• Login
    View Item 
    •   Shocker Open Access Repository Home
    • Graduate Student Research
    • ETD: Electronic Theses and Dissertations
    • Dissertations
    • View Item
    •   Shocker Open Access Repository Home
    • Graduate Student Research
    • ETD: Electronic Theses and Dissertations
    • Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Application of machine learning models and feature engineering to predict genomic phenomena

    View/Open
    diss. embargoed till 2023-12-31 (6.618Mb)
    Date
    2022-05
    Author
    Okwori, Michael
    Advisor
    Eslami, Ali
    Metadata
    Show full item record
    Abstract
    This dissertation investigates two topics towards designing tiny machine learningbased intelligent devices for biomedical applications. In the first section, feature engineering and machine learning models are developed to predict two genomic phenomena: gene mutation and differential gene expression. A hypothesis that features encoding interactions between genes will improve gene mutation prediction performance is proposed. To test this, additional training features are engineered from protein-coding gene cofunctional networks and combined with a mutation dataset of E. coli exposed to different conditions. Also, a feature-selection algorithm based on gene cofunctional networks is presented. Then, a support vector classifier, an artificial neural network, and an ensemble of both models are trained to predict gene mutation using the extended dataset. A sequential mutation modeling approach to predicting gene mutation is also presented. In addition, the prediction of differentially expressed genes (DEGs) when exposed to conditions in space from a set of diverse engineered features is investigated. DEGs and non-differentially expressed genes (NDEGs) of house mouse (Mus musculus)-based experiments are collected and a unique feature engineering procedure is proposed to generate key training features for machine learning models. The results show that the proposed feature engineering procedure generates features that boost the gene mutation prediction performance by a maximum of 8.74% in the receiver operating characteristics curve (AUC). Additionally, the generated features in the prediction of DEGs achieve a maximum and minimum AUC of 0.97 and 0.74, respectively. In the second work, magnetic induction-based communication and powering are demonstrated via simulation for a microscale mote. Then, low-power modulation, error-correction coding, and suitable low-power media access control (MAC) schemes with evidence of feasible implementation in microscale are explored. Results of the performance analysis indicate that the proposed design achieves communication at a range of at least a few centimeters (5 - 6 cm) with an acceptable bit error rate (BER). Finally, MAC layer analysis reveals the optimum number of motes to be deployed for various read delays and transmission rates.
    Description
    Thesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of Electrical and Computer Engineering
    URI
    https://soar.wichita.edu/handle/10057/23438
    Collections
    • CE Theses and Dissertations
    • Dissertations
    • EECS Theses and Dissertations

    Browse

    All of Shocker Open Access RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsBy TypeThis CollectionBy Issue DateAuthorsTitlesSubjectsBy Type

    My Account

    LoginRegister

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    DSpace software copyright © 2002-2023  DuraSpace
    DSpace Express is a service operated by 
    Atmire NV