MALA: A Microarray Clustering and Classification Software

Microarray Logic Analyzer (MALA) is a clustering and classification software, particularly engineered for microarray gene expression analysis. The aims of MALA are to cluster the microarray gene expression profiles in order to reduce the amount of data to be analyzed and to classify the microarray experiments. To fulfil this objective MALA uses a machine learning process based methodology, that relies on 1) Discretization, 2) Gene clustering, 3) Feature selection, 4) Formulas computation,5) Classification. In this paper we describe the methodology, the software design, the different releases and user interfaces of MALA. We also emphasize its strengths: the identification of classification formulas that are able to precisely describe in a compact way the different classes of the microarray experiments. Finally, we show the experimental results obtained on a real microarray data set coming from Alzheimer diseased versus control mice microarray probes, and conclude that MALA is a powerful and reliable software for microarray gene expression analysis.

[1]  Tao Han,et al.  Microarray scanner calibration curves: characteristics and implications , 2005, BMC Bioinformatics.

[2]  P. Bertolazzi,et al.  Gene expression biomarkers in the brain of a mouse model for Alzheimer's disease: mining of microarray data by logic classification and feature selection. , 2011, Journal of Alzheimer's disease : JAD.

[3]  Klaus Truemper,et al.  A MINSAT Approach for Learning in Logic Domains , 2002, INFORMS J. Comput..

[4]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[5]  W. Liang,et al.  TM4 microarray software suite. , 2006, Methods in enzymology.

[6]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[7]  M. Resende,et al.  A probabilistic heuristic for a computationally difficult set covering problem , 1989 .

[8]  Tao Li,et al.  A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression , 2004, Bioinform..

[9]  Mauricio G. C. Resende,et al.  An Annotated Bibliography of Grasp Part I: Algorithms , 2022 .

[10]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[11]  Arlindo L. Oliveira,et al.  Biclustering algorithms for biological data analysis: a survey , 2004, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[12]  Hua Wang,et al.  A Comparative Study of Classification Methods For Microarray Data Analysis , 2006, AusDM.

[13]  Giovanni Felici,et al.  Human polyomaviruses identification by logic mining techniques , 2012, Virology Journal.

[14]  Giovanni Felici,et al.  Species classification using DNA Barcode sequences: A comparative analysis , 2011 .

[15]  W. Liang,et al.  9) TM4 Microarray Software Suite , 2006 .

[16]  Giovanni Felici,et al.  Learning to classify species with barcodes , 2009, BMC Bioinformatics.

[17]  Venkatesan Guruswami,et al.  Combinatorial feature selection problems , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.

[18]  Aidong Zhang,et al.  Cluster analysis for gene expression data: a survey , 2004, IEEE Transactions on Knowledge and Data Engineering.

[19]  Giovanni Felici,et al.  Logic classification and feature selection for biomedical data , 2008, Comput. Math. Appl..

[20]  J. Mesirov,et al.  GenePattern 2.0 , 2006, Nature Genetics.

[21]  J. Stuart Aitken,et al.  Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes , 2005, BMC Bioinformatics.

[22]  Giovanni Felici,et al.  DNA Barcoding of Recently Diverged Species: Relative Performance of Matching Methods , 2012, PloS one.

[23]  Béchir el Ayeb,et al.  Mining microarray gene expression data with unsupervised possibilistic clustering and proximity graphs , 2010, Applied Intelligence.

[24]  Lukasz A. Kurgan,et al.  CAIM discretization algorithm , 2004, IEEE Transactions on Knowledge and Data Engineering.

[25]  Manju Bansal,et al.  A novel method for prokaryotic promoter prediction based on DNA stability , 2005, BMC Bioinformatics.