Feature selection for gene prediction in metagenomic fragments.

ABSTRACT:

Background

Computational approaches, specifically machine-learning techniques, play an important role in many metagenomic analysis algorithms, such as gene prediction. Due to the large feature space, current de novo gene prediction algorithms use different combinations of classification algorithms to distinguish between coding and non-coding sequences.

Results

In this study, we apply a filter method to select relevant features from a large set of known features instead of combining them using linear classifiers or ignoring their individual coding potential. We use minimum redundancy maximum relevance (mRMR) to select the most relevant features. Support vector machines (SVM) are trained using these features, and the classification score is transformed into the posterior proba

SUBMITTER: Al-Ajlan A 

PROVIDER: S-EPMC6047368 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature