TNMCA: Generation and application of network motif based inference models for drug repositioning

Jaejoon Choi, Kwangmin Kim, Min Song, Doheon Lee

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

1 Citation (Scopus)

Abstract

Since the increase of the public biomedical data, Undiscovered Public Knowledge (UPK, proposed by Swanson) became an important research topic in the biological field. Drug repositioning is one of famous UPK tasks which infer alternative indications for approved drugs. Many researchers tried to find novel candidates of existing drugs, but these previous works are not fully automated which required manual modulations to desired tasks, and was not able to cover various biomedical entities. In addition, they had inference limitations that those works could infer only pre-defined cases using limited patterns. In this paper, we propose the Typed Network Motif Comparison Algorithm (TNMCA) to discover novel drug indications using topological patterns of data. Typed network motifs (TNM) are connected sub-graphs of data, which store types of data, instead of values of data. While previous researches depends on ABC model (or extension of it), TNMCA utilizes more generalized patterns as its inference models. Also, TNMCA can infer not only an existence of interaction, but also the type of the interaction. TNMCA is suited for multi-level biomedical interaction data as TNMs depend on the different types of entities and relations. We apply TNMCA to a public database, Comparative Toxicogenomics Database (CTD), to validate our method. The results show that TNMCA could infer meaningful indications with high performance (AUC=0.7469) compared to the ABC model (AUC=0.7050).

Original languageEnglish
Title of host publicationDTMBIO'12 - Proceedings of the 6th ACM International Workshop on Data and Text Mining in Biomedical Informatics, Co-located with CIKM 2012
Pages61-68
Number of pages8
DOIs
Publication statusPublished - 2012 Dec 10
Event6th ACM International Workshop on Data and Text Mining in Biomedical Informatics, DTMBIO 2012, in Conjunction with the 21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States
Duration: 2012 Oct 292012 Oct 29

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other6th ACM International Workshop on Data and Text Mining in Biomedical Informatics, DTMBIO 2012, in Conjunction with the 21st ACM International Conference on Information and Knowledge Management, CIKM 2012
CountryUnited States
CityMaui, HI
Period12/10/2912/10/29

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Inference
Drugs
Interaction
Data base
High performance
Graph

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

Cite this

Choi, J., Kim, K., Song, M., & Lee, D. (2012). TNMCA: Generation and application of network motif based inference models for drug repositioning. In DTMBIO'12 - Proceedings of the 6th ACM International Workshop on Data and Text Mining in Biomedical Informatics, Co-located with CIKM 2012 (pp. 61-68). (International Conference on Information and Knowledge Management, Proceedings). https://doi.org/10.1145/2390068.2390081
Choi, Jaejoon ; Kim, Kwangmin ; Song, Min ; Lee, Doheon. / TNMCA : Generation and application of network motif based inference models for drug repositioning. DTMBIO'12 - Proceedings of the 6th ACM International Workshop on Data and Text Mining in Biomedical Informatics, Co-located with CIKM 2012. 2012. pp. 61-68 (International Conference on Information and Knowledge Management, Proceedings).
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abstract = "Since the increase of the public biomedical data, Undiscovered Public Knowledge (UPK, proposed by Swanson) became an important research topic in the biological field. Drug repositioning is one of famous UPK tasks which infer alternative indications for approved drugs. Many researchers tried to find novel candidates of existing drugs, but these previous works are not fully automated which required manual modulations to desired tasks, and was not able to cover various biomedical entities. In addition, they had inference limitations that those works could infer only pre-defined cases using limited patterns. In this paper, we propose the Typed Network Motif Comparison Algorithm (TNMCA) to discover novel drug indications using topological patterns of data. Typed network motifs (TNM) are connected sub-graphs of data, which store types of data, instead of values of data. While previous researches depends on ABC model (or extension of it), TNMCA utilizes more generalized patterns as its inference models. Also, TNMCA can infer not only an existence of interaction, but also the type of the interaction. TNMCA is suited for multi-level biomedical interaction data as TNMs depend on the different types of entities and relations. We apply TNMCA to a public database, Comparative Toxicogenomics Database (CTD), to validate our method. The results show that TNMCA could infer meaningful indications with high performance (AUC=0.7469) compared to the ABC model (AUC=0.7050).",
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Choi, J, Kim, K, Song, M & Lee, D 2012, TNMCA: Generation and application of network motif based inference models for drug repositioning. in DTMBIO'12 - Proceedings of the 6th ACM International Workshop on Data and Text Mining in Biomedical Informatics, Co-located with CIKM 2012. International Conference on Information and Knowledge Management, Proceedings, pp. 61-68, 6th ACM International Workshop on Data and Text Mining in Biomedical Informatics, DTMBIO 2012, in Conjunction with the 21st ACM International Conference on Information and Knowledge Management, CIKM 2012, Maui, HI, United States, 12/10/29. https://doi.org/10.1145/2390068.2390081

TNMCA : Generation and application of network motif based inference models for drug repositioning. / Choi, Jaejoon; Kim, Kwangmin; Song, Min; Lee, Doheon.

DTMBIO'12 - Proceedings of the 6th ACM International Workshop on Data and Text Mining in Biomedical Informatics, Co-located with CIKM 2012. 2012. p. 61-68 (International Conference on Information and Knowledge Management, Proceedings).

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

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Choi J, Kim K, Song M, Lee D. TNMCA: Generation and application of network motif based inference models for drug repositioning. In DTMBIO'12 - Proceedings of the 6th ACM International Workshop on Data and Text Mining in Biomedical Informatics, Co-located with CIKM 2012. 2012. p. 61-68. (International Conference on Information and Knowledge Management, Proceedings). https://doi.org/10.1145/2390068.2390081