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A Review of Cancer Classification Software for Gene Expression Data
Tan Ching Siang,Ting Wai Soon,Shahreen Kasim,Mohd Saberi Mohamad,Chan Weng Howe,Safaai Deris,Zalmiyah Zakaria,Zuraini Ali Shah,Zuwairie Ibrahim 보안공학연구지원센터 2015 International Journal of Bio-Science and Bio-Techn Vol.7 No.4
Microarray technology provides a way for researchers to measure the expression level of thousands of genes simultaneously in a single experiment. Due to the increasing amount of microarray data, the field of microarray data analysis has become a major topic among researchers. One of the examples of microarray data analysis is classification. Classification is the process of determining the classes for samples. The goal of classification is to identify the differentially expressed genes so that these genes can be used to predict the classes for new samples. In order to perform the tasks of classification of microarray data, classification software is required for effective classification and analysis of large-scale data. This paper reviews numerous classification software applications for gene expression data. In this paper, the reviewed software can be categorized into six supervised classification methods: Support Vector Machine, K-Nearest Neighbour, Neural Network, Linear Discriminant Analysis, Bayesian Classifier, and Random Forest.
A Review of Software for Predicting Gene Function
Swee Kuan Loha,Swee Thing Low,Mohd Saberi Mohamad,Safaai Deris,Shahreen Kasim,Choon Yee Wen,Zuwairie Ibrahim,Bambang Susilo,Yusuf Hendrawan,Agustin Krisna Wardani 보안공학연구지원센터 2015 International Journal of Bio-Science and Bio-Techn Vol.7 No.2
A rich resource of information on functional genomics data can be applied to annotating the thousands of unknown gene functions that can be retrieved from most sequenced. High-throughput sequencing can lead to increased understanding of proteins and genes. We can infer networks of functional couplings from direct and indirect interactions. The development of gene function prediction is one of the major recent advances in the bioinformatics fields. These methods explore genomic context by major recent advances in the bioinformatics fields rather than by sequence alignment. This paper reviews software related to predicting gene function. Most of these programs are freely available online. The advantages and disadvantages of each program are stated clearly in order for the reader to understand them in a simple way. Web links to the software are provided as well.