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Comparative analysis of the use of chemoinformatics-based and substructure-based descriptors for quantitative structure-activity relationship (QSAR) modeling
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
2013 (engelsk)Inngår i: Intelligent Data Analysis, ISSN 1088-467X, E-ISSN 1571-4128, Vol. 17, nr 2, 327-341 s.Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
2013. Vol. 17, nr 2, 327-341 s.
Emneord [en]
QSAR modeling, chemical descriptors, graph mining
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
URN: urn:nbn:se:su:diva-91543DOI: 10.3233/IDA-130581ISI: 000319344300010OAI: oai:DiVA.org:su-91543DiVA: diva2:634470
Forskningsfinansiär
Swedish Foundation for Strategic Research , IIS11-0053
Merknad

AuthorCount:3;

Tilgjengelig fra: 2013-07-01 Laget: 2013-06-28 Sist oppdatert: 2014-02-26bibliografisk kontrollert
Inngår i avhandling
1. Learning predictive models from graph data using pattern mining
Åpne denne publikasjonen i ny fane eller vindu >>Learning predictive models from graph data using pattern mining
2014 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2014. 118 s.
Serie
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 14-003
Emneord
Machine Learning, Graph Data, Pattern Mining, Classification, Regression, Predictive Models
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
urn:nbn:se:su:diva-100713 (URN)978-91-7447-837-2 (ISBN)
Disputas
2014-03-25, room B, Forum, Isafjordsgatan 39, Kista, 13:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2014-03-03 Laget: 2014-02-11 Sist oppdatert: 2014-03-04bibliografisk kontrollert

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