Evaluation of classification and forecasting methods on time series gene expression data.

ABSTRACT: Time series gene expression data is widely used to study different dynamic biological processes. Although gene expression datasets share many of the characteristics of time series data from other domains, most of the analyses in this field do not fully leverage the time-ordered nature of the data and focus on clustering the genes based on their expression values. Other domains, such as financial stock and weather prediction, utilize time series data for forecasting purposes. Moreover, many studies have been conducted to classify generic time series data based on trend, seasonality, and other patterns. Therefore, an assessment of these approaches on gene expression data would be of great interest to evaluate their adequacy in this domain. Here, we perform a comprehensive evaluation of diffe

SUBMITTER: Tripto NI 

PROVIDER: S-EPMC7647064 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature