ArticleBiophysical journal2003
Support vector machines for predicting membrane protein types by using functional domain composition.
Article in Biophysical journal, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.
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69 citing papers in PubMed.
- Ion channel classification through machine learning and protein language model embeddings.Journal of integrative bioinformatics · 2024Article
- Ensemble Learning for Hormone Binding Protein Prediction: A Promising Approach for Early Diagnosis of Thyroid Hormone Disorders in Serum.Diagnostics (Basel, Switzerland) · 2023Article
- Identification of intelligence-related proteins through a robust two-layer predictor.Communicative & integrative biology · 2022Article
- iMPT-FDNPL: Identification of Membrane Protein Types with Functional Domains and a Natural Language Processing Approach.Computational and mathematical methods in medicine · 2021Article
- sgRNA-PSM: Predict sgRNAs On-Target Activity Based on Position-Specific Mismatch.Molecular therapy. Nucleic acids · 2020Article
- Is There Any Sequence Feature in the RNA Pseudouridine Modification Prediction Problem?Molecular therapy. Nucleic acids · 2020Article
- Some illuminating remarks on molecular genetics and genomics as well as drug development.Molecular genetics and genomics : MGG · 2020Review
- Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach.Frontiers in genetics · 2020Article
- Protein sequence information extraction and subcellular localization prediction with gapped k-Mer method.BMC bioinformatics · 2019Article
- A Linear Regression Predictor for Identifying NMolecular therapy. Nucleic acids · 2019Article
- Gap Junction Channels of Innexins and Connexins: Relations and Computational Perspectives.International journal of molecular sciences · 2019Review
- Employing a novel 2-gram subgroup intra pattern (2GSIP) with stacked auto encoder for membrane protein classification.Molecular biology reports · 2019Article
- Predicting membrane proteins and their types by extracting various sequence features into Chou's general PseAAC.Molecular biology reports · 2018Article
- An Efficient Classifier for Alzheimer's Disease Genes Identification.Molecules (Basel, Switzerland) · 2018Article
- M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning.Molecular therapy. Nucleic acids · 2018Article
- Prediction of subcellular location of apoptosis proteins by incorporating PsePSSM and DCCA coefficient based on LFDA dimensionality reduction.BMC genomics · 2018Article
- SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins.International journal of molecular sciences · 2018Article
- Accurate prediction of subcellular location of apoptosis proteins combining Chou's PseAAC and PsePSSM based on wavelet denoising.Oncotarget · 2017Article
- PSFM-DBT: Identifying DNA-Binding Proteins by Combing Position Specific Frequency Matrix and Distance-Bigram Transformation.International journal of molecular sciences · 2017Article
- Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression.Oncotarget · 2017Article
9 more citing papers are in PubMed but not listed here.
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Membrane proteins are generally classified into the following five types: 1), type I membrane protein; 2), type II membrane protein; 3), multipass transmembrane proteins; 4), lipid chain-anchored membrane proteins; and 5), GPI-anchored membrane proteins. In this article, based on the concept of using the functional domain composition to define a protein, the Support Vector Machine algorithm is developed for predicting the membrane protein type. High success rates are obtained by both the self-consistency and jackknife tests. The current approach, complemented with the powerful covariant discriminant algorithm based on the pseudo-amino acid composition that has incorporated quasi-sequence-order effect as recently proposed by K. C. Chou (2001), may become a very useful high-throughput tool in the area of bioinformatics and proteomics.
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