Novel Data Mining Techniques in aCGH based Breast Cancer Subtypes Profiling: the Biological Perspective
作者： F. Menolascina , S. Tommasi , A. Paradiso , M. Cortellino , V. Bevilacqua
DOI: 10.1109/CIBCB.2007.4221198
关键词: Artificial intelligence 、 Profiling (information science) 、 Computational intelligence 、 Naive Bayes classifier 、 C4.5 algorithm 、 Gene expression programming 、 Rule induction 、 Decision tree 、 Computer science 、 Data mining 、 Machine learning 、 Intelligent decision support system
摘要: In this paper we present a comparative study among well established data mining algorithm (namely J48 and naive Bayes tree) novel machine learning paradigms like ant miner gene expression programming. The aim of was to discover significant rules discriminating ER+ ER-cases breast cancer. We compared both statistical accuracy biological validity the results using common methods ontology. Some worth noting characteristics these systems have been observed analysed even giving some possible interpretations findings. With tried show how intelligent can be employed in design experimental pipeline disease processes investigation deriving high-throughput validated new computational tools. Results returned by approach seem encourage efforts field
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