Clustering-based multi-class classification of complex disease

Conference proceedings article


Authors/Editors


Strategic Research Themes

No matching items found.


Publication Details

Author listPhongwattana T., Engchuan W., Chan J.H.

PublisherHindawi

Publication year2015

Start page25

End page29

Number of pages5

ISBN9781479960491

ISSN0146-9428

eISSN1745-4557

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84925850631&doi=10.1109%2fKST.2015.7051475&partnerID=40&md5=30782624a1f069edd2016d71f7ffce05

LanguagesEnglish-Great Britain (EN-GB)


View on publisher site


Abstract

Pathway activity data transformed from gene expression profiles may be used to identify tumors, complex diseases progression, and cellular response to stimuli, and so on. Previous researches utilized data mining techniques on pathway activity data to distinguish subjects or to predict the phenotype outcome of subject directly. However, in the multi-class classification, learning those data mixing with population from different groups may result in contaminated model as excessive information is presented. This research, we use a two-stage approach applying clustering to homogenize training data before building the classification model. Hierarchical Clustering is used as a clustering method and Random Forest is used as classifier for evaluating the performance of the proposed method. The results are promising and show that using a clustering technique before classifying improves classification performance in general. ฉ 2015 IEEE.


Keywords

DNA MicroarrayHierarchical ClusteringPathway ActivitiesTwo-stage Multi-class Analysis


Last updated on 2023-28-09 at 07:35