Abstract
Background
Classification of certain proteins with specific functions is momentous for biological research. Encoding approaches of protein sequences for feature extraction play an important role in protein classification. Many computational methods (namely classifiers) are used for classification on protein sequences according to various encoding approaches. Commonly, protein sequences keep certain labels corresponding to different categories of biological functions (e.g., bacterial type IV secreted effectors or not), which makes protein prediction a fantasy. As to protein prediction, a kernel set of protein sequences kee** certain labels certified by biological experiments should be existent in advance. However, it has been hardly ever seen in prevailing researches. Therefore, unsupervised learning rather than supervised learning (e.g. classification) should be considered. As to protein classification, various classifiers may help to evaluate the effectiveness of different encoding approaches. Besides, variable selection from an encoded feature representing protein sequences is an important issue that also needs to be considered.
Results
Focusing on the latter problem, we propose a new method for variable selection from an encoded feature representing protein sequences. Taking a benchmark dataset containing 1947 protein sequences as a case, experiments are made to identify bacterial type IV secreted effectors (T4SE) from protein sequences, which are composed of 399 T4SE and 1548 non-T4SE. Comparable and quantified results are obtained only using certain components of the encoded feature, i.e., position-specific scoring matix, and that indicates the effectiveness of our method.
Conclusions
Certain variables other than an encoded feature they belong to do work for discrimination between different types of proteins. In addition, ensemble classifiers with an automatic assignment of different base classifiers do achieve a better classification result.
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Background
Feature extraction from protein sequences plays an important role in protein classification [1,2,3,4] of many areas, such as identification of plant pentatricopeptide repeat coding protein [5], prediction of bacterial type IV secreted effectors [Variable selection Variable selection is accomplished at the sixth step. In each dimension, the established ensemble classifier is applied to the testing samples. The accuracy (Acc) expressed in Eq. (2) and the area under curve (AUC) of the receiver operating characteristic (ROC) are calculated. Accordingly, a line chart is obtained with its horizontal and vertical coordinates corresponding to the variable indices in their descending order and the corresponding Accs and AUCs in different dimensions. A dimension threshold can be made when Accs and AUCs are kee** almost the same with dimension incrementally increasing. Thus, the variables that really help to recognize proteins with specific functions are selected from the encoded feature. Evaluation metrics are made to estimate the effectiveness of selected variables at the seventh step. The classification error rate is expressed as follows, where TP, TN, FP and FN represent the number of true positive, true negative, false positive and false negative, respectively. On the contrary, Acc is shown as follows, Besides, we choose four widely used quantitative measurements. The confusion matrix illustrates TP, TN, FP and FN together. Besides, Precision and Recall are computed as follows, In addition, \(F1-measure\) is a harmonic average of Precision and Recall, which is expressed as Moreover, the ROC and AUC are also provided here as qualitative measurements.Measure
Availability of data and materials
The public dataset analysed during the current study is available in reference [51], and can be downloaded from the website https://github.com/LoopGan/Effective-prediction-of-bacterial-type-IV-secreted-effectors.
Abbreviations
- Acc::
-
Accuracy;
- AUC::
-
Area under curve;
- DTC::
-
Decision tree classifier;
- GBM::
-
Gradient boosting machine;
- kNN::
-
k-nearest-neighbor;
- LDA::
-
Linear discriminant analysis;
- LR::
-
Logistic regression;
- MLP::
-
Multi-layer perceptron;
- NB::
-
Naive bayesian;
- PseAAC::
-
Pseudo-amino acid composition;
- PSI-BLAST::
-
Position-specific iterated blast;
- PSSM::
-
Position-specific scoring matrix;
- RF::
-
Random forest;
- ROC::
-
Receiver operating characteristic;
- SVM::
-
Support vector machine;
- T4SE::
-
Type IV secreted effectors
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Acknowledgements
This work is derived from Scientific Research Project Supported by Enterprise Suzhou Dachen Medical Technology Co., Ltd.
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This work has been supported by the financial support of This work has been supported by the financial support of Natural Science Foundation of Heilongjiang Province (No. LH2020F002). The funding body of Fundamental Research Funds for Natural Science Foundation of Heilongjiang Province played an important role in the design of the study, collection, analysis and interpretation of data and in writing the manuscript.
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X.D.Z conceived the general research and supervised it. J.Z performed the research and were the principal developers. D.L.L and L.X.L analyzed the data. D.N.K, M.A.A.A and X.D.Z wrote and revised the manuscript. All authors read and approved the final manuscript.
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Zhang, J., Lv, L., Lu, D. et al. Variable selection from a feature representing protein sequences: a case of classification on bacterial type IV secreted effectors. BMC Bioinformatics 21, 480 (2020). https://doi.org/10.1186/s12859-020-03826-6
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DOI: https://doi.org/10.1186/s12859-020-03826-6