Journalartikel

Jahr der Veröffentlichung: 2010

Zeitschrift: BMC Bioinformatics

Bandnummer: 11

ISSN: 1471-2105

DOI Link: https://doi.org/10.1186/1471-2105-11-98

Verlag: BioMed Central

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.

Harvard-Zitierstil: Keilwagen, J., Grau, J., Posch, S., Strickert, M. and Grosse, I. (2010) Unifying generative and discriminative learning principles, BMC Bioinformatics, 11, Article 98. https://doi.org/10.1186/1471-2105-11-98

APA-Zitierstil: Keilwagen, J., Grau, J., Posch, S., Strickert, M., & Grosse, I. (2010). Unifying generative and discriminative learning principles. BMC Bioinformatics. 11, Article 98. https://doi.org/10.1186/1471-2105-11-98