Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods
文献类型：期刊论文
| 作者 | Li, Jing2; Lan, Chun-Na2; Kong, Ying2; Feng, Song-Shan1; Huang, Tao3; , |
| 刊名 | FRONTIERS IN GENETICS
 |
| 出版日期 | 2018 |
| 卷号 | 9期号:-页码:246 |
| 关键词 | osteoarthritis blood gene expression signature support vector machine minimal redundancy maximal relevance incremental feature selection |
| ISSN号 | 1664-8021 |
| DOI | 10.3389/fgene.2018.00246 |
| 文献子类 | Article |
| 英文摘要 | Osteoarthritis (OA) is a complex disease that affects articular joints and may cause disability. The incidence of OA is extremely high. Most elderly people have the symptoms of osteoarthritis. The physiotherapy of OA is time consuming, and the chances of full recovery from OA are very minimal. The most effective way of fighting OA is early diagnosis and early intervention. Liquid biopsy has become a popular noninvasive test. To find the blood gene expression signature for OA, we reanalyzed the publicly available blood gene expression pro files of 106 patients with OA and 33 control samples using an automatic computational pipeline based on advanced feature selection methods. Finally, a compact 23-gene set was identified. On the basis of these 23 genes, we constructed a Support Vector Machine (SVM) classifier and evaluated it with leave-one-out cross-validation. Its sensitivity (Sn), specificity (Sp), accuracy (ACC), and Mathew's correlation coefficient (MCC) were 0.991, 0.909, 0.971, and 0.920, respectively. Obviously, the performance needed to be validated in an independent large dataset, but the in-depth biological analysis of the 23 biomarkers showed great promise and suggested that mRNA surveillance pathway and multicellular organism growth played important roles in OA. Our results shed light on OA diagnosis through liquid biopsy. |
| 学科主题 | Genetics & Heredity |
| WOS关键词 | PROTEIN-INTERACTION NETWORKS ; KNEE OSTEOARTHRITIS ; SYNOVIAL-FLUID ; BIOMARKERS ; PERSPECTIVE ; PROGRESSION ; REDUNDANCY ; PREDICTION ; RELEVANCE ; MACHINE |
| 语种 | 英语 |
| WOS记录号 | WOS:000443185700001 |
| 出版者 | FRONTIERS MEDIA SA |
| 版本 | 出版稿 |
| 源URL | [http://202.127.25.144/handle/331004/762]  |
| 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.Cent S Univ, Xiangya Hosp, Dept Neurosurg, Changsha, Hunan, Peoples R China; 2.Cent S Univ, Xiangya Hosp 2, Dept Rehabil, Changsha, Hunan, Peoples R China; 3.Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, Shanghai, Peoples R China, |
| 推荐引用方式 GB/T 7714 | Li, Jing,Lan, Chun-Na,Kong, Ying,et al. Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods[J]. FRONTIERS IN GENETICS,2018,9(-):246. |
| APA | Li, Jing,Lan, Chun-Na,Kong, Ying,Feng, Song-Shan,Huang, Tao,&,.(2018).Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods.FRONTIERS IN GENETICS,9(-),246. |
| MLA | Li, Jing,et al."Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods".FRONTIERS IN GENETICS 9.-(2018):246. |
入库方式： OAI收割
来源：上海营养与健康研究所
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