GenDisMix
by Jens Keilwagen, Jan Grau, Stefan Posch, Marc Strickert, and Ivo Grosse.
Description
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 be 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 and enables 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.
Paper
The paper Unifying generative and discriminative learning principles has been submitted to BMC Bioinformatics.
