Linked biomedical dataspace: Lessons learned integrating data for drug discovery

Ali Hasnain; Maulik R. Kamdar; Panagiotis Hasapis; Dimitris Zeginis; Claude N. Warren; Helena F. Deus; Dimitrios Ntalaperas; Konstantinos Tarabanis; Muntazir Mehdi; Stefan Decker

doi:10.1007/978-3-319-11964-9

Linked biomedical dataspace: Lessons learned integrating data for drug discovery

Ali Hasnain, Maulik R. Kamdar, Panagiotis Hasapis, Dimitris Zeginis, Claude N. Warren, Helena F. Deus, Dimitrios Ntalaperas, Konstantinos Tarabanis, Muntazir Mehdi, Stefan Decker

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

29 Scopus citations

Abstract

The increase in the volume and heterogeneity of biomedical data sources has motivated researchers to embrace Linked Data (LD) technologies to solve the ensuing integration challenges and enhance information discovery. As an integral part of the EU GRANATUM project, a Linked Biomedical Dataspace (LBDS) was developed to semantically interlink data from multiple sources and augment the design of in silico experiments for cancer chemoprevention drug discovery. The different components of the LBDS facilitate both the bioinformaticians and the biomedical researchers to publish, link, query and visually explore the heterogeneous datasets. We have extensively evaluated the usability of the entire platform. In this paper, we showcase three different workflows depicting real-world scenarios on the use of LBDS by the domain users to intuitively retrieve meaningful information from the integrated sources. We report the important lessons that we learned through the challenges encountered and our accumulated experience during the collaborative processes which would make it easier for LD practitioners to create such dataspaces in other domains. We also provide a concise set of generic recommendations to develop LD platforms useful for drug discovery.

Original language	English
Title of host publication	The SemanticWeb - ISWC 2014 - 13th International SemanticWeb Conference, Proceedings
Editors	Tania Tudorache, Craig Knoblock, Paul Groth, Carole Goble, Chris Welty, Abraham Bernstein, Peter Mika, Denny Vrandečić, Natasha Noy, Krzysztof Janowicz
Publisher	Springer Verlag
Pages	114-130
Number of pages	17
ISBN (Electronic)	9783319119632
DOIs	https://doi.org/10.1007/978-3-319-11964-9
State	Published - 2014
Externally published	Yes
Event	13th International Semantic Web Conference, ISWC 2014 - Riva del Garda, Italy Duration: Oct 19 2014 → Oct 23 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8796
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Semantic Web Conference, ISWC 2014
Country/Territory	Italy
City	Riva del Garda
Period	10/19/14 → 10/23/14

Keywords

Biomedical research
Drug discovery
Linked data
SPARQL federation
Visualization

Access to Document

10.1007/978-3-319-11964-9

Cite this

Hasnain, A., Kamdar, M. R., Hasapis, P., Zeginis, D., Warren, C. N., Deus, H. F., Ntalaperas, D., Tarabanis, K., Mehdi, M., & Decker, S. (2014). Linked biomedical dataspace: Lessons learned integrating data for drug discovery. In T. Tudorache, C. Knoblock, P. Groth, C. Goble, C. Welty, A. Bernstein, P. Mika, D. Vrandečić, N. Noy, & K. Janowicz (Eds.), The SemanticWeb - ISWC 2014 - 13th International SemanticWeb Conference, Proceedings (pp. 114-130). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8796). Springer Verlag. https://doi.org/10.1007/978-3-319-11964-9

Hasnain, Ali ; Kamdar, Maulik R. ; Hasapis, Panagiotis et al. / Linked biomedical dataspace : Lessons learned integrating data for drug discovery. The SemanticWeb - ISWC 2014 - 13th International SemanticWeb Conference, Proceedings. editor / Tania Tudorache ; Craig Knoblock ; Paul Groth ; Carole Goble ; Chris Welty ; Abraham Bernstein ; Peter Mika ; Denny Vrandečić ; Natasha Noy ; Krzysztof Janowicz. Springer Verlag, 2014. pp. 114-130 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The increase in the volume and heterogeneity of biomedical data sources has motivated researchers to embrace Linked Data (LD) technologies to solve the ensuing integration challenges and enhance information discovery. As an integral part of the EU GRANATUM project, a Linked Biomedical Dataspace (LBDS) was developed to semantically interlink data from multiple sources and augment the design of in silico experiments for cancer chemoprevention drug discovery. The different components of the LBDS facilitate both the bioinformaticians and the biomedical researchers to publish, link, query and visually explore the heterogeneous datasets. We have extensively evaluated the usability of the entire platform. In this paper, we showcase three different workflows depicting real-world scenarios on the use of LBDS by the domain users to intuitively retrieve meaningful information from the integrated sources. We report the important lessons that we learned through the challenges encountered and our accumulated experience during the collaborative processes which would make it easier for LD practitioners to create such dataspaces in other domains. We also provide a concise set of generic recommendations to develop LD platforms useful for drug discovery.",

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Hasnain, A, Kamdar, MR, Hasapis, P, Zeginis, D, Warren, CN, Deus, HF, Ntalaperas, D, Tarabanis, K, Mehdi, M & Decker, S 2014, Linked biomedical dataspace: Lessons learned integrating data for drug discovery. in T Tudorache, C Knoblock, P Groth, C Goble, C Welty, A Bernstein, P Mika, D Vrandečić, N Noy & K Janowicz (eds), The SemanticWeb - ISWC 2014 - 13th International SemanticWeb Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8796, Springer Verlag, pp. 114-130, 13th International Semantic Web Conference, ISWC 2014, Riva del Garda, Italy, 10/19/14. https://doi.org/10.1007/978-3-319-11964-9

Linked biomedical dataspace: Lessons learned integrating data for drug discovery. / Hasnain, Ali; Kamdar, Maulik R.; Hasapis, Panagiotis et al.
The SemanticWeb - ISWC 2014 - 13th International SemanticWeb Conference, Proceedings. ed. / Tania Tudorache; Craig Knoblock; Paul Groth; Carole Goble; Chris Welty; Abraham Bernstein; Peter Mika; Denny Vrandečić; Natasha Noy; Krzysztof Janowicz. Springer Verlag, 2014. p. 114-130 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8796).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Kamdar, Maulik R.

AU - Hasapis, Panagiotis

AU - Zeginis, Dimitris

AU - Warren, Claude N.

AU - Deus, Helena F.

AU - Ntalaperas, Dimitrios

AU - Tarabanis, Konstantinos

AU - Mehdi, Muntazir

AU - Decker, Stefan

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N2 - The increase in the volume and heterogeneity of biomedical data sources has motivated researchers to embrace Linked Data (LD) technologies to solve the ensuing integration challenges and enhance information discovery. As an integral part of the EU GRANATUM project, a Linked Biomedical Dataspace (LBDS) was developed to semantically interlink data from multiple sources and augment the design of in silico experiments for cancer chemoprevention drug discovery. The different components of the LBDS facilitate both the bioinformaticians and the biomedical researchers to publish, link, query and visually explore the heterogeneous datasets. We have extensively evaluated the usability of the entire platform. In this paper, we showcase three different workflows depicting real-world scenarios on the use of LBDS by the domain users to intuitively retrieve meaningful information from the integrated sources. We report the important lessons that we learned through the challenges encountered and our accumulated experience during the collaborative processes which would make it easier for LD practitioners to create such dataspaces in other domains. We also provide a concise set of generic recommendations to develop LD platforms useful for drug discovery.

AB - The increase in the volume and heterogeneity of biomedical data sources has motivated researchers to embrace Linked Data (LD) technologies to solve the ensuing integration challenges and enhance information discovery. As an integral part of the EU GRANATUM project, a Linked Biomedical Dataspace (LBDS) was developed to semantically interlink data from multiple sources and augment the design of in silico experiments for cancer chemoprevention drug discovery. The different components of the LBDS facilitate both the bioinformaticians and the biomedical researchers to publish, link, query and visually explore the heterogeneous datasets. We have extensively evaluated the usability of the entire platform. In this paper, we showcase three different workflows depicting real-world scenarios on the use of LBDS by the domain users to intuitively retrieve meaningful information from the integrated sources. We report the important lessons that we learned through the challenges encountered and our accumulated experience during the collaborative processes which would make it easier for LD practitioners to create such dataspaces in other domains. We also provide a concise set of generic recommendations to develop LD platforms useful for drug discovery.

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KW - SPARQL federation

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A2 - Tudorache, Tania

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Hasnain A, Kamdar MR, Hasapis P, Zeginis D, Warren CN, Deus HF et al. Linked biomedical dataspace: Lessons learned integrating data for drug discovery. In Tudorache T, Knoblock C, Groth P, Goble C, Welty C, Bernstein A, Mika P, Vrandečić D, Noy N, Janowicz K, editors, The SemanticWeb - ISWC 2014 - 13th International SemanticWeb Conference, Proceedings. Springer Verlag. 2014. p. 114-130. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-11964-9

Linked biomedical dataspace: Lessons learned integrating data for drug discovery

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