MFSC: multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou’s PseAAC components. (English) Zbl 1406.92167
Summary: Automatic identification of protein subcellular localization has gained much popularity in the last few decades. Subcellular localizations are useful in diagnosis of different diseases as well as in the process of drug development. Golgi is a vital type of protein, which provides means of transportation to several other proteins destined for lysosome, plasma membrane and secretion etc. Cis-Golgi and trans-Golgi are two ends of Golgi protein meant for reception and transmission of various substances. Dysfunction in Golgi proteins may lead to different types of diseases especially the inheritable and neurodegenerative problems.
Due to the significance of Golgi proteins, it is indispensable to correctly identify the Golgi proteins. In this paper, a novel and high throughput computational model is proposed which can identify the subGolgi proteins precisely. Discrete and evolutionary feature extraction schemes are applied so that all the salient, noiseless, and relevant information from protein sequences could be captured. Unfortunately, the benchmark dataset publicly available is quite imbalance, where trans-Golgi sequences constitute 72% of the whole dataset that reflects biasness, redundancy, and lack of hypothesis generalization. In order to cover the limitations of imbalance data, synthetic minority over sampling technique is utilized to balance the number of instances in different classes of the dataset. In addition, a condense feature space is formed by fusing the high rank features of eleven different feature selection techniques. The high rank features are selected through majority voting algorithm; consequently, the feature space is reduced 85%. The experiential results demonstrate that kNN classifier obtained promising results in combination with hybrid feature space. It has yielded an accuracy of 98% in jackknife cross-validation, 94% in independent data and 96% in 10-fold cross-validation test. It is ascertained that the proposed model is reliable, consistent and serves as a valuable tool for the research community.
Due to the significance of Golgi proteins, it is indispensable to correctly identify the Golgi proteins. In this paper, a novel and high throughput computational model is proposed which can identify the subGolgi proteins precisely. Discrete and evolutionary feature extraction schemes are applied so that all the salient, noiseless, and relevant information from protein sequences could be captured. Unfortunately, the benchmark dataset publicly available is quite imbalance, where trans-Golgi sequences constitute 72% of the whole dataset that reflects biasness, redundancy, and lack of hypothesis generalization. In order to cover the limitations of imbalance data, synthetic minority over sampling technique is utilized to balance the number of instances in different classes of the dataset. In addition, a condense feature space is formed by fusing the high rank features of eleven different feature selection techniques. The high rank features are selected through majority voting algorithm; consequently, the feature space is reduced 85%. The experiential results demonstrate that kNN classifier obtained promising results in combination with hybrid feature space. It has yielded an accuracy of 98% in jackknife cross-validation, 94% in independent data and 96% in 10-fold cross-validation test. It is ascertained that the proposed model is reliable, consistent and serves as a valuable tool for the research community.
MSC:
| 92C40 | Biochemistry, molecular biology |
| 92D20 | Protein sequences, DNA sequences |
| 68T05 | Learning and adaptive systems in artificial intelligence |
Software:
pLoc-mGneg; Cell-PLoc; iDHS-EL; Prnam-PC; iLoc-lncRNA; SMOTE; iRSpot-PseDNC; Memtype-2L; PseAAC; PseAAC-General; iSuc-PseOpt; iPPBS-Opt; pSuc-Lys; iDNA-Methyl; iDrug-Target; iPromoter-2L; iPPI-Esml; Pse-in-One; PseAAC-Builder; propy; pSumo-CD; iRNAm5C-PseDNC; iPreny-PseAAC; iRNA-2methyl; BLAST; iProt-Sub; iRNA-PseU; iRNA-3typeA; iRSpot-EL; pLoc_bal-mHum; pLoc_bal-mGpos; pLoc_bal-mGneg; pLoc-mHum; iDNA6mA-PseKNC; iKcr-PseEns; iRSpot-GAEnsC; 2L-piRNA; iRO-3wPseKNC; iRSpot-Pse6NC; pLoc-mPlant; PREvaIL; iEnhancer-2L; iRNA-AI; pLoc-mVirus; Acalpred; pLoc-mEuk; PSI-BLAST; iHyd-PseCp; MemHyb; iACP; pLoc-mAnimal; iRNA-PseColl; Hum-PLoc; iCar-PseCpReferences:
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