Prof. LEUNG Kwong SakLee, Kin HongKin HongLeeWang, Jin-FengJin-FengWangNg, Eddie Y. T.Eddie Y. T.NgChan, Henry L. Y.Henry L. Y.ChanTsui, Stephen K. W.Stephen K. W.TsuiMok, Tony S. K.Tony S. K.MokTse, Pete Chi-HangPete Chi-HangTseSung, Joseph Jao-YiuJoseph Jao-YiuSung2023-03-232023-03-232011IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2011, Vol. 8 ( 2), pp. 428 - 440, Article number 476013415455963http://hdl.handle.net/20.500.11861/7535Extraction of meaningful information from large experimental data sets is a key element in bioinformatics research. One of the challenges is to identify genomic markers in Hepatitis B Virus (HBV) that are associated with HCC (liver cancer) development by comparing the complete genomic sequences of HBV among patients with HCC and those without HCC. In this study, a data mining framework, which includes molecular evolution analysis, clustering, feature selection, classifier learning, and classification, is introduced. Our research group has collected HBV DNA sequences, either genotype B or C, from over 200 patients specifically for this project. In the molecular evolution analysis and clustering, three subgroups have been identified in genotype C and a clustering method has been developed to separate the subgroups. In the feature selection process, potential markers are selected based on Information Gain for further classifier learning. Then, meaningful rules are learned by our algorithm called the Rule Learning, which is based on Evolutionary Algorithm. Also, a new classification method by Nonlinear Integral has been developed. Good performance of this method comes from the use of the fuzzy measure and the relevant nonlinear integral. The nonadditivity of the fuzzy measure reflects the importance of the feature attributes as well as their interactions. These two classifiers give explicit information on the importance of the individual mutated sites and their interactions toward the classification (potential causes of liver cancer in our case). A thorough comparison study of these two methods with existing methods is detailed. For genotype B, genotype C subgroups C1, C2, and C3, important mutation markers (sites) have been found, respectively. These two classification methods have been applied to classify never-seen-before examples for validation. The results show that the classification methods have more than 70 percent accuracy and 80 percent sensitivity for most data sets, which are considered high as an initial scanning method for liver cancer diagnosis. © 2011 IEEE.enDNA SequencingHepatitis BData MiningClassification MethodsMutation SitesEvolutionary AlgorithmsCharacterization of PropertiesLearning ClassifiersInformation GainLearning RuleMolecular Evolutionary AnalysisHepatitis B Virus DNABioinformatics ResearchFuzzy MeasureHepatitis B Virus SequencesNeural NetworkPhylogenetic AnalysisTraining SetSupport Vector MachineDecision TreeHepatitis B Virus GenotypeChronic Hepatitis BReal AxisBayesian ClassifierNumber of SplitsSite SequenceN-Dimensional SpaceDimensional Feature SpaceHepatocellular Carcinoma PatientsNaïve Bayes ModelPeer Reviewed Journal Article10.1109/TCBB.2009.6