Identification of Differentially Expressed Genes between Original Breast Cancer and Xenograft Using Machine Learning Algorithms
文献类型：期刊论文
| 作者 | Wang, Deling1,2,3; Zhang, Yu-Hang1; Huang, Tao1; Li, Jia-Rui4; Cai, Yu-Dong4; Chen, Lei5; , |
| 刊名 | GENES
 |
| 出版日期 | 2018 |
| 卷号 | 9期号:3页码:155 |
| ISSN号 | 2073-4425 |
| 关键词 | Monte Carlo feature selection breast cancer random forest patient-derived tumor xenograft |
| DOI | 10.3390/genes9030155 |
| 文献子类 | Article |
| 英文摘要 | Breast cancer is one of the most common malignancies in women. Patient-derived tumor xenograft (PDX) model is a cutting-edge approach for drug research on breast cancer. However, PDX still exhibits differences from original human tumors, thereby challenging the molecular understanding of tumorigenesis. In particular, gene expression changes after tissues are transplanted from human to mouse model. In this study, we propose a novel computational method by incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS), random forest (RF), and rough set-based rule learning, to identify genes with significant expression differences between PDX and original human tumors. First, 831 breast tumors, including 657 PDX and 174 human tumors, were collected. Based on MCFS and RF, 32 genes were then identified to be informative for the prediction of PDX and human tumors and can be used to construct a prediction model. The prediction model exhibits a Matthews coefficient correlation value of 0.777. Seven interpretable interactions within the informative gene were detected based on the rough set-based rule learning. Furthermore, the seven interpretable interactions can be well supported by previous experimental studies. Our study not only presents a method for identifying informative genes with differential expression but also provides insights into the mechanism through which gene expression changes after being transplanted from human tumor into mouse model. This work would be helpful for research and drug development for breast cancer. |
| 学科主题 | Genetics & Heredity |
| WOS关键词 | HEMATOPOIETIC STEM-CELLS ; FEATURE-SELECTION ; RANDOM FOREST ; MODELS ; PROMOTES ; METASTASIS ; INHIBITORS ; PREDICTION ; CARCINOMA ; INSIGHTS |
| 语种 | 英语 |
| CSCD记录号 | CSCD:31776984 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000428508800038 |
| 版本 | 出版稿 |
| 源URL | [http://202.127.25.144/handle/331004/772]  |
| 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.Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, Shanghai 200031, Peoples R China; 2.Sun Yat Sen Univ, Ctr Canc, State Key Lab Oncol South China, Dept Med Imaging, Guangzhou 510060, Guangdong, Peoples R China; 3.Collaborat Innovat Ctr Canc Med, Guangzhou 510060, Guangdong, Peoples R China; 4.Shanghai Univ, Sch Life Sci, Shanghai 200444, Peoples R China; 5.Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China, |
| 推荐引用方式 GB/T 7714 | Wang, Deling,Zhang, Yu-Hang,Huang, Tao,et al. Identification of Differentially Expressed Genes between Original Breast Cancer and Xenograft Using Machine Learning Algorithms[J]. GENES,2018,9(3):155. |
| APA | Wang, Deling.,Zhang, Yu-Hang.,Huang, Tao.,Li, Jia-Rui.,Cai, Yu-Dong.,...&,.(2018).Identification of Differentially Expressed Genes between Original Breast Cancer and Xenograft Using Machine Learning Algorithms.GENES,9(3),155. |
| MLA | Wang, Deling,et al."Identification of Differentially Expressed Genes between Original Breast Cancer and Xenograft Using Machine Learning Algorithms".GENES 9.3(2018):155. |
入库方式： OAI收割
来源：上海营养与健康研究所
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