iDEF-PseRAAC: Identifying the Defensin Peptide by Using Reduced Amino Acid Composition Descriptor

Zuo, Yongchun; Chang, Yu; Huang, Shenghui; Zheng, Lei; Yang, Lei; Cao, Guifang

doi:10.1177/1176934319867088

Cited by 16 publications

(27 citation statements)

References 45 publications

(60 reference statements)

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“…Statistical learning theory specializes in machine learning with limited samples in practical application, and develops SVM [22]- [24], a general learning method. Because SVM is based on the principle of structural risk minimization, it shows great performance better than the existing methods, and has achieved lots of research results [25]- [28].…”

Section: A Prediction Performance Of Svm Classifiermentioning

confidence: 99%

Mining Key Regulators of Cell Reprogramming and Prediction Research Based on Deep Learning Neural Networks

Liu

et al. 2020

IEEE Access

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Deciphering the dynamic changes of core factors at different reprogramming stages plays an important role in elucidating the reprogramming mechanism of induced pluripotent stem cells (iPSCs) and improving their induction efficiency. The use of transcription factors (TFs) in combination with histone modification is vital to understand the multiple regulatory of pioneer factor. However, existing studies are not enough to consider the classification of stage-specific gene clusters from the perspective of multi-omic in the process of cell reprogramming. In this study, three stage-specific gene clusters of reprogramming initiation, maturation and stabilization phase were identified by using differential expression analysis. Considering the effects of regional binding preference, we further constructed a quantitative model on different genome regions (promoter, enhancer and enhancer subdivision region) by integrating the DNA binding profiles of Oct4 and three histone modifications (HMs). For promoter and enhancer regions, the receiver operating characteristic curve (Roc curve) of support vector machine (SVM) model was above 0.75 and predictive with the accuracy (Acc) about 66 ∼ 69%. But on enhancer subdivision region, the convolutional neural network (CNN) model we constructed showed more faithful predictive performance than the model on promoter and enhancer, which Roc curve area can reach 0.87. Taken together, our studies quantitatively reveal the cooperative effects of TFs and HMs on reprogramming stage-specific gene clusters, hoping to provide new sights in mining the key regulators of reprogramming.

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Section: A Prediction Performance Of Svm Classifiermentioning

confidence: 99%

Mining Key Regulators of Cell Reprogramming and Prediction Research Based on Deep Learning Neural Networks

Liu

et al. 2020

IEEE Access

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“…Different feature descriptors reflect the diverse properties of proteins [24]- [28]. These properties roughly divide into the following categories: amino acid composition (AAC) [29]- [33], pseudo amino acid composition (PseAAC) [34]- [36], physicochemical properties, evolutionary and gene ontology information [37]- [39]. Feature extraction selection will directly influence the model's efficiency and precision.…”

Section: B Feature Extractionmentioning

confidence: 99%

Identification of Type VI Effector Proteins Using a Novel Ensemble Classifier

Wang

Zhang

et al. 2020

IEEE Access

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The type VI secretion system (T6SS) delivers effector proteins (Type VI secretion system effectors, termed T6SEs) into neighboring target cells. Many human pathogens express T6SEs, including Vibrio cholera, Burkholderia spp., and Pseudomonas aeruginosa. T6SEs play vital roles in the competitive survival and pathogenesis of bacterial populations. Several machine-learning methods are able to distinguish T6SEs from non-T6SEs. However, we believe there is room for further development. Therefore, herein we propose a more powerful ensemble predictor for identifying T6SEs. Initially, we construct a benchmark dataset from existing studies and databases. Then we use k-separated-bigrams-PSSM (a type of feature encoding) to convert the protein sequences to mathematical vectors. A synthetic minority oversampling technique (SMOTE) is next employed to solve the training data imbalance problem. Finally, we employ a soft voting strategy to construct an integrated model combining six fine-tuned base classifiers. The model we propose performs excellently in terms of accuracy (ACC, 99.0%), Matthew's correlation coefficient (MCC, 97.8%), sensitivity (SN, 97.1%), and specificity (SP, 100%) in independent testing.

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“…Finally, on the one hand, the independent test set is used for testing performances of model; on the other hand, the web server is constructed with the trained model to provide two-stage prediction service. Kong and Zhang, 2019;Wang et al, 2019;Zhou et al, 2019;Zhu et al, 2019), gene ontology method (GO) (Camon et al, 2003;Wan et al, 2013;Zhou et al, 2017;Cheng et al, 2018), reduced amino acid (RAA) (Zuo et al, 2015(Zuo et al, , 2019Zheng et al, 2019). For example, Lee introduced the concept of n-gram (Chung et al, 2019), calculated the features in n-gram using binary location map, and used the feature selection method for multi feature fusion, which has achieved good results in the classification practice of seven kinds of AMPs.…”

Section: Introductionmentioning

confidence: 99%

Amino Acid Reduction Can Help to Improve the Identification of Antimicrobial Peptides and Their Functional Activities

et al. 2021

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Antimicrobial peptides (AMPs) are considered as potential substitutes of antibiotics in the field of new anti-infective drug design. There have been several machine learning algorithms and web servers in identifying AMPs and their functional activities. However, there is still room for improvement in prediction algorithms and feature extraction methods. The reduced amino acid (RAA) alphabet effectively solved the problems of simplifying protein complexity and recognizing the structure conservative region. This article goes into details about evaluating the performances of more than 5,000 amino acid reduced descriptors generated from 74 types of amino acid reduced alphabet in the first stage and the second stage to construct an excellent two-stage classifier, Identification of Antimicrobial Peptides by Reduced Amino Acid Cluster (iAMP-RAAC), for identifying AMPs and their functional activities, respectively. The results show that the first stage AMP classifier is able to achieve the accuracy of 97.21 and 97.11% for the training data set and independent test dataset. In the second stage, our classifier still shows good performance. At least three of the four metrics, sensitivity (SN), specificity (SP), accuracy (ACC), and Matthews correlation coefficient (MCC), exceed the calculation results in the literature. Further, the ANOVA with incremental feature selection (IFS) is used for feature selection to further improve prediction performance. The prediction performance is further improved after the feature selection of each stage. At last, a user-friendly web server, iAMP-RAAC, is established at http://bioinfor.imu.edu.cn/iampraac.

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iDEF-PseRAAC: Identifying the Defensin Peptide by Using Reduced Amino Acid Composition Descriptor

Cited by 16 publications

References 45 publications

Mining Key Regulators of Cell Reprogramming and Prediction Research Based on Deep Learning Neural Networks

Mining Key Regulators of Cell Reprogramming and Prediction Research Based on Deep Learning Neural Networks

Identification of Type VI Effector Proteins Using a Novel Ensemble Classifier

Amino Acid Reduction Can Help to Improve the Identification of Antimicrobial Peptides and Their Functional Activities

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