title: Effective automated feature construction and selection for classification of biological sequences.
abstract: BACKGROUND
  Many open problems in bioinformatics involve elucidating underlying functional signals in biological sequences. DNA sequences, in particular, are characterized by rich architectures in which functional signals are increasingly found to combine local and distal interactions at the nucleotide level. Problems of interest include detection of regulatory regions, splice sites, exons, hypersensitive sites, and more. These problems naturally lend themselves to formulation as classification problems in machine learning. When classification is based on features extracted from the sequences under investigation, success is critically dependent on the chosen set of features.
  
  
  METHODS
  We present an algorithmic framework (EFFECT) for automated detection of functional signals in biological sequences. We focus here on classification problems involving DNA sequences which state-of-the-art work in machine learning shows to be challenging and involve complex combinations of local and distal features. EFFECT uses a two-stage process to first construct a set of candidate sequence-based features and then select a most effective subset for the classification task at hand. Both stages make heavy use of evolutionary algorithms to efficiently guide the search towards informative features capable of discriminating between sequences that contain a particular functional signal and those that do not.
  
  
  RESULTS
  To demonstrate its generality, EFFECT is applied to three separate problems of importance in DNA research: the recognition of hypersensitive sites, splice sites, and ALU sites. Comparisons with state-of-the-art algorithms show that the framework is both general and powerful. In addition, a detailed analysis of the constructed features shows that they contain valuable biological information about DNA architecture, allowing biologists and other researchers to directly inspect the features and potentially use the insights obtained to assist wet-laboratory studies on retainment or modification of a specific signal. Code, documentation, and all data for the applications presented here are provided for the community at http://www.cs.gmu.edu/~ashehu/?q=OurTools.
authors.affiliation: Computer Science, George Mason University, Fairfax, Virginia, United States of America.
authors.forename: Uday
authors.initials: U
authors.lastname: Kamath
authors.affiliation: Computer Science, George Mason University, Fairfax, Virginia, United States of America; Krasnow Institute, George Mason University, Fairfax, Virginia, United States of America.
authors.forename: Kenneth
authors.initials: K
authors.lastname: De Jong
authors.affiliation: Computer Science, George Mason University, Fairfax, Virginia, United States of America; Bioengineering, George Mason University, Fairfax, Virginia, United States of America; School of Systems Biology, George Mason University, Fairfax, Virginia, United States of America.
authors.forename: Amarda
authors.initials: A
authors.lastname: Shehu
mesh_terms: D000465:Algorithms; D020087:Alu Elements / Q000235:genetics*; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000235:genetics; D010363:Pattern Recognition, Automated / Q000379:methods*; D022821:RNA Splice Sites / Q000235:genetics*; D012045:Regulatory Sequences, Nucleic Acid; D017422:Sequence Analysis, DNA; D015398:Signal Transduction / Q000235:genetics
chemical_list: D022821:RNA Splice Sites; D004247:DNA