Conotoxin Superfamily Prediction Using Diffusion Maps Dimensionality Reduction and Subspace Classifier

Yin, Jiang-Bo; Fan, Yongxian; Shen, Hong‐Bin

doi:10.2174/138920311796957702

Cited by 20 publications

(19 citation statements)

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“…Although many dimensionality reduction techniques such as principal component analysis (PCA) [10,11], diffusion Maps [12] and minimal-redundancy-maximal-relevance (mRMR) [13,14] have been proposed to perform feature selection, none of them concerned the statistical significance of the features. According to this, we proposed the binomial distribution to investigate the statistical significance of each tripeptide and the optimal the feature set.…”

Section: Resultsmentioning

confidence: 99%

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Identifying the Subfamilies of Voltage-Gated Potassium Channels Using Feature Selection Technique

Liu

Deng

Chen

et al. 2014

IJMS

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Voltage-gated K+ channel (VKC) plays important roles in biology procession, especially in nervous system. Different subfamilies of VKCs have different biological functions. Thus, knowing VKCs’ subfamilies has become a meaningful job because it can guide the direction for the disease diagnosis and drug design. However, the traditional wet-experimental methods were costly and time-consuming. It is highly desirable to develop an effective and powerful computational tool for identifying different subfamilies of VKCs. In this study, a predictor, called iVKC-OTC, has been developed by incorporating the optimized tripeptide composition (OTC) generated by feature selection technique into the general form of pseudo-amino acid composition to identify six subfamilies of VKCs. One of the remarkable advantages of introducing the optimized tripeptide composition is being able to avoid the notorious dimension disaster or over fitting problems in statistical predictions. It was observed on a benchmark dataset, by using a jackknife test, that the overall accuracy achieved by iVKC-OTC reaches to 96.77% in identifying the six subfamilies of VKCs, indicating that the new predictor is promising or at least may become a complementary tool to the existing methods in this area. It has not escaped our notice that the optimized tripeptide composition can also be used to investigate other protein classification problems.

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Section: Resultsmentioning

confidence: 99%

“…Among them, the jackknife test method makes best use of the data, involves no random sub-sampling and achieves unique results [6,22]. It has been widely and increasingly adopted in bioinformatics [5,12,13,14,23,24,25]. Therefore, the jackknife cross-validation was used in all procession of feature selection and parameter optimization of SVM.…”

Section: Methodsmentioning

confidence: 99%

Identifying the Subfamilies of Voltage-Gated Potassium Channels Using Feature Selection Technique

Liu

Deng

Chen

et al. 2014

IJMS

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“…Some algorithms such as principal component analysis [46], minimal-redundancy-maximal-relevance (mRMR) [31], diffusion Maps [71] and the analysis of variance (ANOVA) [40] have been proposed for reducing the dimensionality. This study will introduce a new algorithm based on binomial distribution to optimize the feature sets [25].…”

Section: Feature Selectionmentioning

confidence: 99%

Predicting Subchloroplast Locations of Proteins Based on the General Form of Chou's Pseudo Amino Acid Composition: Approached From Optimal Tripeptide Composition

et al. 2013

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Chloroplasts are organelles found in plant cells that conduct photosynthesis. The subchloroplast locations of proteins are correlated with their functions. With the availability of a great number of protein data, it is highly desired to develop a computational method to predict the subchloroplast locations of chloroplast proteins. In this study, we proposed a novel method to predict subchloroplast locations of proteins using tripeptide compositions. It first used the binomial distribution to optimize the feature sets. Then the support vector machine was selected to perform the prediction of subchloroplast locations of proteins. The proposed method was tested on a reliable and rigorous dataset including 259 chloroplast proteins with sequence identity ≤ 25%. In the jack-knife cross-validation, 92.21% envelope proteins, 93.20% thylakoid membrane, 52.63% thylakoid lumen and 85.00% stroma can be correctly identified. The overall accuracy achieves 88.03% which is higher than that of other models. Based on this method, a predictor called ChloPred has been built and can be freely available §, ¶ Corresponding authors. 1350003-1 Int. J. Biomath. 2013.06. Downloaded from www.worldscientific.com by NANYANG TECHNOLOGICAL UNIVERSITY on 08/20/15. For personal use only. H. Lin et al.from http://cobi.uestc.edu.cn/people/hlin/tools/ChloPred/. The predictor will provide important information for theoretical and experimental research of chloroplast proteins.

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“…The neurotoxins and bacterial toxins derived from Swiss-Prot were predicted by Feed-forwarded Neural Network (FNN), Partial Recurrent Neural Network (RNN) and Support Vector Machine (SVM) 21–23 . Four kinds of conotoxin superfamilies for 116 conotoxin sequences were predicted by ISort predictor, Least Hamming, Multi-class SVMs, one-versus-rest SVMs 24 , modified Mahalanobis discriminant 25 , and dHKNN 26 . Four conotoxin superfamilies for 261 conotoxin sequences that collected from Swiss-Prot were predicted by SVM 27 .…”

Section: Introductionmentioning

confidence: 99%

Prediction of presynaptic and postsynaptic neurotoxins by combining various Chou’s pseudo components

Huo

Wang

et al. 2017

Sci Rep

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Presynaptic and postsynaptic neurotoxins are two groups of neurotoxins. Identification of presynaptic and postsynaptic neurotoxins is an important work for numerous newly found toxins. It is both costly and time consuming to determine these two neurotoxins by experimental methods. As a complement, using computational methods for predicting presynaptic and postsynaptic neurotoxins could provide some useful information in a timely manner. In this study, we described four algorithms for predicting presynaptic and postsynaptic neurotoxins from sequence driven features by using Increment of Diversity (ID), Multinomial Naive Bayes Classifier (MNBC), Random Forest (RF), and K-nearest Neighbours Classifier (IBK). Each protein sequence was encoded by pseudo amino acid (PseAA) compositions and three biological motif features, including MEME, Prosite and InterPro motif features. The Maximum Relevance Minimum Redundancy (MRMR) feature selection method was used to rank the PseAA compositions and the 50 top ranked features were selected to improve the prediction accuracy. The PseAA compositions and three kinds of biological motif features were combined and 12 different parameters that defined as P1-P12 were selected as the input parameters of ID, MNBC, RF, and IBK. The prediction results obtained in this study were significantly better than those of previously developed methods.

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Conotoxin Superfamily Prediction Using Diffusion Maps Dimensionality Reduction and Subspace Classifier

Cited by 20 publications

References 0 publications

Identifying the Subfamilies of Voltage-Gated Potassium Channels Using Feature Selection Technique

Identifying the Subfamilies of Voltage-Gated Potassium Channels Using Feature Selection Technique

Predicting Subchloroplast Locations of Proteins Based on the General Form of Chou's Pseudo Amino Acid Composition: Approached From Optimal Tripeptide Composition

Prediction of presynaptic and postsynaptic neurotoxins by combining various Chou’s pseudo components

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