Prediction of Enzyme&amp;#8217;s Family Based on Protein-Protein Interaction Network

Niu, Bing; Lu, Yin; Lu, Jing; Chen, Fuxue; Zhao, Tong-Hui; Liu, Zhanmin; Huang, Tao; Zhang, Yuhui

doi:10.2174/157489361001150309122016

Cited by 8 publications

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“…where N i is the number of samples in ethnic group i and T i is the number of correctly predicted samples in ethnic group i . The total accuracy [ 50 , 51 ] was …”

Section: Methodsmentioning

confidence: 99%

Genetic differences among ethnic groups

2015

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BackgroundMany differences between different ethnic groups have been observed, such as skin color, eye color, height, susceptibility to some diseases, and response to certain drugs. However, the genetic bases of such differences have been under-investigated. Since the HapMap project, large-scale genotype data from Caucasian, African and Asian population samples have been available. The project found that these populations were located in different areas of the PCA (Principal Component Analysis) plot. However, as an unsupervised method, PCA does not measure the differences in each single nucleotide polymorphism (SNP) among populations.ResultsWe applied an advanced mutual information-based feature selection method to detect associations between SNP status and ethnic groups using the latest HapMap Phase 3 release version 3, which included more sub-populations. A total of 299 SNPs were identified, and they can accurately predicted the ethnicity of all HapMap populations. The 10-fold cross validation accuracy of the SMO (sequential minimal optimization) model on training dataset was 0.901, and the accuracy on independent test dataset was 0.895.ConclusionsIn-depth functional analysis of these SNPs and their nearby genes revealed the genetic bases of skin and eye color differences among populations.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2328-0) contains supplementary material, which is available to authorized users.

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“…where N i is the number of samples in ethnic group i and T i is the number of correctly predicted samples in ethnic group i . The total accuracy [ 50 , 51 ] was …”

Section: Methodsmentioning

confidence: 99%

Genetic differences among ethnic groups

2015

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“…A protein-protein interaction (PPI) network-based approach for predicting enzyme families was created in this study. As an example, the jackknife test had a success rate of 62.86 percent in identifying enzyme family class ( Niu et al, 2015 ). The positive results suggest that the predictor described in this work might be beneficial in enzyme research as shown in table 7 .…”

Section: Resultsmentioning

confidence: 99%

Identification of Enzymes-specific Protein Domain Based on DDE, and Convolutional Neural Network

et al. 2021

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Predicting the protein sequence information of enzymes and non-enzymes is an important but a very challenging task. Existing methods use protein geometric structures only or protein sequences alone to predict enzymatic functions. Thus, their prediction results are unsatisfactory. In this paper, we propose a novel approach for predicting the amino acid sequences of enzymes and non-enzymes via Convolutional Neural Network (CNN). In CNN, the roles of enzymes are predicted from multiple sides of biological information, including information on sequences and structures. We propose the use of two-dimensional data via 2DCNN to predict the proteins of enzymes and non-enzymes by using the same fivefold cross-validation function. We also use an independent dataset to test the performance of our model, and the results demonstrate that we are able to solve the overfitting problem. We used the CNN model proposed herein to demonstrate the superiority of our model for classifying an entire set of filters, such as 32, 64, and 128 parameters, with the fivefold validation test set as the independent classification. Via the Dipeptide Deviation from Expected Mean (DDE) matrix, mutation information is extracted from amino acid sequences and structural information with the distance and angle of amino acids is conveyed. The derived feature maps are then encoded in DDE exploitation. The independent datasets are then compared with other two methods, namely, GRU and XGBOOST. All analyses were conducted using 32, 64 and 128 filters on our proposed CNN method. The cross-validation datasets achieved an accuracy score of 0.8762%, whereas the accuracy of independent datasets was 0.7621%. Additional variables were derived on the basis of ROC AUC with fivefold cross-validation was achieved score is 0.95%. The performance of our model and that of other models in terms of sensitivity (0.9028%) and specificity (0.8497%) was compared. The overall accuracy of our model was 0.9133% compared with 0.8310% for the other model.

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