Unifying generative and discriminative learning principles

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 he...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Jens Keilwagen, Jan Grau, Stefan Posch, Marc Strickert, Ivo Grosse
• Keywords: Binding Sites, BMCBI 2010, Discriminative Learning Principles, Transcription Factor