The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Inference of Gene Regulatory Networks by Topological Prior Information and Data Integration
Abstract
The inference of Gene Regulatory Networks (GRNs) is a very challenging problem which has attracted increasing attention since the development of high-throughput sequencing and gene expression measurement technologies. Many models and algorithms have been developed to identify GRNs using mainly gene expression profile as data source. As the gene expression data usually has limited number of samples and inherent noise, the integration of gene expression with several other sources of information can be vital for accurately inferring GRNs. For instance, some prior information about the overall topological structure of the GRN can guide inference techniques toward better results. In addition to gene expression data, recently biological information from heterogeneous data sources have been integrated by GRN inference methods as well. The objective of this chapter is to present an overview of GRN inference models and techniques with focus on incorporation of prior information such as, global and local topological features and integration of several heterogeneous data sources.
Related Content
|
Shweta Arun Avhad.
© 2023.
21 pages.
|
|
Majorie Moraa Nyasani, Victor Odhiambo Shikuku.
© 2023.
10 pages.
|
|
Prashant Kumar, Sunil Kumar Verma.
© 2023.
12 pages.
|
|
Sunil Kumar Verma, Prashant Kumar.
© 2023.
21 pages.
|
|
Kannadhasan S., Nagarajan R..
© 2023.
10 pages.
|
|
Kondapalli Vamsi Krishna, Sompalli Bhavana, Koushik Koujalagi, Alok Malaviya.
© 2023.
44 pages.
|
|
Bela Khiratkar, Shankar Mukundrao Khade, Abhishek Dutt Tripathi.
© 2023.
10 pages.
|
|
|