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Genes 2018, 9(9), 449; https://doi.org/10.3390/genes9090449

A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes

1,†
,
2,3,†
,
4
,
4,* , 4,* and 1,*
1
School of Life Sciences, Shanghai University, Shanghai 200444, China
2
College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
3
Shanghai Key Laboratory of PMMP, East China Normal University, Shanghai 200241, China
4
Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
These authors contributed to work equally.
*
Authors to whom correspondence should be addressed.
Received: 3 August 2018 / Revised: 1 September 2018 / Accepted: 4 September 2018 / Published: 7 September 2018
(This article belongs to the Special Issue Systems Analytics and Integration of Big Omics Data)
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Abstract

Tissue-specific gene expression has long been recognized as a crucial key for understanding tissue development and function. Efforts have been made in the past decade to identify tissue-specific expression profiles, such as the Human Proteome Atlas and FANTOM5. However, these studies mainly focused on “qualitatively tissue-specific expressed genes” which are highly enriched in one or a group of tissues but paid less attention to “quantitatively tissue-specific expressed genes”, which are expressed in all or most tissues but with differential expression levels. In this study, we applied machine learning algorithms to build a computational method for identifying “quantitatively tissue-specific expressed genes” capable of distinguishing 25 human tissues from their expression patterns. Our results uncovered the expression of 432 genes as optimal features for tissue classification, which were obtained with a Matthews Correlation Coefficient (MCC) of more than 0.99 yielded by a support vector machine (SVM). This constructed model was superior to the SVM model using tissue enriched genes and yielded MCC of 0.985 on an independent test dataset, indicating its good generalization ability. These 432 genes were proven to be widely expressed in multiple tissues and a literature review of the top 23 genes found that most of them support their discriminating powers. As a complement to previous studies, our discovery of these quantitatively tissue-specific genes provides insights into the detailed understanding of tissue development and function. View Full-Text
Keywords: tissue-specific expressed genes; transcriptome; tissue classification; support vector machine; feature selection tissue-specific expressed genes; transcriptome; tissue classification; support vector machine; feature selection
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Li, J.; Chen, L.; Zhang, Y.-H.; Kong, X.; Huang, T.; Cai, Y.-D. A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes. Genes 2018, 9, 449.

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