A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes
文献类型：期刊论文
| 作者 | Li, JiaRui1; Cai, Yu-Dong1; Chen, Lei2,3; Zhang, Yu-Hang4; Kong, XiangYin4; Huang, Tao4; , |
| 刊名 | GENES
 |
| 出版日期 | 2018 |
| 卷号 | 9期号:9页码:449 |
| 关键词 | tissue-specific expressed genes transcriptome tissue classification support vector machine feature selection |
| ISSN号 | 2073-4425 |
| DOI | 10.3390/genes9090449 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| 学科主题 | Genetics & Heredity |
| WOS关键词 | ACUTE LYMPHOBLASTIC-LEUKEMIA ; HUMAN PROTEIN ATLAS ; REGULATORY T-CELLS ; FEATURE-SELECTION ; CANCER ; RELEVANCE ; IDENTIFICATION ; REDUNDANCY ; PREDICTION ; MUTATIONS |
| 语种 | 英语 |
| WOS记录号 | WOS:000448398700030 |
| 出版者 | MDPI |
| 版本 | 出版稿 |
| 源URL | [http://202.127.25.144/handle/331004/872]  |
| 专题 | 中国科学院上海生命科学研究院营养科学研究所 |
| 作者单位 | 1.Shanghai Univ, Sch Life Sci, Shanghai 200444, Peoples R China; 2.Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China; 3.East China Normal Univ, Shanghai Key Lab PMMP, Shanghai 200241, Peoples R China; 4.Chinese Acad Sci, Inst Hlth Sci, Shanghai Inst Biol Sci, Shanghai 200031, Peoples R China, |
| 推荐引用方式 GB/T 7714 | Li, JiaRui,Cai, Yu-Dong,Chen, Lei,et al. A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes[J]. GENES,2018,9(9):449. |
| APA | Li, JiaRui.,Cai, Yu-Dong.,Chen, Lei.,Zhang, Yu-Hang.,Kong, XiangYin.,...&,.(2018).A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes.GENES,9(9),449. |
| MLA | Li, JiaRui,et al."A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes".GENES 9.9(2018):449. |
入库方式： OAI收割
来源：上海营养与健康研究所
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