- 作者: Liang-Tsung, Huang
- 作者服務機構: Department of Computer Science and Information Engineering, Mingdao University, Changhua, Taiwan, R.O.C.
- 中文摘要: --
- 英文摘要:
Different microarray techniques recently have been successfully used to
investigate useful information for cancer diagnosis at the gene expression level due to
their ability to measure thousands of gene expression levels in a massively parallel
way. One important issue is to improve classification performance of microarray data.
However, it would be ideal that influential genes and even interpretable rules can be
explored at the same time to offer biological insight.
Introducing the concepts of system design in software engineering, this paper has
presented an integrated and effective method (named X-AI) for accurate cancer
classification and the acquisition of knowledge from DNA microarray data. This
method included a feature selector to systematically extract the relative important
genes so as to reduce the dimension and retain as much as possible of the class
discriminatory information. Next, diagonal quadratic discriminant analysis (DQDA)
was combined to classify tumors, and generalized rule induction (GRI) was integrated
to establish association rules which can give an understanding of the relationships
between cancer classes and related genes.
Two non-redundant datasets of acute leukemia were used to validate the
proposed X-AI, showing significantly high accuracy for discriminating different
classes. On the other hand, I have presented the abilities of X-AI to extract relevant
genes, as well as to develop interpretable rules. Further, a web server has been
established for cancer classification and it is freely available at
http://bioinformatics.myweb.hinet.net/xai.htm. - 中文關鍵字: --
- 英文關鍵字: --