ArticleThe Journal of membrane biology2013
A multilabel model based on Chou's pseudo-amino acid composition for identifying membrane proteins with both single and multiple functional types.
Article in The Journal of membrane biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Some illuminating remarks on molecular genetics and genomics as well as drug development.Molecular genetics and genomics : MGG · 2020Review
- 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
- iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals.Oncotarget · 2017Article
- A Treatise to Computational Approaches Towards Prediction of Membrane Protein and Its Subtypes.The Journal of membrane biology · 2017Review
- Benchmark data for identifying multi-functional types of membrane proteins.Data in brief · 2016Article
- Classifying Multifunctional Enzymes by Incorporating Three Different Models into Chou's General Pseudo Amino Acid Composition.The Journal of membrane biology · 2016Article
- Predicting the Functional Types of Singleplex and Multiplex Eukaryotic Membrane Proteins via Different Models of Chou's Pseudo Amino Acid Compositions.The Journal of membrane biology · 2016Article
- A Prediction Model for Membrane Proteins Using Moments Based Features.BioMed research international · 2016Article
- TargetFreeze: Identifying Antifreeze Proteins via a Combination of Weights using Sequence Evolutionary Information and Pseudo Amino Acid Composition.The Journal of membrane biology · 2015Article
- iMem-Seq: A Multi-label Learning Classifier for Predicting Membrane Proteins Types.The Journal of membrane biology · 2015Article
- A new multi-label classifier in identifying the functional types of human membrane proteins.The Journal of membrane biology · 2015Article
- A multi-label classifier for prediction membrane protein functional types in animal.The Journal of membrane biology · 2014Article
- iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking.International journal of molecular sciences · 2014Article
- PseAAC-General: fast building various modes of general form of Chou's pseudo-amino acid composition for large-scale protein datasets.International journal of molecular sciences · 2014Article
- Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection.Bioinformatics (Oxford, England) · 2014Article
- iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components.International journal of molecular sciences · 2014Article
- iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition.BioMed research international · 2014Article
- iEzy-drug: a web server for identifying the interaction between enzymes and drugs in cellular networking.BioMed research international · 2013Article
- iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking.PloS one · 2013Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting membrane protein type is a meaningful task because this kind of information is very useful to explain the function of membrane proteins. Due to the explosion of new protein sequences discovered, it is highly desired to develop efficient computation tools for quickly and accurately predicting the membrane type for a given protein sequence. Even though several membrane predictors have been developed, they can only deal with the membrane proteins which belong to the single membrane type. The fact is that there are membrane proteins belonging to two or more than two types. To solve this problem, a system for predicting membrane protein sequences with single or multiple types is proposed. Pseudo-amino acid composition, which has proven to be a very efficient tool in representing protein sequences, and a multilabel KNN algorithm are used to compose this prediction engine. The results of this initial study are encouraging.
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