ArticleScientifica2016
Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest.
Article in Scientifica, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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Who cites it
23 citing papers in PubMed.
- Studying the Effect of the Host Genetic Background of Juvenile Polyposis Development Using Collaborative Cross andInternational journal of molecular sciences · 2024Article
- High-fat diet and oral infection induced type 2 diabetes and obesity development under different genetic backgrounds.Animal models and experimental medicine · 2023Article
- Intestinal cancer development in response to oral infection with high-fat diet-induced Type 2 diabetes (T2D) in collaborative cross mice under different host genetic background effects.Mammalian genome : official journal of the International Mammalian Genome Society · 2023Article
- A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins.Frontiers in genetics · 2022Article
- Identification of stress response proteins through fusion of machine learning models and statistical paradigms.Scientific reports · 2021Article
- EMCBOW-GPCR: A method for identifying G-protein coupled receptors based on word embedding and wordbooks.Computational and structural biotechnology journal · 2021Article
- A computational model for GPCR-ligand interaction prediction.Journal of integrative bioinformatics · 2020Article
- Classification and prediction of diabetes disease using machine learning paradigm.Health information science and systems · 2020Article
- Binding Characterization of GPCRs-Modulator by Molecular Complex Characterizing System (MCCS).ACS chemical neuroscience · 2020Article
- Blood Uric Acid Prediction With Machine Learning: Model Development and Performance Comparison.JMIR medical informatics · 2020Article
- Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework.Journal of diabetes research · 2020Article
- Prediction of G Protein-Coupled Receptors With CTDC Extraction and MRMD2.0 Dimension-Reduction Methods.Frontiers in bioengineering and biotechnology · 2020Article
- MPLs-Pred: Predicting Membrane Protein-Ligand Binding Sites Using Hybrid Sequence-Based Features and Ligand-Specific Models.International journal of molecular sciences · 2019Article
- Representation Learning for Class C G Protein-Coupled Receptors Classification.Molecules (Basel, Switzerland) · 2018Article
- AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest.Scientific reports · 2018Article
- Predicting Diabetes Mellitus With Machine Learning Techniques.Frontiers in genetics · 2018Article
- IonchanPred 2.0: A Tool to Predict Ion Channels and Their Types.International journal of molecular sciences · 2017Article
- Recent Advances in Conotoxin Classification by Using Machine Learning Methods.Molecules (Basel, Switzerland) · 2017Review
- OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models.Scientific reports · 2017Article
- High-Performance Prediction of Human Estrogen Receptor Agonists Based on Chemical Structures.Molecules (Basel, Switzerland) · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
G protein-coupled receptors (GPCRs) are the largest receptor superfamily. In this paper, we try to employ physical-chemical properties, which come from SVM-Prot, to represent GPCR. Random Forest was utilized as classifier for distinguishing them from other protein sequences. MEME suite was used to detect the most significant 10 conserved motifs of human GPCRs. In the testing datasets, the average accuracy was 91.61%, and the average AUC was 0.9282. MEME discovery analysis showed that many motifs aggregated in the seven hydrophobic helices transmembrane regions adapt to the characteristic of GPCRs. All of the above indicate that our machine-learning method can successfully distinguish GPCRs from non-GPCRs.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.