ArticleEURASIP journal on bioinformatics & systems biology2008
A time-series-based feature extraction approach for prediction of protein structural class.
Article in EURASIP journal on bioinformatics & systems biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A novel method for dipper/non-dipper pattern classification in hypertensive and non-diabetic patients.Technology and health care : official journal of the European Society for Engineering and Medicine · 2019Article
- A Tool Preference Choice Method for RNA Secondary Structure Prediction by SVM with Statistical Tests.Evolutionary bioinformatics online · 2013Article
- Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences.BMC bioinformatics · 2009Article
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Authors and funding
3 authors.
Funding
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Abstract
This paper presents a novel feature vector based on physicochemical property of amino acids for prediction protein structural classes. The proposed method is divided into three different stages. First, a discrete time series representation to protein sequences using physicochemical scale is provided. Later on, a wavelet-based time-series technique is proposed for extracting features from mapped amino acid sequence and a fixed length feature vector for classification is constructed. The proposed feature space summarizes the variance information of ten different biological properties of amino acids. Finally, an optimized support vector machine model is constructed for prediction of each protein structural class. The proposed approach is evaluated using leave-one-out cross-validation tests on two standard datasets. Comparison of our result with existing approaches shows that overall accuracy achieved by our approach is better than exiting methods.
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