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    A centrality based multi-objective approach to disease gene association

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    Author
    Collins, Tyler K.
    Houghten, Sheridan
    Keyword
    General Biochemistry, Genetics and Molecular Biology
    Modeling and Simulation
    General Medicine
    Statistics and Probability
    Disease Gene Association
    Genetic Algorithm
    Journal title
    Biosystems
    Publication Volume
    193-194
    Publication Begin page
    104133
    
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    URI
    http://hdl.handle.net/10464/16883
    Abstract
    Disease Gene Association nds genes that are involved in the presentation of a given genetic disease. We present a hybrid approach which implements a multi-objective genetic algorithm, where input consists of centrality measures based on various relational biological evidence types merged into a complex network. Multiple objective settings and parameters are studied including the development of a new exchange methodology, safe dealer-based crossover. Successful results with respect to breast cancer and Parkinson's disease compared to previous techniques and popular known databases are shown. In addition, the newly developed methodology is also successfully applied to Alzheimer's disease, further demonstrating its flexibility. Across all three case studies the strongest results were produced by the shortest path-based measures stress and betweenness, either in a single objective parameter setting or when used in conjunction in a multi-objective environment. The new crossover technique achieved the best results when applied to Alzheimer's disease.
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.biosystems.2020.104133
    Scopus Count
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    Computer Science

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