抽样技术产生大量不同结构的学习样本,可以肯定大部分的高相关基因或部分相关基因可被挖掘出来。在这部分
工作被完成之后,一个随之而来的工作是研究一个更复杂的生物学课题:这些基因是如何作用或相互作用,从而
导致我们所观察的表型?也就是所谓的目标专一性基因网络,正是我们目前研究的课题。
致谢 我们感谢二位匿名专家对本文初稿的评审意见。无论是对本文的修改或对我们进一步的工作都受益匪浅.
参 考 文 献
1
2
3
4
5
6
7 DeRisi, J L, Iyer, V R, Brown, P O. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science,1997,278:680~686 Golub, T R, Slonim, D K, Tamayo, P et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science,1999,286:531~537 Ambroise, C, McLachlan, G J. Selection bias in gene extraction on the basis of microarray gene-expression data. Proc Natl Acad Sci U S A,2002,99:6562~6566 Bo, T, Jonassen, I. New feature subset selection procedures for classification of expression profiles. Genome Biol,2002,3:RESEARCH0017 Chow, M L, Moler, E J, Mian, I S. Identifying marker genes in transcription profiling data using a mixture of feature relevance experts. Physiol Genomics,2001,5:99~111 Hastie, T, Tibshirani, R, Eisen, M B et al. 'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns. Genome Biol,2000,1:RESEARCH0003 Li, L, Weinberg, C R, Darden, T A et al. Gene selection for sample classification based on gene expression data: study
of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics,2001,17:1131~1142
Burke, H B. Discovering patterns in microarray data. Mol Diagn,2000,5:349~357
Breiman, L. Random forests. Machine Learning,2001,45:5~32
Breiman, L. Bagging predictors. Machine Learning,1996,24:123~140
Shannon, W D, Province, M A, Rao, D C. Tree-based recursive partitioning methods for subdividing sibpairs into
relatively more homogeneous subgroups. Genet Epidemiol,2001,20:293~306 8 9 10 11
应用DNA 芯片数据挖掘复杂疾病相关基因的集成决策方法
第1期 李 霞等:应用DNA芯片数据挖掘复杂疾病相关基因的集成决策方法 9 12
13
14
15 Province, M A, Shannon, W D, Rao, D C. Classification methods for confronting heterogeneity. Adv Genet,2001,42:273~286 Mills, J C, Gordon, J I. A new approach for filtering noise from high-density oligonucleotide microarray datasets. Nucleic Acids Res,2001,29:E72 Hall, M. Correlation-based Feature Selection for Machine Learning. Hamilton: University of Waikato, 1998. PhD Thesis Blum, A L, Langley, P. Selection of relevant features and examples in machine learning. Artificial
Intelligence,1997,97:245~271
Kohavi, R, John, G H. Wrappers for feature subset selection. Artificial Intelligence,1997,97:273~324
Xing, E P, Jordan, M I, Karp, R M. Feature Selection for High-Dimensional Genomic Microarray Data. In: Machine
Learning: Proceedings of the Eighteenth International Conference, San Mateo, CA, 2001. San Fransisco:Morgan
Kaufmann
Dietterich, T G. Ensemble Methods in Machine Learning. In: Kittler J, Roli F eds. First International Workshop on
Multiple Classifier Systems, Lecture Notes in Computer Science, New York: Springer Verlag, 2000, 1~15
Guo, Z, Li, X, Rao, S. Analysis of Medical Data: An Introduction to Bioinformatics, Harbin, China: Harbin Publisher,
2001
Zhang, H, Yu, C Y, Singer, B et al. Recursive partitioning for tumor classification with gene expression microarray
data. Proc Natl Acad Sci U S A,2001,98:6730~6735
Alon, U, Barkai, N, Notterman, D A et al. Broad patterns of gene expression revealed by clustering analysis of tumor
and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci U S A,1999,96:6745~6750
Kowalski, J, Denhardt, D T. Regulation of the mRNA for monocyte-derived neutrophil-activating peptide in
differentiating HL60 promyelocytes. Mol Cell Biol,1989,9:1946~1957
Su, Y, Murali, T M, Pavlovic, V et al. RankGene: identification of diagnostic genes based on expression data.
Bioinformatics,2003,19:1578~1579
Yeoh, E J, Ross, M E, Shurtleff, S A et al. Classification, subtype discovery, and prediction of outcome in pediatric
acute lymphoblastic leukemia by gene expression profiling. Cancer Cell,2002,1:133~143
Haseman, J K, Elston, R C. The investigation of linkage between a quantitative trait and a marker locus. Behav
Genet,1972,2:3~19 16 17 18 19 20 21 22 23 24 25
百度搜索“77cn”或“免费范文网”即可找到本站免费阅读全部范文。收藏本站方便下次阅读,免费范文网,提供经典小说公务员考试应用DNA 芯片数据挖掘复杂疾病相关基因的集成决策方法(6)在线全文阅读。
相关推荐: