title: Many accurate small-discriminatory feature subsets exist in microarray transcript data: biomarker discovery.
abstract: BACKGROUND
  Molecular profiling generates abundance measurements for thousands of gene transcripts in biological samples such as normal and tumor tissues (data points). Given such two-class high-dimensional data, many methods have been proposed for classifying data points into one of the two classes. However, finding very small sets of features able to correctly classify the data is problematic as the fundamental mathematical proposition is hard. Existing methods can find "small" feature sets, but give no hint how close this is to the true minimum size. Without fundamental mathematical advances, finding true minimum-size sets will remain elusive, and more importantly for the microarray community there will be no methods for finding them.
  
  
  RESULTS
  We use the brute force approach of exhaustive search through all genes, gene pairs (and for some data sets gene triples). Each unique gene combination is analyzed with a few-parameter linear-hyperplane classification method looking for those combinations that form training error-free classifiers. All 10 published data sets studied are found to contain predictive small feature sets. Four contain thousands of gene pairs and 6 have single genes that perfectly discriminate.
  
  
  CONCLUSIONS
  This technique discovered small sets of genes (3 or less) in published data that form accurate classifiers, yet were not reported in the prior publications. This could be a common characteristic of microarray data, thus making looking for them worth the computational cost. Such small gene sets could indicate biomarkers and portend simple medical diagnostic tests. We recommend checking for small gene sets routinely. We find 4 gene pairs and many gene triples in the large hepatocellular carcinoma (HCC, Liver cancer) data set of Chen et al. The key component of these is the "placental gene of unknown function", PLAC8. Our HMM modeling indicates PLAC8 might have a domain like part of lP59's crystal structure (a Non-Covalent Endonuclease lii-Dna Complex). The previously identified HCC biomarker gene, glypican 3 (GPC3), is part of an accurate gene triple involving MT1E and ARHE. We also find small gene sets that distinguish leukemia subtypes in the large pediatric acute lymphoblastic leukemia cancer set of Yeoh et al.
authors.affiliation: Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. lesliegrate@comcast.net
authors.forename: Leslie R
authors.initials: LR
authors.lastname: Grate
mesh_terms: D015415:Biomarkers / Q000737:chemistry*; D014408:Biomarkers, Tumor; D006528:Carcinoma, Hepatocellular / Q000175:diagnosis / Q000235:genetics; D045744:Cell Line, Tumor; D016000:Cluster Analysis; D019295:Computational Biology / Q000295:instrumentation* / Q000379:methods*; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008113:Liver Neoplasms / Q000175:diagnosis / Q000235:genetics; D008390:Markov Chains; D008958:Models, Molecular; D008962:Models, Theoretical; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000175:diagnosis / Q000235:genetics; D011487:Protein Conformation; D017422:Sequence Analysis, DNA; D012984:Software; D012985:Software Design
chemical_list: D015415:Biomarkers; D014408:Biomarkers, Tumor