Logical knowledge representation of regulatory relations in biomedical pathways

Sine Zambach, Jens Ulrik Hansen

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Knowledge on regulatory relations, in for example regulatory pathways in biology, is used widely in experiment design by biomedical researchers and in systems biology. The knowledge has typically either been represented through simple graphs or through very expressive differential equation simulations of smaller parts of a pathway.

In this work we suggest a knowledge representation of the most basic relations in regulatory processes regulates, positively regulates and negatively regulates in logics based on a semantic analysis. We discuss the usage of these relations in biology and in articial intelligence for hypothesis development in drug discovery.

Original languageEnglish
Title of host publicationITBAM 2010 : 1st International Conference on Information Technology in Bio- and Medical Informatics
EditorsS. Khuri, L. Lhotská, N. Pisanti
Number of pages14
PublisherSpringer
Publication date2010
Pages186-200
Publication statusPublished - 2010
Event1st International Conference on Information Technology in Bio- and Medical Informatics 2010 - Bilbao, Spain
Duration: 30 Aug 20103 Sep 2010
Conference number: 1

Conference

Conference1st International Conference on Information Technology in Bio- and Medical Informatics 2010
Number1
CountrySpain
CityBilbao
Period30/08/201003/09/2010
SeriesLecture Notes in Computer Science
Volume6266
ISSN0302-9743

Keywords

  • Formal relations
  • Knowledge representation
  • Semantic analysis
  • Biomedical ontologies
  • Applied logic
  • Formal ontologies
  • Knowledge Discovery

Cite this

Zambach, S., & Hansen, J. U. (2010). Logical knowledge representation of regulatory relations in biomedical pathways. In S. Khuri, L. Lhotská, & N. Pisanti (Eds.), ITBAM 2010: 1st International Conference on Information Technology in Bio- and Medical Informatics (pp. 186-200). Springer. Lecture Notes in Computer Science, Vol.. 6266
Zambach, Sine ; Hansen, Jens Ulrik. / Logical knowledge representation of regulatory relations in biomedical pathways. ITBAM 2010: 1st International Conference on Information Technology in Bio- and Medical Informatics. editor / S. Khuri ; L. Lhotská ; N. Pisanti. Springer, 2010. pp. 186-200 (Lecture Notes in Computer Science, Vol. 6266).
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title = "Logical knowledge representation of regulatory relations in biomedical pathways",
abstract = "Knowledge on regulatory relations, in for example regulatory pathways in biology, is used widely in experiment design by biomedical researchers and in systems biology. The knowledge has typically either been represented through simple graphs or through very expressive differential equation simulations of smaller parts of a pathway. In this work we suggest a knowledge representation of the most basic relations in regulatory processes regulates, positively regulates and negatively regulates in logics based on a semantic analysis. We discuss the usage of these relations in biology and in articial intelligence for hypothesis development in drug discovery.",
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Zambach, S & Hansen, JU 2010, Logical knowledge representation of regulatory relations in biomedical pathways. in S Khuri, L Lhotská & N Pisanti (eds), ITBAM 2010: 1st International Conference on Information Technology in Bio- and Medical Informatics. Springer, Lecture Notes in Computer Science, vol. 6266, pp. 186-200, Bilbao, Spain, 30/08/2010.

Logical knowledge representation of regulatory relations in biomedical pathways. / Zambach, Sine; Hansen, Jens Ulrik.

ITBAM 2010: 1st International Conference on Information Technology in Bio- and Medical Informatics. ed. / S. Khuri; L. Lhotská; N. Pisanti. Springer, 2010. p. 186-200 (Lecture Notes in Computer Science, Vol. 6266).

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

TY - GEN

T1 - Logical knowledge representation of regulatory relations in biomedical pathways

AU - Zambach, Sine

AU - Hansen, Jens Ulrik

PY - 2010

Y1 - 2010

N2 - Knowledge on regulatory relations, in for example regulatory pathways in biology, is used widely in experiment design by biomedical researchers and in systems biology. The knowledge has typically either been represented through simple graphs or through very expressive differential equation simulations of smaller parts of a pathway. In this work we suggest a knowledge representation of the most basic relations in regulatory processes regulates, positively regulates and negatively regulates in logics based on a semantic analysis. We discuss the usage of these relations in biology and in articial intelligence for hypothesis development in drug discovery.

AB - Knowledge on regulatory relations, in for example regulatory pathways in biology, is used widely in experiment design by biomedical researchers and in systems biology. The knowledge has typically either been represented through simple graphs or through very expressive differential equation simulations of smaller parts of a pathway. In this work we suggest a knowledge representation of the most basic relations in regulatory processes regulates, positively regulates and negatively regulates in logics based on a semantic analysis. We discuss the usage of these relations in biology and in articial intelligence for hypothesis development in drug discovery.

KW - Formelle relationer

KW - Vidensrepræsentation

KW - Biomedicinske ontologier

KW - Formelle ontologier

KW - Formal relations

KW - Knowledge representation

KW - Semantic analysis

KW - Biomedical ontologies

KW - Applied logic

KW - Formal ontologies

KW - Knowledge Discovery

M3 - Article in proceedings

SP - 186

EP - 200

BT - ITBAM 2010

A2 - Khuri, S.

A2 - Lhotská, L.

A2 - Pisanti, N.

PB - Springer

ER -

Zambach S, Hansen JU. Logical knowledge representation of regulatory relations in biomedical pathways. In Khuri S, Lhotská L, Pisanti N, editors, ITBAM 2010: 1st International Conference on Information Technology in Bio- and Medical Informatics. Springer. 2010. p. 186-200. (Lecture Notes in Computer Science, Vol. 6266).