A Data Adaptive Biological Sequence Representation for Supervised Learning.

ABSTRACT: Proper expression of the genes plays a vital role in the function of an organism. Recent advancements in DNA microarray technology allow for monitoring the expression level of thousands of genes. One of the important tasks in this context is to understand the underlying mechanisms of gene regulation. Recently, researchers have focused on identifying local DNA elements, or motifs to infer the relation between the expression and the nucleotide sequence of the gene. This study proposes a novel data adaptive representation approach for supervised learning to predict the response associated with the biological sequences. Biological sequences such as DNA and protein are a class of categorical sequences. In machine learning, categorical sequences are generally mapped to a lower dimensional repres

SUBMITTER: Cakin H 

PROVIDER: S-EPMC8982718 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature