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

R2C: improving ab initio residue contact map prediction using dynamic fusion strategy and Gaussian noise filter.

Bioinformatics (Oxford, England) - 15 Aug 2016

Yang Jing, Jin Qi-Yu, Zhang Biao, Shen Hong-Bin

Abstract excerpt

MOTIVATION: Inter-residue contacts in proteins dictate the topology of protein structures. They are crucial for protein folding and structural stability. Accurate prediction of residue contacts especially for long-range contacts is important to the quality of ab inito structure modeling since they can enforce strong restraints to structure assembly. RESULTS: In this paper, we present a new Residue-Residue Contact...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Computational Biology
Share to Machine Learning

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Mutation

Share to Normal Distribution

Share to Protein Folding

Share to Proteins

Source

Identifiers

PMID: 27153618
DOI: 10.1093/bioinformatics/btw181

Genes

CASP10

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

R2C: improving ab initio residue contact map prediction using dynamic fusion strategy and Gaussian noise filter.PMID 27153618Read ↗

Contact prediction using mutual information and neural nets.Semantic proximity Read ↗

Correlated mutations and residue contacts in proteins.Semantic proximity Read ↗

Forecasting residue-residue contact prediction accuracy.Semantic proximity Read ↗

Predicting residue contacts using pragmatic correlated mutations method: reducing the false positives.Semantic proximity Read ↗

UEP: an open-source and fast classifier for predicting the impact of mutations in protein-protein complexes.Semantic proximity Read ↗

PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments.Semantic proximity Read ↗

CCMpred--fast and precise prediction of protein residue-residue contacts from correlated mutations.Semantic proximity Read ↗

Identification of Protein-Protein Interactions by Detecting Correlated Mutation at the Interface.Semantic proximity Read ↗

Prediction of contact maps with neural networks and correlated mutations.Semantic proximity Read ↗

Is there an optimal substitution matrix for contact prediction with correlated mutations?Semantic proximity Read ↗

Co-Occurring Atomic Contacts for the Characterization of Protein Binding Hot Spots.Semantic proximity Read ↗

Predicting protein β-sheet contacts using a maximum entropy-based correlated mutation measure.Semantic proximity Read ↗

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