GIST: a Gibbs sampler to identify intracellular signal transduction pathways.

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

Research output: Contribution to journalArticle

Abstract

Identification of intracellular signal transduction pathways plays an important role in understanding the mechanisms of how cells respond to external stimuli. The availability of high throughput microarray expression data and accumulating knowledge of protein-protein interactions have provided us with useful information to infer condition-specific signal transduction pathways. We propose a novel method called Gibbs sampler to Infer Signal Transduction pathways (GIST) to search dys-regulated pathways from large-scale protein-protein interaction networks. GIST incorporates different knowledge sources to extract paths that are highly associated with biological phenotypes or clinical information. One of the most attractive features of GIST is that the algorithm will not only provide the single optimal path according to the defined cost function but also reveal multiple suboptimal paths as alternative solutions, which can be utilized to study the pathway crosstalk. As a proof-of-concept, we test our GIST algorithm on yeast PPI networks and the identified MAPK signaling pathways are well supported by existing biological knowledge. We also apply the GIST algorithm onto a breast cancer patient dataset to show its feasibility of identifying potential pathways for further biological validation.

Original languageEnglish (US)
Pages (from-to)2434-2437
Number of pages4
JournalConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
Volume2011
StatePublished - 2011
Externally publishedYes

Fingerprint

Signal transduction
Signal Transduction
Proteins
Protein Interaction Maps
Microarrays
Crosstalk
Yeasts
Cost functions
Yeast
Breast Neoplasms
Phenotype
Costs and Cost Analysis
Throughput
Availability

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

@article{a59e2de9ffe348f087e32478732ffc10,
title = "GIST: a Gibbs sampler to identify intracellular signal transduction pathways.",
abstract = "Identification of intracellular signal transduction pathways plays an important role in understanding the mechanisms of how cells respond to external stimuli. The availability of high throughput microarray expression data and accumulating knowledge of protein-protein interactions have provided us with useful information to infer condition-specific signal transduction pathways. We propose a novel method called Gibbs sampler to Infer Signal Transduction pathways (GIST) to search dys-regulated pathways from large-scale protein-protein interaction networks. GIST incorporates different knowledge sources to extract paths that are highly associated with biological phenotypes or clinical information. One of the most attractive features of GIST is that the algorithm will not only provide the single optimal path according to the defined cost function but also reveal multiple suboptimal paths as alternative solutions, which can be utilized to study the pathway crosstalk. As a proof-of-concept, we test our GIST algorithm on yeast PPI networks and the identified MAPK signaling pathways are well supported by existing biological knowledge. We also apply the GIST algorithm onto a breast cancer patient dataset to show its feasibility of identifying potential pathways for further biological validation.",
author = "Jinghua Gu and Chen Wang and Shih, {Ie Ming} and Wang, {Tian Li} and Yue Wang and Robert Clarke and Jianhua Xuan",
year = "2011",
language = "English (US)",
volume = "2011",
pages = "2434--2437",
journal = "Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference",
issn = "1557-170X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - GIST

T2 - a Gibbs sampler to identify intracellular signal transduction pathways.

AU - Gu, Jinghua

AU - Wang, Chen

AU - Shih, Ie Ming

AU - Wang, Tian Li

AU - Wang, Yue

AU - Clarke, Robert

AU - Xuan, Jianhua

PY - 2011

Y1 - 2011

N2 - Identification of intracellular signal transduction pathways plays an important role in understanding the mechanisms of how cells respond to external stimuli. The availability of high throughput microarray expression data and accumulating knowledge of protein-protein interactions have provided us with useful information to infer condition-specific signal transduction pathways. We propose a novel method called Gibbs sampler to Infer Signal Transduction pathways (GIST) to search dys-regulated pathways from large-scale protein-protein interaction networks. GIST incorporates different knowledge sources to extract paths that are highly associated with biological phenotypes or clinical information. One of the most attractive features of GIST is that the algorithm will not only provide the single optimal path according to the defined cost function but also reveal multiple suboptimal paths as alternative solutions, which can be utilized to study the pathway crosstalk. As a proof-of-concept, we test our GIST algorithm on yeast PPI networks and the identified MAPK signaling pathways are well supported by existing biological knowledge. We also apply the GIST algorithm onto a breast cancer patient dataset to show its feasibility of identifying potential pathways for further biological validation.

AB - Identification of intracellular signal transduction pathways plays an important role in understanding the mechanisms of how cells respond to external stimuli. The availability of high throughput microarray expression data and accumulating knowledge of protein-protein interactions have provided us with useful information to infer condition-specific signal transduction pathways. We propose a novel method called Gibbs sampler to Infer Signal Transduction pathways (GIST) to search dys-regulated pathways from large-scale protein-protein interaction networks. GIST incorporates different knowledge sources to extract paths that are highly associated with biological phenotypes or clinical information. One of the most attractive features of GIST is that the algorithm will not only provide the single optimal path according to the defined cost function but also reveal multiple suboptimal paths as alternative solutions, which can be utilized to study the pathway crosstalk. As a proof-of-concept, we test our GIST algorithm on yeast PPI networks and the identified MAPK signaling pathways are well supported by existing biological knowledge. We also apply the GIST algorithm onto a breast cancer patient dataset to show its feasibility of identifying potential pathways for further biological validation.

UR - http://www.scopus.com/inward/record.url?scp=84862246030&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84862246030&partnerID=8YFLogxK

M3 - Article

C2 - 22254833

AN - SCOPUS:84862246030

VL - 2011

SP - 2434

EP - 2437

JO - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

JF - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

SN - 1557-170X

ER -