Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Prediction of disease-associated nsSNPs by integrating multi-scale ResNet models with deep feature fusion.

Briefings in bioinformatics - 17 Jan 2022

Ge Fang, Zhang Ying, Xu Jian, Muhammad Arif, Song Jiangning, Yu Dong-Jun

Abstract excerpt

More than 6000 human diseases have been recorded to be caused by non-synonymous single nucleotide polymorphisms (nsSNPs). Rapid and accurate prediction of pathogenic nsSNPs can improve our understanding of the principle and design of new drugs, which remains an unresolved challenge. In the present work, a new computational approach, termed MSRes-MutP, is proposed based on ResNet blocks with multi-scale kernel...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Area Under Curve
Share to Cellular Microenvironment

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Computational Biology

Share to Deep Learning

Share to Disease

Share to Pharmaceutical Preparations

Share to Polymorphism, Single Nucleotide

Source

Open in PubMed Open full text

Identifiers

PMID: 34953462
DOI: 10.1093/bib/bbab530
PMCID: PMC8769912

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Prediction of disease-associated nsSNPs by integrating multi-scale ResNet models with deep feature fusion.PMID 34953462Read ↗

FCMSTrans: Accurate Prediction of Disease-Associated nsSNPs by Utilizing Multiscale Convolution and Deep Feature Combination within a Transformer Framework.Semantic proximity Read ↗

An integrative framework for clinical diagnosis and knowledge discovery from exome sequencing data.Semantic proximity Read ↗

Inferring non-synonymous single-nucleotide polymorphisms-disease associations via integration of multiple similarity networks.Semantic proximity Read ↗

Evaluation of computational techniques for predicting non-synonymous single nucleotide variants pathogenicity.Semantic proximity Read ↗

Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies.Semantic proximity Read ↗

Prediction of disease-associated functional variants in noncoding regions through a comprehensive analysis by integrating datasets and features.Semantic proximity Read ↗

Using DIVAN to assess disease/trait-associated single nucleotide variants in genome-wide scale.Semantic proximity Read ↗

Prioritization Of Nonsynonymous Single Nucleotide Variants For Exome Sequencing Studies Via Integrative Learning On Multiple Genomic Data.Semantic proximity Read ↗

Prediction of deleterious nonsynonymous SNPs by integrating multiple classifiers – An application to neurodegenerative diseasesSemantic proximity Read ↗

dbNSFP: a lightweight database of human nonsynonymous SNPs and their functional predictions.Semantic proximity Read ↗

Collective judgment predicts disease-associated single nucleotide variants.Semantic proximity Read ↗

SFM: A novel sequence-based fusion method for disease genes identification and prioritization.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.