AUTOMATIC CLUSTERING OF MICROARRAY DATA USING ART 2 NEURAL NETWORK

The Microarray technology has offered an overall view on the activity levels of numerous genes simultaneously. A typical Microarray dataset is characterized by a large number of genes; which is usually high with respect to the number of experiments. Due to the huge number of genes and the intricacy of biological data, clustering represents a gainful exploratory technique for analyzing Microarray data. The aim of clustering is to allocate objects to the adequate groups or clusters. On one hand, the objects belonging to the same cluster are more identical to each others. On the other hand, objects from dissimilar clusters are as unalike as possible. In the present work, the major goal is to extract automatically the number of clusters in the lung cancer Microarray dataset. In fact, a clustering technique based on Adaptive Resonance Theory is used; without any prior information about the number or the form of the clusters behind the processed data. The chosen form of Adaptive Resonance Theory architectures is ART2, which quickly self-organizes categories of pattern recognition in response to arbitrary presented sequences of both analog or binary input patterns [1]. The proposed approach was simulated on raw data and reduced data, using the PCA technique. The efficiency of the applied method was evaluated by measuring the performance of the classifiers against lung cancer Microarray data.

[1]  Jian Xin-chun.  The Microarray Data Analysis Process:from Raw Data to Biological Significance , 2007 .

[2]  G. Church,et al.  Systematic determination of genetic network architecture , 1999, Nature Genetics.

[3]  Peter D. Wentzell,et al.  An introduction to DNA microarrays for gene expression analysis , 2010 .

[4]  Zohar Yakhini,et al.  Clustering gene expression patterns , 1999, J. Comput. Biol..

[5]  Jonas S Almeida,et al.  Predictive non-linear modeling of complex data by artificial neural networks. , 2002, Current opinion in biotechnology.

[6]  Martial Mermillod,et al.  The stability-plasticity dilemma: investigating the continuum from catastrophic forgetting to age-limited learning effects , 2013, Front. Psychol..

[7]  Z.M. Li,et al.  Incremental learning for online tool condition monitoring using Ellipsoid ARTMAP network model , 2015, Appl. Soft Comput..

[8]  S. P. Fodor,et al.  Light-directed, spatially addressable parallel chemical synthesis. , 1991, Science.

[9]  Stephen Grossberg,et al.  ARTMAP: supervised real-time learning and classification of nonstationary data by a self-organizing neural network , 1991, [1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering.

[10]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..

[11]  M'hamed Ait Kbir,et al.  MLP network for lung cancer presence prediction based on microarray data , 2015, 2015 Third World Conference on Complex Systems (WCCS).

[12]  Doulaye Dembélé,et al.  Fuzzy C-means Method for Clustering Microarray Data , 2003, Bioinform..

[13]  M'hamed Ait Kbir,et al.  Data Preprocessing and Reducing for Microarray Data Exploration and Analysis , 2015 .

[14]  Stephen Grossberg,et al.  Adaptive pattern classification and universal recoding: II. Feedback, expectation, olfaction, illusions , 1976, Biological Cybernetics.

[15]  Adrian E. Raftery,et al.  How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis , 1998, Comput. J..

[16]  M. Malathi,et al.  Diagnosis Of Ovarian Cancer Using Artificial Neural Network , 2013 .

[17]  Stephen Grossberg,et al.  ART 2-A: an adaptive resonance algorithm for rapid category learning and recognition , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[18]  Paulo J. G. Lisboa,et al.  A framework for initialising a dynamic clustering algorithm: ART2-A , 2014, 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

[19]  S. Grossberg.  How does a brain build a cognitive code , 1980 .

[20]  Stephen Grossberg,et al.  Art 2: Self-Organization Of Stable Category Recognition Codes For Analog Input Patterns , 1988, Other Conferences.

[21]  K. Khrapko,et al.  An oligonucleotide hybridization approach to DNA sequencing , 1989, FEBS letters.

[22]  Stephen Grossberg,et al.  Adaptive Resonance Theory: How a brain learns to consciously attend, learn, and recognize a changing world , 2013, Neural Networks.

[23]  Shikha Agrawal,et al.  Neural Network Techniques for Cancer Prediction: A Survey , 2015, KES.

[24]  Stephen Grossberg,et al.  Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system , 1991, Neural Networks.