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Saeed F.,University of Technology Malaysia | Saeed F.,Sanhan Community College | Salim N.,University of Technology Malaysia | Abdo A.,Hodeidah University | Abdo A.,French Institute for Research in Computer Science and Automation
Molecular Informatics | Year: 2013

Many consensus clustering methods have been applied in different areas such as pattern recognition, machine learning, information theory and bioinformatics. However, few methods have been used for chemical compounds clustering. In this paper, an information theory and voting based algorithm (Adaptive Cumulative Voting-based Aggregation Algorithm A-CVAA) was examined for combining multiple clusterings of chemical structures. The effectiveness of clusterings was evaluated based on the ability of the clustering method to separate active from inactive molecules in each cluster, and the results were compared with Ward's method. The chemical dataset MDL Drug Data Report (MDDR) and the Maximum Unbiased Validation (MUV) dataset were used. Experiments suggest that the adaptive cumulative voting-based consensus method can improve the effectiveness of combining multiple clusterings of chemical structures. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. Source


Saeed F.,University of Technology Malaysia | Saeed F.,Sanhan Community College | Salim N.,University of Technology Malaysia | Abdo A.,Hodeidah University | Hentabli H.,University of Technology Malaysia
Communications in Computer and Information Science | Year: 2012

Many types of clustering techniques for chemical file structures have been used in the literature, but it is known that any single method will not always give the best results for all types of applications. Recent work on consensus clustering methods is motivated because of the successes of combining multiple classifiers in many areas and the ability of consensus clustering to improve the robustness, novelty, consistency and stability of clustering. In this paper, Cluster-based Similarity Partitioning Algorithm (CSPA) was examined for improving the quality of chemical structures clustering. The effectiveness of clustering was evaluated based on the ability to separate active from inactive molecules in each cluster and compared with the Ward's clustering method. The chemical dataset MDL Drug Data Report (MDDR) database was used for experiments. The results were obtained by combining multiple individual clusterings with different distance measures. Experiments suggest that the effectiveness of consensus partition depends on the consensus generation step so that the effective individual clusterings with different distance measures can obtain more robust and stable consensus clustering. © Springer-Verlag Berlin Heidelberg 2012. Source


Saeed F.,University of Technology Malaysia | Saeed F.,Sanhan Community College | Salim N.,University of Technology Malaysia | Abdo A.,Hodeidah University | Abdo A.,French Institute for Research in Computer Science and Automation
Journal of Chemical Information and Modeling | Year: 2013

The goal of consensus clustering methods is to find a consensus partition that optimally summarizes an ensemble and improves the quality of clustering compared with single clustering algorithms. In this paper, an enhanced voting-based consensus method was introduced and compared with other consensus clustering methods, including co-association-based, graph-based, and voting-based consensus methods. The MDDR and MUV data sets were used for the experiments and were represented by three 2D fingerprints: ALOGP, ECFP-4, and ECFC-4. The results were evaluated based on the ability of the clustering method to separate active from inactive molecules in each cluster using four criteria: F-measure, Quality Partition Index (QPI), Rand Index (RI), and Fowlkes-Mallows Index (FMI). The experiments suggest that the consensus methods can deliver significant improvements for the effectiveness of chemical structures clustering. © 2013 American Chemical Society. Source


Saeed F.,University of Technology Malaysia | Saeed F.,Sanhan Community College | Ahmed A.,Karary University | Shamsir M.S.,University of Technology Malaysia | Salim N.,University of Technology Malaysia
Journal of Computer-Aided Molecular Design | Year: 2014

The cluster-based compound selection is used in the lead identification process of drug discovery and design. Many clustering methods have been used for chemical databases, but there is no clustering method that can obtain the best results under all circumstances. However, little attention has been focused on the use of combination methods for chemical structure clustering, which is known as consensus clustering. Recently, consensus clustering has been used in many areas including bioinformatics, machine learning and information theory. This process can improve the robustness, stability, consistency and novelty of clustering. For chemical databases, different consensus clustering methods have been used including the co-association matrix-based, graph-based, hypergraph-based and voting-based methods. In this paper, a weighted cumulative voting-based aggregation algorithm (W-CVAA) was developed. The MDL Drug Data Report (MDDR) benchmark chemical dataset was used in the experiments and represented by the AlogP and ECPF-4 descriptors. The results from the clustering methods were evaluated by the ability of the clustering to separate biologically active molecules in each cluster from inactive ones using different criteria, and the effectiveness of the consensus clustering was compared to that of Ward's method, which is the current standard clustering method in chemoinformatics. This study indicated that weighted voting-based consensus clustering can overcome the limitations of the existing voting-based methods and improve the effectiveness of combining multiple clusterings of chemical structures. © 2014 Springer International Publishing. Source


Saeed F.,University of Technology Malaysia | Saeed F.,Sanhan Community College | Hentabli H.,University of Technology Malaysia
Jurnal Teknologi (Sciences and Engineering) | Year: 2013

The consensus clustering has shown capability to improve the robustness, novelty and stability of individual clusterings in many areas including chemoinformatics. In this paper, graph-based consensus method (cluster-based similarity partitioning algorithm CSPA) and soft consensus clustering were examined for combining multiple clusterings of chemical structures. The clustering is evaluated based on the ability to separate active from inactive molecules in each cluster. Experiments suggest that the effectiveness of soft consensus method can obtain better results than the hard consensus method (CSPA). © 2013 Penerbit UTM Press. All rights reserved. Source

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