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Journal article

ALZUBAIDI, A., TEPPER, J. and LOTFI, A., 2020. Deep mining for determining cancer biomarkers. HealthManagement.org - The Journal, 20 (6), pp. 462-464. ISSN 1377-7629

ALZUBAIDI, A., TEPPER, J. and LOTFI, A., 2020. A novel deep mining model for effective knowledge discovery from omics data. Artificial Intelligence in Medicine, 104: 101821. ISSN 0933-3657

Chapter in book

ALZUBAIDI, A. and COSMA, G., 2017. A multivariate feature selection framework for high dimensional biomedical data classification. In: Proceedings of the 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2017), Manchester, United Kingdom, 23-25 August 2017. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), pp. 59-66. ISBN 9781467389891

ALZUBAIDI, A., COSMA, G., BROWN, D. and POCKLEY, A.G., 2016. Breast cancer diagnosis using a hybrid genetic algorithm for feature selection based on mutual information. In: Proceedings: iTAG 2016: the 2016 International Conference on Interactive Technologies and Games - EduRob in Conjuction with ITAG2016 - 26-27 October 2016, Nottingham, United Kingdom. Washington, DC: IEEE Computer Society Conference Publishing Services, pp. 70-76. ISBN 9781509037384

ALZUBAIDI, A., COSMA, G., BROWN, D. and POCKLEY, A.G., 2016. A new hybrid global optimization approach for selecting clinical and biological features that are relevant to the effective diagnosis of ovarian cancer. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI): Proceedings. Piscataway, NJ: Institute of Electrical and Electronic Engineers. ISBN 9781509042401

Thesis

ALZUBAIDI, A.H.A., 2019. Evolutionary and deep mining models for effective biomarker discovery. PhD, Nottingham Trent University.