ArticleScientific reports2016
Identification of apolipoprotein using feature selection technique.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
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- ApoPred: Identification of Apolipoproteins and Their Subfamilies With Multifarious Features.Frontiers in cell and developmental biology · 2020Article
- Semi-Supervised Maximum Discriminative Local Margin for Gene Selection.Scientific reports · 2018Article
- iRNA-3typeA: Identifying Three Types of Modification at RNA's Adenosine Sites.Molecular therapy. Nucleic acids · 2018Article
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- iRSpot-Pse6NC: Identifying recombination spots inInternational journal of biological sciences · 2018Article
- Identification of Inhibitors of MMPS Enzymes via a Novel Computational Approach.International journal of biological sciences · 2018Article
- HBPred: a tool to identify growth hormone-binding proteins.International journal of biological sciences · 2018Article
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- Analysis of Bioactive Amino Acids from Fish Hydrolysates with a New Bioinformatic Intelligent System Approach.Scientific reports · 2017Article
- Recent Advances in Conotoxin Classification by Using Machine Learning Methods.Molecules (Basel, Switzerland) · 2017Review
- Predicting Presynaptic and Postsynaptic Neurotoxins by Developing Feature Selection Technique.BioMed research international · 2017Article
- PSBinder: A Web Service for Predicting Polystyrene Surface-Binding Peptides.BioMed research international · 2017Article
- A novel feature ranking method for prediction of cancer stages using proteomics data.PloS one · 2017Article
- Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Apolipoprotein is a kind of protein which can transport the lipids through the lymphatic and circulatory systems. The abnormal expression level of apolipoprotein always causes angiocardiopathy. Thus, correct recognition of apolipoprotein from proteomic data is very crucial to the comprehension of cardiovascular system and drug design. This study is to develop a computational model to predict apolipoproteins. In the model, the apolipoproteins and non-apolipoproteins were collected to form benchmark dataset. On the basis of the dataset, we extracted the g-gap dipeptide composition information from residue sequences to formulate protein samples. To exclude redundant information or noise, the analysis of various (ANOVA)-based feature selection technique was proposed to find out the best feature subset. The support vector machine (SVM) was selected as discrimination algorithm. Results show that 96.2% of sensitivity and 99.3% of specificity were achieved in five-fold cross-validation. These findings open new perspectives to improve apolipoproteins prediction by considering the specific dipeptides. We expect that these findings will help to improve drug development in anti-angiocardiopathy disease.
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