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dc.contributor.authorLiu, Xiyuan
dc.description.abstractThe classification problem of gene expression level, more specifically, gene expression analysis, is a major research area in statistics. There are several classical methods to solve the classification problem. To apply Logistic Regression Model (LRM) and other classical methods, the observations in the dataset should fit the assumption of independence. That is, the observations in the dataset are independent to each other, and the predictor (independent variable) should be independent. These assumptions are usually violated in gene expression analysis. Although the Classical Hidden Markov Chain Model (HMM) can solve the independence of observation problem, the classical HMM requires the independent variables in the dataset are discrete and independent. Unfortunately, the gene expression level is a continuous variable. To solve the classification problem of Gene Expression Level data, the Conditional Random Field(CRF) is introduce. Finally, the Least Absolute Selection and Shrinkage Operator (LASSO) penalty, a dimensional reduction method, is introduced to improve the CRF model.en_US
dc.publisherNorth Dakota State Universityen_US
dc.titleConditional Random Field with Lasso and its Application to the Classification of Barley Genes Based on Expression Level Affected by Fungal Infectionen_US
dc.typeDissertationen_US
dc.typeVideoen_US
dc.date.accessioned2019-04-05T19:23:02Z
dc.date.available2019-04-05T19:23:02Z
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/10365/29518
dc.title.alternativeConditional Random Fields with Lasso and its Application to the Classification of Relationships between Plant Genes Expression Level and Fungus Genesen_US
dc.identifier.orcid0000-0002-0456-7931
ndsu.degreeDoctor of Philosophy (PhD)en_US
ndsu.collegeScience and Mathematicsen_US
ndsu.departmentStatisticsen_US
ndsu.programStatisticsen_US
ndsu.advisorShen, Gang


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