title: Expectation Maximization of Frequent Patterns, a Specific, Local, Pattern-Based Biclustering Algorithm for Biological Datasets.
abstract: Currently, binary biclustering algorithms are too slow and non-specific to handle biological datasets that have a large number of attributes, which is essential for the computational biology problem of microarray analysis. Specialized computers may be needed to execute an algorithm, and may fail to produce a solution, due to its large resource needs. The biclusters also include too many false positives, the type I error, which hinders biological discovery. We propose an algorithm that can analyze datasets with a large attribute set at different densities, and can operate on a laptop, which makes it accessible to practitioners. EMFP produces biclusters that have a very low Root Mean Squared Error and false positive rate, with very few type II errors. Our binary biclustering algorithm is a hybrid, axis-parallel, pattern-based algorithm that finds multiple, non-overlapping, near-constant, deterministic, binary submatricies, with a variable confidence threshold, and the novel use of local density comparisons versus the standard global threshold. EMFP introduces a new, and intuitive way to calculate internal measures for binary biclustering methods. We also introduce a framework to ease comparison with other algorithms, and compare to both binary and general biclustering algorithms using two real, and 80 synthetic databases.
authors.forename: Erin Jessica
authors.initials: EJ
authors.lastname: Moore
authors.forename: Thirmachos
authors.initials: T
authors.lastname: Bourlai
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D057225:Data Mining / Q000379:methods; D016208:Databases, Factual; D020869:Gene Expression Profiling / Q000379:methods*; D016013:Likelihood Functions; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017421:Sequence Analysis / Q000379:methods*; D012984:Software