Integrating machine learning techniques into robust data enrichment approach and its application to gene expression data


Erdogdu U., TAN M., Alhajj R., POLAT F. , Rokne J., Demetrick D.

INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, cilt.8, ss.247-281, 2013 (SCI İndekslerine Giren Dergi) identifier identifier identifier

  • Cilt numarası: 8 Konu: 3
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1504/ijdmb.2013.056090
  • Dergi Adı: INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS
  • Sayfa Sayıları: ss.247-281

Özet

The availability of enough samples for effective analysis and knowledge discovery has been a challenge in the research community, especially in the area of gene expression data analysis. Thus, the approaches being developed for data analysis have mostly suffered from the lack of enough data to train and test the constructed models. We argue that the process of sample generation could be successfully automated by employing some sophisticated machine learning techniques. An automated sample generation framework could successfully complement the actual sample generation from real cases. This argument is validated in this paper by describing a framework that integrates multiple models (perspectives) for sample generation. We illustrate its applicability for producing new gene expression data samples, a highly demanding area that has not received attention. The three perspectives employed in the process are based on models that are not closely related. The independence eliminates the bias of having the produced approach covering only certain characteristics of the domain and leading to samples skewed towards one direction. The first model is based on the Probabilistic Boolean Network (PBN) representation of the gene regulatory network underlying the given gene expression data. The second model integrates Hierarchical Markov Model (HIMM) and the third model employs a genetic algorithm in the process. Each model learns as much as possible characteristics of the domain being analysed and tries to incorporate the learned characteristics in generating new samples. In other words, the models base their analysis on domain knowledge implicitly present in the data itself. The developed framework has been extensively tested by checking how the new samples complement the original samples. The produced results are very promising in showing the effectiveness, usefulness and applicability of the proposed multi-model framework.