Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Comparative analysis of the use of chemoinformatics-based and substructure-based descriptors for quantitative structure-activity relationship (QSAR) modeling
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2013 (English)In: Intelligent Data Analysis, ISSN 1088-467X, E-ISSN 1571-4128, Vol. 17, no 2, 327-341 p.Article in journal (Refereed) Published
Abstract [en]

Quantitative structure-activity relationship (QSAR) models have gained popularity in the pharmaceutical industry due to their potential to substantially decrease drug development costs by reducing expensive laboratory and clinical tests. QSAR modeling consists of two fundamental steps, namely, descriptor discovery and model building. Descriptor discovery methods are either based on chemical domain knowledge or purely data-driven. The former, chemoinformatics-based, and the latter, substructures-based, methods for QSAR modeling, have been developed quite independently. As a consequence, evaluations involving both types of descriptor discovery method are rarely seen. In this study, a comparative analysis of chemoinformatics-based and substructure-based approaches is presented. Two chemoinformatics-based approaches; ECFI and SELMA, are compared to five approaches for substructure discovery; CP, graphSig, MFI, MoFa and SUBDUE, using 18 QSAR datasets. The empirical investigation shows that one of the chemo-informatics-based approaches, ECFI, results in significantly more accurate models compared to all other methods, when used on their own. Results from combining descriptor sets are also presented, showing that the addition of ECFI descriptors to any other descriptor set leads to improved predictive performance for that set, while the use of ECFI descriptors in many cases also can be improved by adding descriptors generated by the other methods.

Place, publisher, year, edition, pages
2013. Vol. 17, no 2, 327-341 p.
Keyword [en]
QSAR modeling, chemical descriptors, graph mining
National Category
Computer Science
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-91543DOI: 10.3233/IDA-130581ISI: 000319344300010OAI: oai:DiVA.org:su-91543DiVA: diva2:634470
Funder
Swedish Foundation for Strategic Research , IIS11-0053
Note

AuthorCount:3;

Available from: 2013-07-01 Created: 2013-06-28 Last updated: 2017-12-06Bibliographically approved
In thesis
1. Learning predictive models from graph data using pattern mining
Open this publication in new window or tab >>Learning predictive models from graph data using pattern mining
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Learning from graphs has become a popular research area due to the ubiquity of graph data representing web pages, molecules, social networks, protein interaction networks etc. However, standard graph learning approaches are often challenged by the computational cost involved in the learning process, due to the richness of the representation. Attempts made to improve their efficiency are often associated with the risk of degrading the performance of the predictive models, creating tradeoffs between the efficiency and effectiveness of the learning. Such a situation is analogous to an optimization problem with two objectives, efficiency and effectiveness, where improving one objective without the other objective being worse off is a better solution, called a Pareto improvement. In this thesis, it is investigated how to improve the efficiency and effectiveness of learning from graph data using pattern mining methods. Two objectives are set where one concerns how to improve the efficiency of pattern mining without reducing the predictive performance of the learning models, and the other objective concerns how to improve predictive performance without increasing the complexity of pattern mining. The employed research method mainly follows a design science approach, including the development and evaluation of artifacts. The contributions of this thesis include a data representation language that can be characterized as a form in between sequences and itemsets, where the graph information is embedded within items. Several studies, each of which look for Pareto improvements in efficiency and effectiveness are conducted using sets of small graphs. Summarizing the findings, some of the proposed methods, namely maximal frequent itemset mining and constraint based itemset mining, result in a dramatically increased efficiency of learning, without decreasing the predictive performance of the resulting models. It is also shown that additional background knowledge can be used to enhance the performance of the predictive models, without increasing the complexity of the graphs.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2014. 118 p.
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 14-003
Keyword
Machine Learning, Graph Data, Pattern Mining, Classification, Regression, Predictive Models
National Category
Computer Science
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-100713 (URN)978-91-7447-837-2 (ISBN)
Public defence
2014-03-25, room B, Forum, Isafjordsgatan 39, Kista, 13:00 (English)
Opponent
Supervisors
Available from: 2014-03-03 Created: 2014-02-11 Last updated: 2014-03-04Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Karunaratne, ThashmeeBoström, Henrik
By organisation
Department of Computer and Systems Sciences
In the same journal
Intelligent Data Analysis
Computer Science

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 23 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf