CFSP: a collaborative frequent sequence pattern discovery algorithm for nucleic acid sequence classification.

ABSTRACT:

Background

Conserved nucleic acid sequences play an essential role in transcriptional regulation. The motifs/templates derived from nucleic acid sequence datasets are usually used as biomarkers to predict biochemical properties such as protein binding sites or to identify specific non-coding RNAs. In many cases, template-based nucleic acid sequence classification performs better than some feature extraction methods, such as N-gram and k-spaced pairs classification. The availability of large-scale experimental data provides an unprecedented opportunity to improve motif extraction methods. The process for pattern extraction from large-scale data is crucial for the creation of predictive models.

Methods

In this article, a Teiresias-like feature extraction algorithm to discover

SUBMITTER: Peng H 

PROVIDER: S-EPMC7179567 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature