Principles and Strategies for Developing Network Models in Cancer | Zendy

Dana Pe’er | Zendy; Nir Hacohen | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Principles and Strategies for Developing Network Models in Cancer

Author(s) -

Dana Pe’er,

Nir Hacohen

Publication year - 2011

Publication title -

cell

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 26.304

H-Index - 776

eISSN - 1097-4172

pISSN - 0092-8674

DOI - 10.1016/j.cell.2011.03.001

Subject(s) - biology , computer science , biological network , computational biology , drug discovery , genomics , big data , genome , bioinformatics , gene , data mining , genetics

The flood of genome-wide data generated by high-throughput technologies currently provides biologists with an unprecedented opportunity: to manipulate, query, and reconstruct functional molecular networks of cells. Here, we outline three underlying principles and six strategies to infer network models from genomic data. Then, using cancer as an example, we describe experimental and computational approaches to infer "differential" networks that can identify genes and processes driving disease phenotypes. In conclusion, we discuss how a network-level understanding of cancer can be used to predict drug response and guide therapeutics.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research