title: Gene expression data analysis with a dynamically extended self-organized map that exploits class information.
abstract: BACKGROUND
  Currently the most popular approach to analyze genome-wide expression data is clustering. One of the major drawbacks of most of the existing clustering methods is that the number of clusters has to be specified a priori. Furthermore, by using pure unsupervised algorithms prior biological knowledge is totally ignored Moreover, most current tools lack an effective framework for tight integration of unsupervised and supervised learning for the analysis of high-dimensional expression data and only very few multi-class supervised approaches are designed with the provision for effectively utilizing multiple functional class labeling.
  
  
  RESULTS
  The paper adapts a novel Self-Organizing map called supervised Network Self-Organized Map (sNet-SOM) to the peculiarities of multi-labeled gene expression data. The sNet-SOM determines adaptively the number of clusters with a dynamic extension process. This process is driven by an inhomogeneous measure that tries to balance unsupervised, supervised and model complexity criteria. Nodes within a rectangular grid are grown at the boundary nodes, weights rippled from the internal nodes towards the outer nodes of the grid, and whole columns inserted within the map The appropriate level of expansion is determined automatically. Multiple sNet-SOM models are constructed dynamically each for a different unsupervised/supervised balance and model selection criteria are used to select the one optimum one. The results indicate that sNet-SOM yields competitive performance to other recently proposed approaches for supervised classification at a significantly reduced computational cost and it provides extensive exploratory analysis potentiality within the analysis framework. Furthermore, it explores simple design decisions that are easier to comprehend and computationally efficient.
authors.affiliation: Department of Medical Physics, School of Medicine, University of Patras, 26500 Patras, Greece. severina@heart.med.upatras.gr
authors.forename: Seferina
authors.initials: S
authors.lastname: Mavroudi
authors.forename: Stergios
authors.initials: S
authors.lastname: Papadimitriou
authors.forename: Anastasios
authors.initials: A
authors.lastname: Bezerianos
mesh_terms: D000465:Algorithms; D004247:DNA / Q000145:classification* / Q000235:genetics*; D030561:Databases, Nucleic Acid; D005246:Feedback; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000235:genetics; D016681:Genome, Fungal; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010363:Pattern Recognition, Automated; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*
chemical_list: D004247:DNA