Sequence-Based Prediction of RNA-Binding Proteins Using Random Forest with Minimum Redundancy Maximum Relevance Feature Selection

Ma, Xin; Guo, Jing; Sun, Xiao

doi:10.1155/2015/425810

Cited by 23 publications

(11 citation statements)

References 43 publications

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“…In this study, the mutual method of minimum Redundancy Maximum Relevance (mRMR) was applied to select features (http://penglab.janelia.org/proj/mRMR/)20212223. We selected 50 features which had the maximum relevance to the classifier and minimum redundancy to the former features.…”

Section: Resultsmentioning

confidence: 99%

Mal-Lys: prediction of lysine malonylation sites in proteins integrated sequence-based features with mRMR feature selection

Ding

et al. 2016

Sci Rep

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Lysine malonylation is an important post-translational modification (PTM) in proteins, and has been characterized to be associated with diseases. However, identifying malonyllysine sites still remains to be a great challenge due to the labor-intensive and time-consuming experiments. In view of this situation, the establishment of a useful computational method and the development of an efficient predictor are highly desired. In this study, a predictor Mal-Lys which incorporated residue sequence order information, position-specific amino acid propensity and physicochemical properties was proposed. A feature selection method of minimum Redundancy Maximum Relevance (mRMR) was used to select optimal ones from the whole features. With the leave-one-out validation, the value of the area under the curve (AUC) was calculated as 0.8143, whereas 6-, 8- and 10-fold cross-validations had similar AUC values which showed the robustness of the predictor Mal-Lys. The predictor also showed satisfying performance in the experimental data from the UniProt database. Meanwhile, a user-friendly web-server for Mal-Lys is accessible at http://app.aporc.org/Mal-Lys/.

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Section: Resultsmentioning

confidence: 99%

Mal-Lys: prediction of lysine malonylation sites in proteins integrated sequence-based features with mRMR feature selection

Ding

et al. 2016

Sci Rep

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“…The following five types of performance evaluation indicators are used to evaluate the effect of the proposed method for ECG J wave detection [ 44 , 46 , 47 ]:

where true positive (TP) and false negative (FN) stand for the number of heartbeats of J-positive which have been classified correctly and incorrectly, respectively, while true negative (TN) and false positive (FP) stand for the number of heartbeats of J-negative which have been classified correctly and incorrectly, respectively. An ideal classification system should have lowered both FN and FP, so that it achieves high Se, high Sp, and high ACC as well as high MCC.…”

Section: Resultsmentioning

confidence: 99%

Autodetection of J Wave Based on Random Forest with Synchrosqueezed Wavelet Transform

Zhao

et al. 2018

BioMed Research International

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J wave is the bulge generated in the descending slope of the terminal portion of the QRS complex in the electrocardiogram. The presence of J wave may lead to sudden death. However, the diagnosis of J wave variation only depends on doctor's clinical experiences at present and missed diagnosis is easy to occur. In this paper, a new method is proposed to realize the automatic detection of J wave. First, the synchrosqueezed wavelet transform is used to obtain the precise time-frequency information of the ECG. Then, the inverse transformation of SST is computed to get the intrinsic mode function of the ECG. At last, the time-frequency features and SST-based and the entropy features based on modes are fed to Random forest to realize the automatic detection of J wave. As the experimental results shown, the proposed method has achieved the highest accuracy, sensitivity, and specificity compared with existing techniques.

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“…Here, we selected the mRMR method, proposed by Peng et al [ 23 ], because it is a widely used feature selection method and deemed an excellent method for analyzing the importance of features. To date, it has been applied in several biological problems [ [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] ].…”

Section: Methodsmentioning

confidence: 99%

Classification of Widely and Rarely Expressed Genes with Recurrent Neural Network

Lei

Pan

Zhang

et al. 2019

Computational and Structural Biotechnology Journal

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A tissue-specific gene expression shapes the formation of tissues, while gene expression changes reflect the immune response of the human body to environmental stimulations or pressure, particularly in disease conditions, such as cancers. A few genes are commonly expressed across tissues or various cancers, while others are not. To investigate the functional differences between widely and rarely expressed genes, we defined the genes that were expressed in 32 normal tissues/cancers (i.e., called widely expressed genes; FPKM >1 in all samples) and those that were not detected (i.e., called rarely expressed genes; FPKM <1 in all samples) based on the large gene expression data set provided by Uhlen et al. Each gene was encoded using the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment scores. Minimum redundancy maximum relevance (mRMR) was used to measure and rank these features on the mRMR feature list. Thereafter, we applied the incremental feature selection method with a supervised classifier recurrent neural network (RNN) to select the discriminate features for classifying widely expressed genes from rarely expressed genes and construct an optimum RNN classifier. The Youden's indexes generated by the optimum RNN classifier and evaluated using a 10-fold cross validation were 0.739 for normal tissues and 0.639 for cancers. Furthermore, the underlying mechanisms of the key discriminate GO and KEGG features were analyzed. Results can facilitate the identification of the expression landscape of genes and elucidation of how gene expression shapes tissues and the microenvironment of cancers.

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Sequence-Based Prediction of RNA-Binding Proteins Using Random Forest with Minimum Redundancy Maximum Relevance Feature Selection

Cited by 23 publications

References 43 publications

Mal-Lys: prediction of lysine malonylation sites in proteins integrated sequence-based features with mRMR feature selection

Mal-Lys: prediction of lysine malonylation sites in proteins integrated sequence-based features with mRMR feature selection

Autodetection of J Wave Based on Random Forest with Synchrosqueezed Wavelet Transform

Classification of Widely and Rarely Expressed Genes with Recurrent Neural Network

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