Classification of Widely and Rarely Expressed Genes with Recurrent Neural Network
文献类型：期刊论文
| 作者 | Chen, Lei1,2,3; Cai, Yu-Dong1; Liu, Min2; Pan, XiaoYong5; Zhang, Yu-Hang4; Huang, Tao4; , |
| 刊名 | COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
 |
| 出版日期 | 2019 |
| 卷号 | 17期号:-页码:49-60 |
| 关键词 | Widely expressed gene Rarely expressed gene Enrichment theory Minimum redundancy maximum relevance Incremental feature selection Recurrent neural network |
| ISSN号 | 2001-0370 |
| DOI | 10.1016/j.csbj.2018.12.002 |
| 文献子类 | Article |
| 英文摘要 | A tissue-specific gene expression shapes the formation of tissues, while gene expression changes reflect the immune response of the human body to environmental stimulations or pressure, particularly in disease conditions, such as cancers. A few genes are commonly expressed across tissues or various cancers, while others are not. To investigate the functional differences between widely and rarely expressed genes, we defined the genes that were expressed in 32 normal tissues/cancers (i.e., called widely expressed genes; FPKM >1 in all samples) and those that were not detected (i.e., called rarely expressed genes; FPKM <1 in all samples) based on the large gene expression data set provided by Uhlen et al. Each gene was encoded using the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment scores. Minimum redundancy maximum relevance (mRMR) was used to measure and rank these features on the mRMR feature list. Thereafter, we applied the incremental feature selection method with a supervised classifier recurrent neural network (RNN) to select the discriminate features for classifying widely expressed genes from rarely expressed genes and construct an optimum RNN classifier. The Youden's indexes generated by the optimum RNN classifier and evaluated using a 10-fold cross validation were 0.739 for normal tissues and 0.639 for cancers. Furthermore, the underlying mechanisms of the key discriminate GO and KEGG features were analyzed. Results can facilitate the identification of the expression landscape of genes and elucidation of how gene expression shapes tissues and the microenvironment of cancers. (C) 2018 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. |
| 学科主题 | Science & Technology - Other Topics |
| WOS关键词 | ACTIVATING POLYPEPTIDE PACAP ; PROTEIN INTERACTIONS ; ROBUST PREDICTION ; UBIQUITIN LIGASES ; CANCER-CELLS ; IDENTIFICATION ; PROFILES ; KEGG ; DYSREGULATION ; INVOLVEMENT |
| 语种 | 英语 |
| CSCD记录号 | CSCD:30595815 |
| WOS记录号 | WOS:000504205700006 |
| 出版者 | ELSEVIER |
| 版本 | 出版稿 |
| 源URL | [http://202.127.25.144/handle/331004/831]  |
| 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, 5.Erasmus MC, Dept Med Informat, Rotterdam, Netherlands; |
| 推荐引用方式 GB/T 7714 | Chen, Lei,Cai, Yu-Dong,Liu, Min,et al. Classification of Widely and Rarely Expressed Genes with Recurrent Neural Network[J]. COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL,2019,17(-):49-60. |
| APA | Chen, Lei.,Cai, Yu-Dong.,Liu, Min.,Pan, XiaoYong.,Zhang, Yu-Hang.,...&,.(2019).Classification of Widely and Rarely Expressed Genes with Recurrent Neural Network.COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL,17(-),49-60. |
| MLA | Chen, Lei,et al."Classification of Widely and Rarely Expressed Genes with Recurrent Neural Network".COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 17.-(2019):49-60. |
入库方式： OAI收割
来源：上海营养与健康研究所
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