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  2. 上海生物化学与细胞生物学研究所
  3. 上海生科院生化细胞研究所
  4. 生化所2019-2020年发文
Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine

文献类型：期刊论文

作者Li, JiaRui6,7; Zhang, Yu-Hang7; Kong, XiangYin7; Huang, Tao7; Cai, Yu-Dong6; Lu, Lin5; Xu, YaoChen4; Liu, Min3; Chen, Lei3; Feng, KaiYan2
刊名CANCER GENE THERAPY
出版日期2020
卷号27期号:2020-01-02页码:56-69
ISSN号0929-1903
关键词ACUTE MYELOID-LEUKEMIA ACUTE MYELOGENOUS LEUKEMIA CORD BLOOD TRANSPLANT GENETIC-HETEROGENEITY ABERRANT EXPRESSION T-CELLS DENSITY GROWTH CYCLE CHEMOTHERAPY
DOI10.1038/s41417-019-0105-y
文献子类Article
英文摘要Acute myeloid leukemia (AML) is a type of blood cancer characterized by the rapid growth of immature white blood cells from the bone marrow. Therapy resistance resulting from the persistence of leukemia stem cells (LSCs) are found in numerous patients. Comparative transcriptome studies have been previously conducted to analyze differentially expressed genes between LSC+ and LSC- cells. However, these studies mainly focused on a limited number of genes with the most obvious expression differences between the two cell types. We developed a computational approach incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), support vector machine (SVM), Repeated Incremental Pruning to Produce Error Reduction (RIPPER), to identify gene expression features specific to LSCs. One thousand 0ne hudred fifty-nine features (genes) were first identified, which can be used to build the optimal SVM classifier for distinguishing LSC+ and LSC- cells. Among these 1159 genes, the top 17 genes were identified as LSC-specific biomarkers. In addition, six classification rules were produced by RIPPER algorithm. The subsequent literature review on these features/genes and the classification rules and functional enrichment analyses of the 1159 features/genes confirmed the relevance of extracted genes and rules to the characteristics of LSCs.
学科主题Biotechnology & Applied Microbiology ; Oncology ; Genetics & Heredity ; Medicine, Research & Experimental
语种英语
WOS记录号WOS:000514119000007
版本出版稿
源URL[http://202.127.25.143/handle/331003/3574]  
专题生化所2019-2020年发文
作者单位1.East China Normal Univ, Shanghai Key Lab PMMP, Shanghai 200241, Peoples R China,
2.Guangdong AIB Polytech, Dept Comp Sci, Guangzhou 510507, Peoples R China;
3.Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China;
4.Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Biochem & Cell Biol, Shanghai 200031, Peoples R China;
5.Columbia Univ, Med Ctr, Dept Radiol, New York, NY 10032 USA;
6.Shanghai Univ, Sch Life Sci, Shanghai 200444, Peoples R China;
7.Chinese Acad Sci, Shanghai Inst Nutr & Hlth, Shanghai Inst Biol Sci, Shanghai 200031, Peoples R China;
推荐引用方式
GB/T 7714		 Li, JiaRui,Zhang, Yu-Hang,Kong, XiangYin,et al. Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine[J]. CANCER GENE THERAPY,2020,27(2020-01-02):56-69.
APA Li, JiaRui.,Zhang, Yu-Hang.,Kong, XiangYin.,Huang, Tao.,Cai, Yu-Dong.,...&,.(2020).Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine.CANCER GENE THERAPY,27(2020-01-02),56-69.
MLA Li, JiaRui,et al."Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine".CANCER GENE THERAPY 27.2020-01-02(2020):56-69.

入库方式： OAI收割

来源：上海生物化学与细胞生物学研究所

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