DocumentCode :
1508002
Title :
Discovering Interesting Molecular Substructures for Molecular Classification
Author :
Lam, Winnie W M ; Chan, Keith C C
Author_Institution :
Dept. of Comput., Hong Kong Polytech. Univ., Hung Horn, China
Volume :
9
Issue :
2
fYear :
2010
fDate :
6/1/2010 12:00:00 AM
Firstpage :
77
Lastpage :
89
Abstract :
Given a set of molecular structure data preclassified into a number of classes, the molecular classification problem is concerned with the discovering of interesting structural patterns in the data so that “unseen” molecules not originally in the dataset can be accurately classified. To tackle the problem, interesting molecular substructures have to be discovered and this is done typically by first representing molecular structures in molecular graphs, and then, using graph-mining algorithms to discover frequently occurring subgraphs in them. These subgraphs are then used to characterize different classes for molecular classification. While such an approach can be very effective, it should be noted that a substructure that occurs frequently in one class may also does occur in another. The discovering of frequent subgraphs for molecular classification may, therefore, not always be the most effective. In this paper, we propose a novel technique called mining interesting substructures in molecular data for classification (MISMOC) that can discover interesting frequent subgraphs not just for the characterization of a molecular class but also for the distinguishing of it from the others. Using a test statistic, MISMOC screens each frequent subgraph to determine if they are interesting. For those that are interesting, their degrees of interestingness are determined using an information-theoretic measure. When classifying an unseen molecule, its structure is then matched against the interesting subgraphs in each class and a total interestingness measure for the unseen molecule to be classified into a particular class is determined, which is based on the interestingness of each matched subgraphs. The performance of MISMOC is evaluated using both artificial and real datasets, and the results show that it can be an effective approach for molecular classification.
Keywords :
bioinformatics; data mining; graph theory; molecular biophysics; molecular configurations; MISMOC technique; graph mining algorithm; molecular classification problem; molecular graph; molecular substructure; unseen molecule; Frequent subgraph; graph mining; interestingness; molecular classification; molecular structures; Algorithms; Area Under Curve; Computational Biology; Computer Simulation; Data Mining; Databases, Factual; Models, Molecular; Molecular Structure;
fLanguage :
English
Journal_Title :
NanoBioscience, IEEE Transactions on
Publisher :
ieee
ISSN :
1536-1241
Type :
jour
DOI :
10.1109/TNB.2010.2042609
Filename :
5477194
Link To Document :
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