An algorithm to learn causal relations between genes from steady state data: Simulation and its application to melanoma dataset

Xin Zhang, Chitta Baral, Seungchan Kim

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

12 Citations (Scopus)

Abstract

In recent years, a few researchers have challenged past dogma and suggested methods (such as the IC algorithm) for inferring causal relationship among variables using steady state observations. In this paper, we present a modified IC (mIC) algorithm that uses entropy to test conditional independence and combines the steady state data with partial prior knowledge of topological ordering in gene regulatory network, for jointly learning the causal relationship among genes. We evaluate our mIC algorithm using the simulated data. The results show that the precision and recall rates are significantly improved compared with using IC algorithm. Finally, we apply the mIC algorithm to microarray data for melanoma. The algorithm identified the important causal relations associated with WNT5A, a gene playing an important role in melanoma, verified by the literatures.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages524-534
Number of pages11
Volume3581 LNAI
StatePublished - 2005
Event10th Conference on Artificial Intelligence in Medicine, AIME 2005 - Aberdeen, United Kingdom
Duration: Jul 23 2005Jul 27 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3581 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other10th Conference on Artificial Intelligence in Medicine, AIME 2005
CountryUnited Kingdom
CityAberdeen
Period7/23/057/27/05

Fingerprint

Melanoma
Genes
Gene
Simulation
Conditional Independence
Gene Regulatory Networks
Gene Regulatory Network
Entropy
Microarrays
Microarray Data
Prior Knowledge
Datasets
Research Personnel
Learning
Partial
Evaluate

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Zhang, X., Baral, C., & Kim, S. (2005). An algorithm to learn causal relations between genes from steady state data: Simulation and its application to melanoma dataset. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3581 LNAI, pp. 524-534). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3581 LNAI).

An algorithm to learn causal relations between genes from steady state data : Simulation and its application to melanoma dataset. / Zhang, Xin; Baral, Chitta; Kim, Seungchan.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3581 LNAI 2005. p. 524-534 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3581 LNAI).

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

Zhang, X, Baral, C & Kim, S 2005, An algorithm to learn causal relations between genes from steady state data: Simulation and its application to melanoma dataset. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3581 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3581 LNAI, pp. 524-534, 10th Conference on Artificial Intelligence in Medicine, AIME 2005, Aberdeen, United Kingdom, 7/23/05.
Zhang X, Baral C, Kim S. An algorithm to learn causal relations between genes from steady state data: Simulation and its application to melanoma dataset. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3581 LNAI. 2005. p. 524-534. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Zhang, Xin ; Baral, Chitta ; Kim, Seungchan. / An algorithm to learn causal relations between genes from steady state data : Simulation and its application to melanoma dataset. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3581 LNAI 2005. pp. 524-534 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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