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Title: Systems and methods for modeling and analyzing networks

Abstract

The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.

Inventors:
; ; ; ; ;
Publication Date:
Research Org.:
Gene Network Sciences, Inc. (Cambridge, MA)
Sponsoring Org.:
USDOE
OSTI Identifier:
1107627
Patent Number(s):
8,571,803
Application Number:
11/985,618
Assignee:
Gene Network Sciences, Inc. (Cambridge, MA)
DOE Contract Number:  
FG02-04ER63806
Resource Type:
Patent
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Hill, Colin C, Church, Bruce W, McDonagh, Paul D, Khalil, Iya G, Neyarapally, Thomas A, and Pitluk, Zachary W. Systems and methods for modeling and analyzing networks. United States: N. p., 2013. Web.
Hill, Colin C, Church, Bruce W, McDonagh, Paul D, Khalil, Iya G, Neyarapally, Thomas A, & Pitluk, Zachary W. Systems and methods for modeling and analyzing networks. United States.
Hill, Colin C, Church, Bruce W, McDonagh, Paul D, Khalil, Iya G, Neyarapally, Thomas A, and Pitluk, Zachary W. Tue . "Systems and methods for modeling and analyzing networks". United States. https://www.osti.gov/servlets/purl/1107627.
@article{osti_1107627,
title = {Systems and methods for modeling and analyzing networks},
author = {Hill, Colin C and Church, Bruce W and McDonagh, Paul D and Khalil, Iya G and Neyarapally, Thomas A and Pitluk, Zachary W},
abstractNote = {The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2013},
month = {10}
}

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