title: Unifying generative and discriminative learning principles.
abstract: BACKGROUND
  The recognition of functional binding sites in genomic DNA remains one of the fundamental challenges of genome research. During the last decades, a plethora of different and well-adapted models has been developed, but only little attention has been payed to the development of different and similarly well-adapted learning principles. Only recently it was noticed that discriminative learning principles can be superior over generative ones in diverse bioinformatics applications, too.
  
  
  RESULTS
  Here, we propose a generalization of generative and discriminative learning principles containing the maximum likelihood, maximum a posteriori, maximum conditional likelihood, maximum supervised posterior, generative-discriminative trade-off, and penalized generative-discriminative trade-off learning principles as special cases, and we illustrate its efficacy for the recognition of vertebrate transcription factor binding sites.
  
  
  CONCLUSIONS
  We find that the proposed learning principle helps to improve the recognition of transcription factor binding sites, enabling better computational approaches for extracting as much information as possible from valuable wet-lab data. We make all implementations available in the open-source library Jstacs so that this learning principle can be easily applied to other classification problems in the field of genome and epigenome analysis.
authors.affiliation: Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany. Jens.Keilwagen@ipk-gatersleben.de
authors.forename: Jens
authors.initials: J
authors.lastname: Keilwagen
authors.forename: Jan
authors.initials: J
authors.lastname: Grau
authors.forename: Stefan
authors.initials: S
authors.lastname: Posch
authors.forename: Marc
authors.initials: M
authors.lastname: Strickert
authors.forename: Ivo
authors.initials: I
authors.lastname: Grosse
mesh_terms: D000465:Algorithms; D004247:DNA / Q000737:chemistry / Q000378:metabolism; D016002:Discriminant Analysis; D016678:Genome; D023281:Genomics; D016247:Information Storage and Retrieval / Q000379:methods*; D016013:Likelihood Functions; D010363:Pattern Recognition, Automated
chemical_list: D004247:DNA