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Full-Text Articles in Life Sciences
A Comparative Study Of Different Machine Learning Methods On Microarray Gene Expression Data, Mehdi Pirooznia, Jack Y. Yang, Mary Qu Yang, Youping Deng
A Comparative Study Of Different Machine Learning Methods On Microarray Gene Expression Data, Mehdi Pirooznia, Jack Y. Yang, Mary Qu Yang, Youping Deng
Faculty Publications
Background
Several classification and feature selection methods have been studied for the identification of differentially expressed genes in microarray data. Classification methods such as SVM, RBF Neural Nets, MLP Neural Nets, Bayesian, Decision Tree and Random Forrest methods have been used in recent studies. The accuracy of these methods has been calculated with validation methods such as v-fold validation. However there is lack of comparison between these methods to find a better framework for classification, clustering and analysis of microarray gene expression results.
Results
In this study, we compared the efficiency of the classification methods including; SVM, RBF Neural Nets, …
Supervised Learning Method For The Prediction Of Subcellular Localization Of Proteins Using Amino Acid And Amino Acid Pair Composition, Tanwir Habib, Chaoyang Zhang, Jack Y. Yang, Mary Qu Yang, Youping Deng
Supervised Learning Method For The Prediction Of Subcellular Localization Of Proteins Using Amino Acid And Amino Acid Pair Composition, Tanwir Habib, Chaoyang Zhang, Jack Y. Yang, Mary Qu Yang, Youping Deng
Faculty Publications
Background
Occurrence of protein in the cell is an important step in understanding its function. It is highly desirable to predict a protein's subcellular locations automatically from its sequence. Most studied methods for prediction of subcellular localization of proteins are signal peptides, the location by sequence homology, and the correlation between the total amino acid compositions of proteins. Taking amino-acid composition and amino acid pair composition into consideration helps improving the prediction accuracy.
Results
We constructed a dataset of protein sequences from SWISS-PROT database and segmented them into 12 classes based on their subcellular locations. SVM modules were trained to …