Distinguishing three subtypes of hematopoietic cells based on gene expression profiles using a support vector machine
文献类型：期刊论文
| 作者 | Zhang, Yu-Hang; Hu, Yu; Zhang, Yuchao; Hu, Lan -Dian; Kong, Xiangyin; , |
| 刊名 | BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR BASIS OF DISEASE
 |
| 出版日期 | 2018 |
| 卷号 | 1864期号:6页码:2255-2265 |
| 关键词 | Hematopoiesis Hematopoietic stem cells Support vector machine Sequential minimum optimization Minimal redundancy maximal relevance |
| ISSN号 | 0925-4439 |
| DOI | 10.1016/j.bbadis.2017.12.003 |
| 文献子类 | Article |
| 英文摘要 | Hematopoiesis is a complicated process involving a series of biological sub-processes that lead to the formation of various blood components. A widely accepted model of early hematopoiesis proceeds from long-term hematopoietic stem cells (LT-HSCs) to multipotent progenitors (MPPs) and then to lineage-committed progenitors. However, the molecular mechanisms of early hematopoiesis have not been fully characterized. In this study, we applied a computational strategy to identify the gene expression signatures distinguishing three types of closely related hematopoietic cells collected in recent studies: (1) hematopoietic stem cell/multipotent progenitor cells; (2) LT-HSCs; and (3) hematopoietic progenitor cells. Each cell in these cell types was represented by its gene expression profile among a total number of 20,475 genes. The expression features were analyzed by a Monte Carlo Feature Selection (MCFS) method, resulting in a feature list. Then, the incremental feature selection (IFS) and a support vector machine (SVM) optimized with a sequential minimum optimization (SMO) algorithm were employed to access the optimal classifier with the highest Matthews correlation coefficient (MCC) value of 0.889, in which 6698 features were used to represent cells. In addition, through an updated program of MCFS method, seventeen decision rules can be obtained, which can classify the three cell types with an overall accuracy of 0.812. Using a literature review, both the rules and the top features used for building the optimal classifier were confirmed to be commonly used or potential biological markers for distinguishing the three cell types of HSPCs. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang. |
| 学科主题 | Biochemistry & Molecular Biology ; Biophysics ; Cell Biology |
| WOS关键词 | TRANSCRIPTION FACTOR NKX2-3 ; CARLO FEATURE-SELECTION ; HEPATIC STELLATE CELLS ; PROTEIN-C RECEPTOR ; STEM-CELLS ; PROGENITOR CELLS ; BONE-MARROW ; SUPERVISED CLASSIFICATION ; MOLECULAR FRAGMENTS ; CIRCULATORY SYSTEMS |
| 语种 | 英语 |
| WOS记录号 | WOS:000432105500006 |
| 出版者 | ELSEVIER SCIENCE BV |
| 版本 | 出版稿 |
| 源URL | [http://202.127.25.144/handle/331004/652]  |
| 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 | Univ Chinese Acad Sci, Shanghai Inst Biol Sci, Chinese Acad Sci, Shanghai 200031, Peoples R China, |
| 推荐引用方式 GB/T 7714 | Zhang, Yu-Hang,Hu, Yu,Zhang, Yuchao,et al. Distinguishing three subtypes of hematopoietic cells based on gene expression profiles using a support vector machine[J]. BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR BASIS OF DISEASE,2018,1864(6):2255-2265. |
| APA | Zhang, Yu-Hang,Hu, Yu,Zhang, Yuchao,Hu, Lan -Dian,Kong, Xiangyin,&,.(2018).Distinguishing three subtypes of hematopoietic cells based on gene expression profiles using a support vector machine.BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR BASIS OF DISEASE,1864(6),2255-2265. |
| MLA | Zhang, Yu-Hang,et al."Distinguishing three subtypes of hematopoietic cells based on gene expression profiles using a support vector machine".BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR BASIS OF DISEASE 1864.6(2018):2255-2265. |
入库方式： OAI收割
来源：上海营养与健康研究所
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