratometeR.

This paper introduces efficient methods to update colored k-mer indexes without rebuilding them from scratch, enabling dynamic maintenance of large genomic collections.

The paper analyzes six-residue Omega-loops in protein complexes, showing their structural constraints, sequence conservation patterns, and functional significance through sequence design and mutation mapping.