New statistical inferences exploring causal changes in biological networks

• Grant number: 13/06223-1
• Support Opportunities: Scholarships in Brazil - Doctorate
• Effective date (Start): July 01, 2013
• Effective date (End): May 07, 2016
• Field of knowledge: Physical Sciences and Mathematics - Probability and Statistics - Applied Probability and Statistics
• Principal Investigator: Anatoli Iambartsev
• Grantee: Lina Dornelas Thomas
• Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated scholarship(s): 13/14722-8 - Study of changes in gene co-expression networks, BE.EP.DR

Abstract

Many current researches aim to understand the causes of transition between conditions in a biological system. Differences in gene expression patterns between different conditions can point to a transition that depends on these conditions. A change in gene expression patterns can indicate that a relation between genes was lost, created, broken, or intensified. Two ways to measure this change and that we will use in this study is the difference of mean (Differentially Expressed Genes - Differentially Expressed Genes - DEGs) and of correlation (Differentially Associated Pair - Differentially Associated Pairs - DAPs). The main objective of this work is to find statistical relations between DEGs and DAPs. We want to find out if it is possible to explain the transition of a system from one state to another taking into account both DEGS and the information about DAPs. We also aim to find the statistical connections between DEGs and investigate how DAPs are related to the rebuilt networks using DEGs. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

DONG, XIAOXI; YAMBARTSEV, ANATOLY; RAMSEY, STEPHEN A.; THOMAS, LINA D.; SHULZHENKO, NATALIA; MORGUN, ANDREY. Reverse enGENEering of Regulatory Networks from Big Data: A Road map for Biologists. BIOINFORMATICS AND BIOLOGY INSIGHTS, v. 9, p. 61-74, 2015. (13/06223-1, 13/14722-8)