NDLI: Gene regulatory network inference from multifactorial perturbation data

Content Provider	IEEE Xplore Digital Library
Author	Jie Xiong Tong Zhou
Copyright Year	2012
Description	Author affiliation: Department of Automation and TNList, Tsinghua University, Beijing 100084 (Tong Zhou) \|\| Department of Automation, Tsinghua University, Beijing 100084 (Jie Xiong)
Abstract	An important problem in systems biology is to reconstruct gene regulatory networks from experimental data and other a priori information. Based on linear regression techniques and significance tests, in this paper, an identification algorithm is developed using multifactorial perturbation experimental data. Basic ideas behind this algorithm are as follows. The larger the sum of residual squares, the weaker the direct regulatory interaction. Moreover, the higher the significance level of linear regression, the greater the probability of the existence of the direct regulatory interaction. To take both of them into consideration, a weight corresponding to a possible direct regulation is selected as their product. Besides, normalization of these weights have also been discussed, noting that in a gene regulatory network, some genes may be easily regulated by other genes, while regulations on some other genes may need more efforts. A distinguished feature of the algorithm is that the power law has been quantitatively incorporated into estimations, which is one important structure property that most large scale genetic regulatory networks approximately have. Through constructing loss functions and incorporating power law, and solving a 0–1 integer programming problem, direct regulation genes for an arbitrary gene can be estimated. The weight matrix is further adjusted using these estimated direct regulatory relationships. Computation results with the DREAM4 In Silico Size 100 Multifactorial subchallenge show that estimation performances of the suggested algorithm can even outperform the best team. Furthermore, the high precision of the obtained most reliable predictions imply that the suggested algorithm may be very helpful in guiding biological validation experiment designs.
Starting Page	7382
Ending Page	7387
File Size	3609144
Page Count	6
File Format	PDF
ISBN	9781467325813
ISSN	21612927
e-ISBN	9789881563811
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Assoc of Automati
Subject Keyword	Multifactorial Perturbation Data Automation Large Scale Genetic Regulatory Network Causal Relationship Inference Linear regression Estimation Educational institutions Genetics Inference algorithms Electronic mail Power Law
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Causal relationship inference for a large scale genetic regulatory network

Gene regulatory network inference from multifactorial perturbation data using both regression and correlation analyses.

Reconstructing gene regulatory networks by integrating knockout and multifactorial perturbation data

A Markov-Blanket-Based Model for Gene Regulatory Network Inference

Gene Regulatory Network Inference from Multifactorial Perturbation Data Using both Regression and Correlation Analyses

Reliability allocation principle for large scale system

Reconstructing Pathways in Large Genetic Networks from Genetic Perturbations

Inferring regulatory networks from expression data using tree-based methods.

Inferring Profile Elements from Publicly Available Social Network Data

Gene regulatory network inference from multifactorial perturbation data

Similar Documents

Causal relationship inference for a large scale genetic regulatory network

Gene regulatory network inference from multifactorial perturbation data using both regression and correlation analyses.

Reconstructing gene regulatory networks by integrating knockout and multifactorial perturbation data

A Markov-Blanket-Based Model for Gene Regulatory Network Inference

Gene Regulatory Network Inference from Multifactorial Perturbation Data Using both Regression and Correlation Analyses

Reliability allocation principle for large scale system

Reconstructing Pathways in Large Genetic Networks from Genetic Perturbations

Inferring regulatory networks from expression data using tree-based methods.

Inferring Profile Elements from Publicly Available Social Network Data

Gene regulatory network inference from multifactorial perturbation data