A cDNA Microarray Gene Expression Data Classifier for Clinical Diagnostics Based on Graph Theory

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 8; no. 3; pp. 577 - 591
• Main Authors: Benso, Alfredo, Di Carlo, Stefano, Politano, Gianfranco
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2011; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial Intelligence; Bioinformatics; classification; clinical diagnostics; Computational Biology - methods; Data models; Databases, Factual; Diseases; DNA; Gene expression; Gene Expression Profiling - methods; Gene Regulatory Networks; graph theory; Humans; Microarray; Models, Genetic; Molecular Diagnostic Techniques - methods; Neoplasms - diagnosis; Neoplasms - genetics; Neoplasms - metabolism; Oligonucleotide Array Sequence Analysis - methods; Phenotype; Predictive models; Reproducibility of Results; Sensitivity and Specificity; Training
• ISSN: 1545-5963; 1557-9964; 1557-9964
• DOI: 10.1109/TCBB.2010.90

Despite great advances in discovering cancer molecular profiles, the proper application of microarray technology to routine clinical diagnostics is still a challenge. Current practices in the classification of microarrays' data show two main limitations: the reliability of the training data sets used to build the classifiers, and the classifiers' performances, especially when the sample to be classified does not belong to any of the available classes. In this case, state-of-the-art algorithms usually produce a high rate of false positives that, in real diagnostic applications, are unacceptable. To address this problem, this paper presents a new cDNA microarray data classification algorithm based on graph theory and is able to overcome most of the limitations of known classification methodologies. The classifier works by analyzing gene expression data organized in an innovative data structure based on graphs, where vertices correspond to genes and edges to gene expression relationships. To demonstrate the novelty of the proposed approach, the authors present an experimental performance comparison between the proposed classifier and several state-of-the-art classification algorithms.