PredT4SE-Stack: Prediction of Bacterial Type IV Secreted Effectors From Protein Sequences Using a Stacked Ensemble Method

Abstract

Gram-negative bacteria use various secretion systems to deliver their secreted effectors. Among them, type IV secretion system exists widely in a variety of bacterial species, and secretes type IV secreted effectors (T4SEs), which play vital roles in host-pathogen interactions. However, experimental approaches to identify T4SEs are time- and resource-consuming. In the present study, we aim to develop an in silico stacked ensemble method to predict whether a protein is an effector of type IV secretion system or not based on its sequence information. The protein sequences were encoded by the feature of position specific scoring matrix (PSSM)-composition by summing rows that correspond to the same amino acid residues in PSSM profiles. Based on the PSSM-composition features, we develop a stacked ensemble model PredT4SE-Stack to predict T4SEs, which utilized an ensemble of base-classifiers implemented by various machine learning algorithms, such as support vector machine, gradient boosting machine, and extremely randomized trees, to generate outputs for the meta-classifier in the classification system. Our results demonstrated that the framework of PredT4SE-Stack was a feasible and effective way to accurately identify T4SEs based on protein sequence information. The datasets and source code of PredT4SE-Stack are freely available at http://xbioinfo.sjtu.edu.cn/PredT4SE_Stack/index.php.

6mA-RicePred: A Method for Identifying DNA N6-Methyladenine Sites in the Rice Genome Based on Feature Fusion

“…SVM is a widely used machine learning algorithm (Ding and Li, 2015;Li et al, 2015;Zeng et al, 2017;Ding et al, 2017a;Zhang et al, 2019;Tan et al, 2019a) and was used in this study to identify 6mA sites in the rice genome. SVM is also widely used in bioinformatics fields (Zou et al, 2016b;Wang et al, 2018;Wei et al, 2018;Xiong et al, 2018;Zeng et al, 2018a;Xu et al, 2018a;Xu et al, 2018b;Xu et al, 2018c;Li et al, 2019). Our experiments showed that SVM was more suitable for the purposes of the present study than were the other algorithms.…”

RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites

“…For the ROC curve, 1-specificity was plotted on the horizontal axis, and sensitivity on the vertical axis. LOO, K-Fold cross-validation, and independent testing are the most widely used methods for predictor evaluation (Mrozek et al, 2015;Cao and Cheng, 2016;Chen et al, 2017Chen et al, , 2018aChen et al, , 2019bPan et al, 2017;He et al, 2018He et al, , 2019Jiang et al, 2018;Xiong et al, 2018;Yu et al, 2018;Zhang et al, 2018;Ding et al, 2019;Feng et al, 2019;Kong and Zhang, 2019;Li and Liu, 2019;Lv et al, 2019a;Manavalan et al, 2019;Shan et al, 2019;Wang et al, 2019a;Wei et al, 2019a,b;Xu et al, 2019;Yu and Dai, 2019). That is the general machine learning evaluation methods (training, validation and testing) are used for optimized model evaluation.…”

Predicting ATP-Binding Cassette Transporters Using the Random Forest Method

“…This method extracts 188 features based on protein sequence information and physical and chemical properties. Previous researchers used the composition-position of proteins and their physical-chemical properties independently to extract protein features (Dubchak et al, 1995;Ding and Dubchak, 2001;Shen et al, 2017Shen et al, , 2019Wang et al, 2017;Yu et al, 2017;Liu et al, 2018;Qiao et al, 2018;Xiong et al, 2018;Zhang et al, 2018a,b;Zou et al, 2019). In 2003, Cai et al first combined amino acid sequences with their physicochemical properties to finish feature extraction (Cai et al, 2003).…”