Abstract
Temporal clustering of time series data is a powerful tool to delaminate the dynamics of transcription and interactions among genes on a large scale. Different algorithms have been proposed to organise experimental data with meaningful biological clusters; however, these approaches often fail to generate well–defined temporal clusters, especially when genes exert their functions or response to stimuli coordinately only in a short period of time span. In this study, we proposed an algorithm using sliding windows to identify different temporal patterns based on fold changes of gene expressions. The algorithm was applied to simulated data and real experimental data. Furthermore, a comparison study has been carried out with the clusters obtained from commercial software packages. The identified clusters using our algorithm demonstrated better temporal matching and consistency.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Functional Informatics and Personalised Medicine
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
