Detecting aberrant signal transduction pathways from high-throughput data using GIST algorithm

Jinghua Gu, Jianhua Xuan, Chen Wang, Li Chen, Tian Li Wang, Ie Ming Shih

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

It is biologically important to integrate high-throughput data to identify aberrant signal transduction pathways in cancer research. The high-throughput data acquired from The Cancer Genome Atlas (TCGA) Project offer a comprehensive picture of the genomic and transcriptional changes across hundreds of tumor samples. In this paper we propose a novel method, namely Gibbs sampler to Infer Signal Transduction pathways (GIST), to detect aberrant pathways that are highly associated with biological phenotypes or clinical information. GIST endeavors to estimate the edge probability by using a Markov Chain Monte Carlo (MCMC) method (i.e., a Gibbs sampling strategy). Through the sampling process, GIST is able to infer the correct signal transduction direction because the sampled edge probabilities are jointly determined by gene expression data and network topology. We first tested the efficacy of the GIST algorithm on yeast data and successfully uncovered several biologically meaningful signaling pathways. A case study on TCGA ovarian cancer data was further designed, aiming to unravel diverse signaling pathways associated with the development of ovarian cancer. The experimental results demonstrated the feasibility of applying GIST to identify and prioritize important signaling pathways in ovarian cancer for further biological validation.

Original languageEnglish (US)
Title of host publication2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012
Pages267-274
Number of pages8
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012 - San Diego, CA, United States
Duration: May 9 2012May 12 2012

Other

Other2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012
CountryUnited States
CitySan Diego, CA
Period5/9/125/12/12

Fingerprint

Signal transduction
Throughput
Genes
Sampling
Gene expression
Yeast
Markov processes
Tumors
Monte Carlo methods
Topology

Keywords

  • gene expression
  • Gibbs sampling
  • Markov chain Mote Carlo
  • protein-protein interaction
  • signal transduction pathway

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Gu, J., Xuan, J., Wang, C., Chen, L., Wang, T. L., & Shih, I. M. (2012). Detecting aberrant signal transduction pathways from high-throughput data using GIST algorithm. In 2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012 (pp. 267-274). [6217240] https://doi.org/10.1109/CIBCB.2012.6217240

Detecting aberrant signal transduction pathways from high-throughput data using GIST algorithm. / Gu, Jinghua; Xuan, Jianhua; Wang, Chen; Chen, Li; Wang, Tian Li; Shih, Ie Ming.

2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012. 2012. p. 267-274 6217240.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Gu, J, Xuan, J, Wang, C, Chen, L, Wang, TL & Shih, IM 2012, Detecting aberrant signal transduction pathways from high-throughput data using GIST algorithm. in 2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012., 6217240, pp. 267-274, 2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012, San Diego, CA, United States, 5/9/12. https://doi.org/10.1109/CIBCB.2012.6217240
Gu J, Xuan J, Wang C, Chen L, Wang TL, Shih IM. Detecting aberrant signal transduction pathways from high-throughput data using GIST algorithm. In 2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012. 2012. p. 267-274. 6217240 https://doi.org/10.1109/CIBCB.2012.6217240
Gu, Jinghua ; Xuan, Jianhua ; Wang, Chen ; Chen, Li ; Wang, Tian Li ; Shih, Ie Ming. / Detecting aberrant signal transduction pathways from high-throughput data using GIST algorithm. 2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012. 2012. pp. 267-274
@inproceedings{2310ad828a514f268fdf00ca38d18472,
title = "Detecting aberrant signal transduction pathways from high-throughput data using GIST algorithm",
abstract = "It is biologically important to integrate high-throughput data to identify aberrant signal transduction pathways in cancer research. The high-throughput data acquired from The Cancer Genome Atlas (TCGA) Project offer a comprehensive picture of the genomic and transcriptional changes across hundreds of tumor samples. In this paper we propose a novel method, namely Gibbs sampler to Infer Signal Transduction pathways (GIST), to detect aberrant pathways that are highly associated with biological phenotypes or clinical information. GIST endeavors to estimate the edge probability by using a Markov Chain Monte Carlo (MCMC) method (i.e., a Gibbs sampling strategy). Through the sampling process, GIST is able to infer the correct signal transduction direction because the sampled edge probabilities are jointly determined by gene expression data and network topology. We first tested the efficacy of the GIST algorithm on yeast data and successfully uncovered several biologically meaningful signaling pathways. A case study on TCGA ovarian cancer data was further designed, aiming to unravel diverse signaling pathways associated with the development of ovarian cancer. The experimental results demonstrated the feasibility of applying GIST to identify and prioritize important signaling pathways in ovarian cancer for further biological validation.",
keywords = "gene expression, Gibbs sampling, Markov chain Mote Carlo, protein-protein interaction, signal transduction pathway",
author = "Jinghua Gu and Jianhua Xuan and Chen Wang and Li Chen and Wang, {Tian Li} and Shih, {Ie Ming}",
year = "2012",
doi = "10.1109/CIBCB.2012.6217240",
language = "English (US)",
isbn = "9781467311892",
pages = "267--274",
booktitle = "2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012",

}

TY - GEN

T1 - Detecting aberrant signal transduction pathways from high-throughput data using GIST algorithm

AU - Gu, Jinghua

AU - Xuan, Jianhua

AU - Wang, Chen

AU - Chen, Li

AU - Wang, Tian Li

AU - Shih, Ie Ming

PY - 2012

Y1 - 2012

N2 - It is biologically important to integrate high-throughput data to identify aberrant signal transduction pathways in cancer research. The high-throughput data acquired from The Cancer Genome Atlas (TCGA) Project offer a comprehensive picture of the genomic and transcriptional changes across hundreds of tumor samples. In this paper we propose a novel method, namely Gibbs sampler to Infer Signal Transduction pathways (GIST), to detect aberrant pathways that are highly associated with biological phenotypes or clinical information. GIST endeavors to estimate the edge probability by using a Markov Chain Monte Carlo (MCMC) method (i.e., a Gibbs sampling strategy). Through the sampling process, GIST is able to infer the correct signal transduction direction because the sampled edge probabilities are jointly determined by gene expression data and network topology. We first tested the efficacy of the GIST algorithm on yeast data and successfully uncovered several biologically meaningful signaling pathways. A case study on TCGA ovarian cancer data was further designed, aiming to unravel diverse signaling pathways associated with the development of ovarian cancer. The experimental results demonstrated the feasibility of applying GIST to identify and prioritize important signaling pathways in ovarian cancer for further biological validation.

AB - It is biologically important to integrate high-throughput data to identify aberrant signal transduction pathways in cancer research. The high-throughput data acquired from The Cancer Genome Atlas (TCGA) Project offer a comprehensive picture of the genomic and transcriptional changes across hundreds of tumor samples. In this paper we propose a novel method, namely Gibbs sampler to Infer Signal Transduction pathways (GIST), to detect aberrant pathways that are highly associated with biological phenotypes or clinical information. GIST endeavors to estimate the edge probability by using a Markov Chain Monte Carlo (MCMC) method (i.e., a Gibbs sampling strategy). Through the sampling process, GIST is able to infer the correct signal transduction direction because the sampled edge probabilities are jointly determined by gene expression data and network topology. We first tested the efficacy of the GIST algorithm on yeast data and successfully uncovered several biologically meaningful signaling pathways. A case study on TCGA ovarian cancer data was further designed, aiming to unravel diverse signaling pathways associated with the development of ovarian cancer. The experimental results demonstrated the feasibility of applying GIST to identify and prioritize important signaling pathways in ovarian cancer for further biological validation.

KW - gene expression

KW - Gibbs sampling

KW - Markov chain Mote Carlo

KW - protein-protein interaction

KW - signal transduction pathway

UR - http://www.scopus.com/inward/record.url?scp=84864037011&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84864037011&partnerID=8YFLogxK

U2 - 10.1109/CIBCB.2012.6217240

DO - 10.1109/CIBCB.2012.6217240

M3 - Conference contribution

SN - 9781467311892

SP - 267

EP - 274

BT - 2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012

ER -