Exploiting modularity and hierarchical modularity to infer large causal gene regulatory network

Leung Yau LO, Man Leung WONG, Kin Hong LEE, Kwong Sak LEUNG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

Abstract

Gene regulatory network (GRN), which refers to the complex interactions with time delays between TFs and other genes, plays an important role in the working of the cell. Therefore inferring the GRN is crucial to studying diseases related to malfunctioning of the cell. Even with high-throughput technology, time series expression data is still limited compared to the network size, which poses significant challenge to inferring large GRN. Since GRNs are known to be modular, or hierarchically modular, we propose to exploit this by first inferring an initial GRN using CLINDE, then decomposing it into possibly overlapping subnetworks, then re-learning the subnetworks using either CLINDE or DD-lasso, and lastly merging the subnetworks. We have performed extensive experiments on synthetic data to test this strategy on both modular and hierarchically modular networks with 500 and 1000 genes, using either a long time series or several short time series. Results show that the strategy does improve GRN inference with statistical significance. Also, the algorithm is robust to different variance and slight deviation of Gaussianity for the error terms.
Original languageEnglish
Title of host publication2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
DOIs
Publication statusPublished - 1 Jan 2015

Fingerprint

Genes
Time series
Merging
Time delay
Throughput
Experiments

Bibliographical note

Paper presented at the 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 12-15 August 2015, Niagara Falls, Canada.
ISBN of the source publication: 9781479969265

Cite this

LO, L. Y., WONG, M. L., LEE, K. H., & LEUNG, K. S. (2015). Exploiting modularity and hierarchical modularity to infer large causal gene regulatory network. In 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CIBCB.2015.7300317
LO, Leung Yau ; WONG, Man Leung ; LEE, Kin Hong ; LEUNG, Kwong Sak. / Exploiting modularity and hierarchical modularity to infer large causal gene regulatory network. 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015. Institute of Electrical and Electronics Engineers Inc., 2015.
@inproceedings{40057bf614cc4351b129f328d28ebe3c,
title = "Exploiting modularity and hierarchical modularity to infer large causal gene regulatory network",
abstract = "Gene regulatory network (GRN), which refers to the complex interactions with time delays between TFs and other genes, plays an important role in the working of the cell. Therefore inferring the GRN is crucial to studying diseases related to malfunctioning of the cell. Even with high-throughput technology, time series expression data is still limited compared to the network size, which poses significant challenge to inferring large GRN. Since GRNs are known to be modular, or hierarchically modular, we propose to exploit this by first inferring an initial GRN using CLINDE, then decomposing it into possibly overlapping subnetworks, then re-learning the subnetworks using either CLINDE or DD-lasso, and lastly merging the subnetworks. We have performed extensive experiments on synthetic data to test this strategy on both modular and hierarchically modular networks with 500 and 1000 genes, using either a long time series or several short time series. Results show that the strategy does improve GRN inference with statistical significance. Also, the algorithm is robust to different variance and slight deviation of Gaussianity for the error terms.",
author = "LO, {Leung Yau} and WONG, {Man Leung} and LEE, {Kin Hong} and LEUNG, {Kwong Sak}",
note = "Paper presented at the 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 12-15 August 2015, Niagara Falls, Canada. ISBN of the source publication: 9781479969265",
year = "2015",
month = "1",
day = "1",
doi = "10.1109/CIBCB.2015.7300317",
language = "English",
booktitle = "2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

LO, LY, WONG, ML, LEE, KH & LEUNG, KS 2015, Exploiting modularity and hierarchical modularity to infer large causal gene regulatory network. in 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/CIBCB.2015.7300317

Exploiting modularity and hierarchical modularity to infer large causal gene regulatory network. / LO, Leung Yau; WONG, Man Leung; LEE, Kin Hong; LEUNG, Kwong Sak.

2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015. Institute of Electrical and Electronics Engineers Inc., 2015.

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

TY - GEN

T1 - Exploiting modularity and hierarchical modularity to infer large causal gene regulatory network

AU - LO, Leung Yau

AU - WONG, Man Leung

AU - LEE, Kin Hong

AU - LEUNG, Kwong Sak

N1 - Paper presented at the 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 12-15 August 2015, Niagara Falls, Canada. ISBN of the source publication: 9781479969265

PY - 2015/1/1

Y1 - 2015/1/1

N2 - Gene regulatory network (GRN), which refers to the complex interactions with time delays between TFs and other genes, plays an important role in the working of the cell. Therefore inferring the GRN is crucial to studying diseases related to malfunctioning of the cell. Even with high-throughput technology, time series expression data is still limited compared to the network size, which poses significant challenge to inferring large GRN. Since GRNs are known to be modular, or hierarchically modular, we propose to exploit this by first inferring an initial GRN using CLINDE, then decomposing it into possibly overlapping subnetworks, then re-learning the subnetworks using either CLINDE or DD-lasso, and lastly merging the subnetworks. We have performed extensive experiments on synthetic data to test this strategy on both modular and hierarchically modular networks with 500 and 1000 genes, using either a long time series or several short time series. Results show that the strategy does improve GRN inference with statistical significance. Also, the algorithm is robust to different variance and slight deviation of Gaussianity for the error terms.

AB - Gene regulatory network (GRN), which refers to the complex interactions with time delays between TFs and other genes, plays an important role in the working of the cell. Therefore inferring the GRN is crucial to studying diseases related to malfunctioning of the cell. Even with high-throughput technology, time series expression data is still limited compared to the network size, which poses significant challenge to inferring large GRN. Since GRNs are known to be modular, or hierarchically modular, we propose to exploit this by first inferring an initial GRN using CLINDE, then decomposing it into possibly overlapping subnetworks, then re-learning the subnetworks using either CLINDE or DD-lasso, and lastly merging the subnetworks. We have performed extensive experiments on synthetic data to test this strategy on both modular and hierarchically modular networks with 500 and 1000 genes, using either a long time series or several short time series. Results show that the strategy does improve GRN inference with statistical significance. Also, the algorithm is robust to different variance and slight deviation of Gaussianity for the error terms.

UR - http://commons.ln.edu.hk/sw_master/6783

U2 - 10.1109/CIBCB.2015.7300317

DO - 10.1109/CIBCB.2015.7300317

M3 - Conference paper (refereed)

BT - 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

LO LY, WONG ML, LEE KH, LEUNG KS. Exploiting modularity and hierarchical modularity to infer large causal gene regulatory network. In 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015. Institute of Electrical and Electronics Engineers Inc. 2015 https://doi.org/10.1109/CIBCB.2015.7300317