Predicting Protein Subcellular Localizations for Gram-Negative Bacteria Using DP-PSSM and Support Vector Machines

Juan, Eric Y. T.; Li, W.J.; Jhang, Jia-Yao; Chiu, Chih-Hui

doi:10.1109/cisis.2009.194

Cited by 10 publications

(5 citation statements)

References 43 publications

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“…For physicochemical property-based features, AAINDEX features outperformed EBGW features across all the three species in terms of ACC, F-value, MCC and AUC value. Surprisingly, the PSSM-based features showed poor performance, indicating a limited contribution to the prediction and a somewhat contrasting observation different from previous prediction studies of other protein attributes for which PSSM-based features are often considered essential [28,36,41,42,[68][69][70][71][72]. A possible reason might be that part of the PSSM matrix used for describing the segment is far less informative than the complete PSSM matrix generated from full-length protein sequences in previous studies, thus making PSSM-based features unable to extract enough useful patterns and characteristics.…”

Section: Performance Evaluation Of Different Feature Encoding Schemescontrasting

confidence: 59%

Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework

Zhang

Xie

Wang

et al. 2018

Briefings in Bioinformatics

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As a newly discovered post-translational modification (PTM), lysine malonylation (Kmal) regulates a myriad of cellular processes from prokaryotes to eukaryotes and has important implications in human diseases. Despite its functional significance, computational methods to accurately identify malonylation sites are still lacking and urgently needed. In particular, there is currently no comprehensive analysis and assessment of different features and machine learning (ML) methods that are required for constructing the necessary prediction models. Here, we review, analyze and compare 11 different feature encoding methods, with the goal of extracting key patterns and characteristics from residue sequences of Kmal sites. We identify optimized feature sets, with which four commonly used ML methods (random forest, support vector machines, K-nearest neighbor and logistic regression) and one recently proposed [Light Gradient Boosting Machine (LightGBM)] are trained on data from three species, namely, Escherichia coli, Mus musculus and Homo sapiens, and compared using randomized 10-fold cross-validation tests. We show that integration of the single method-based models through ensemble learning further improves the prediction performance and model robustness on the independent test. When compared to the existing state-of-the-art predictor, MaloPred, the optimal ensemble models were more accurate for all three species (AUC: 0.930, 0.923 and 0.944 for E. coli, M. musculus and H. sapiens, respectively). Using the ensemble models, we developed an accessible online predictor, kmal-sp, available at http://kmalsp.erc.monash.edu/. We hope that this comprehensive survey and the proposed strategy for building more accurate models can serve as a useful guide for inspiring future developments of computational methods for PTM site prediction, expedite the discovery of new malonylation and other PTM types and facilitate hypothesis-driven experimental validation of novel malonylated substrates and malonylation sites.

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Section: Performance Evaluation Of Different Feature Encoding Schemescontrasting

confidence: 59%

Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework

Zhang

Xie

Wang

et al. 2018

Briefings in Bioinformatics

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“…(v) Pse-PSSM (Chou and Shen, 2007 ) is similar to PseAAC and encodes the PSSM of proteins with different lengths using a uniform length matrix. (iv) DP-PSSM (Juan et al, 2009 ), a protein descriptor based on similarity, gets the hidden sequential order information and can avoid cancellation of positive or negative terms in the average process. As a result, we obtained a 400-dimensional vector for each sequence.…”

Section: Methodsmentioning

confidence: 99%

T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm

et al. 2020

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Type IV secreted effectors (T4SEs) can be translocated into the cytosol of host cells via type IV secretion system (T4SS) and cause diseases. However, experimental approaches to identify T4SEs are time-and resource-consuming, and the existing computational tools based on machine learning techniques have some obvious limitations such as the lack of interpretability in the prediction models. In this study, we proposed a new model, T4SE-XGB, which uses the eXtreme gradient boosting (XGBoost) algorithm for accurate identification of type IV effectors based on optimal features based on protein sequences. After trying 20 different types of features, the best performance was achieved when all features were fed into XGBoost by the 5-fold cross validation in comparison with other machine learning methods. Then, the ReliefF algorithm was adopted to get the optimal feature set on our dataset, which further improved the model performance. T4SE-XGB exhibited highest predictive performance on the independent test set and outperformed other published prediction tools. Furthermore, the SHAP method was used to interpret the contribution of features to model predictions. The identification of key features can contribute to improved understanding of multifactorial contributors to host-pathogen interactions and bacterial pathogenesis. In addition to type IV effector prediction, we believe that the proposed framework can provide instructive guidance for similar studies to construct prediction methods on related biological problems. The data and source code of this study can be freely accessed at https://github.com/CT001002/T4SE-XGB.

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“…The predictor trained by the training set without and with SMOTE both achieved promising results, especially the latter, as evaluated by an independent test set. SMOTE improved the performance of all majority feature encodings, except Directional Property-PSSM (DP_PSSM) [104]. The comparison is shown in Figure 6.…”

Section: Smote Effectsmentioning

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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Predicting Protein Subcellular Localizations for Gram-Negative Bacteria Using DP-PSSM and Support Vector Machines

Cited by 10 publications

References 43 publications

Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework

Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework

T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm

Identification of Type VI Effector Proteins Using a Novel Ensemble Classifier

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