Tag: computational biology

Researchers have introduced a linear-time semi-metric that evaluates reconciled gene trees by simultaneously weighing species tree mappings, evolutionary events, and ...

A scientific review highlights how computational linguistics, statistical laws, and formal grammars help decode regulatory patterns across noncoding genomic sequences.

A new multi-task adversarial autoencoder framework generates functional genomic sequences that preserve biophysical properties and emergent biological structure, outperforming standard ...

A compact energy-based neural network called LEEN predicts protein stability changes from single amino acid substitutions using roughly 2 to ...

Researchers have introduced SwimmingIndividuals, an open-source agent-based model that simulates marine ecosystem dynamics and fisheries management using a hybrid CPU-GPU ...

Coarse-grained simulations and deep learning demonstrate that the DEAD-box helicase DDX3X can separate double-stranded RNA without ATP by harnessing thermal ...

A comprehensive multi-cohort study reveals elevated COL1A2 expression in gastric cancer, detailing its modest prognostic contribution, stromal associations, and computational ...

Machine learning-driven rescoring with MS²Rescore improves peptide identification rates and taxonomic specificity in metaproteomics datasets, particularly in complex environments such ...

Researchers have introduced KlaDB, an open-access database compiling 64,072 experimentally identified lysine lactylation sites across nine species alongside standardized prediction ...

A computational workflow named CELLIA pairs statistical differential expression metrics with curated marker databases to improve the accuracy and consistency ...

Researchers have developed SAIGE-QTL, an efficient Poisson mixed-model framework that directly analyzes single-cell read counts to substantially expand expression quantitative ...

Researchers have introduced iAMP-Deep, an alignment-free artificial intelligence framework that combines multimodal sequence embeddings and ensemble learning to discover divergent ...

Researchers developed VOL-Gene, a graph-based clustering method that partitions human genes into exclusive functional categories to annotate understudied genetic markers.

Researchers developed ESMTmP, a multiscale multitask learning model that predicts protein melting temperatures with reduced error and order-consistent outputs.

Researchers have developed CancerSubtyper, an end-to-end deep learning framework with an interactive web interface that automates preprocessing, batch effect correction, ...

Researchers have developed ALPACA, an open-source visual data mining system that integrates multi-omics abundance with subcellular localization to pinpoint cancer-associated ...

Researchers have developed NuWebKit, an open web-based simulation framework that combines artificial intelligence code generation with modular runtime mathematical modeling.

Researchers profiled Magnolia officinalis bark extract and evaluated computational interactions between its candidate constituents and apoptosis-associated proteins relevant to chemotherapy ...

Researchers developed TCRi, a hierarchical Bayesian information-theoretic framework that infers joint distributions of T cell clonotypes and phenotypes to predict ...

A computational study from the University of Kalyani predicts the structural features of Aβ175, a protein encoded by the circular ...