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BMC Medical Genomics

, 6:S10

First Online: 23 January 2013

Abstract

BackgroundUnderstanding how genes are expressed specifically in particular tissues is a fundamental question in developmental biology. Many tissue-specific genes are involved in the pathogenesis of complex human diseases. However, experimental identification of tissue-specific genes is time consuming and difficult. The accurate predictions of tissue-specific gene targets could provide useful information for biomarker development and drug target identification.

ResultsIn this study, we have developed a machine learning approach for predicting the human tissue-specific genes using microarray expression data. The lists of known tissue-specific genes for different tissues were collected from UniProt database, and the expression data retrieved from the previously compiled dataset according to the lists were used for input vector encoding. Random Forests RFs and Support Vector Machines SVMs were used to construct accurate classifiers. The RF classifiers were found to outperform SVM models for tissue-specific gene prediction. The results suggest that the candidate genes for brain or liver specific expression can provide valuable information for further experimental studies. Our approach was also applied for identifying tissue-selective gene targets for different types of tissues.

ConclusionsA machine learning approach has been developed for accurately identifying the candidate genes for tissue specific-selective expression. The approach provides an efficient way to select some interesting genes for developing new biomedical markers and improve our knowledge of tissue-specific expression.

Electronic supplementary materialThe online version of this article doi:10.1186-1755-8794-6-S1-S10 contains supplementary material, which is available to authorized users.

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Autor: Shaolei Teng - Jack Y Yang - Liangjiang Wang

Fuente: https://link.springer.com/







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