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    Multiclass disease predictions based on integrated clinical and genomics datasets

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    biotechno_2019_2_20_70033.pdf
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    Authors
    Anjum, Ashiq cc
    Subhani, Moeez cc
    Affiliation
    University of Derby
    Issue Date
    2019-06-02
    
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    Abstract
    Clinical predictions using clinical data by computational methods are common in bioinformatics. However, clinical predictions using information from genomics datasets as well is not a frequently observed phenomenon in research. Precision medicine research requires information from all available datasets to provide intelligent clinical solutions. In this paper, we have attempted to create a prediction model which uses information from both clinical and genomics datasets. We have demonstrated multiclass disease predictions based on combined clinical and genomics datasets using machine learning methods. We have created an integrated dataset, using a clinical (ClinVar) and a genomics (gene expression) dataset, and trained it using instancebased learner to predict clinical diseases. We have used an innovative but simple way for multiclass classification, where the number of output classes is as high as 75. We have used Principal Component Analysis for feature selection. The classifier predicted diseases with 73% accuracy on the integrated dataset. The results were consistent and competent when compared with other classification models. The results show that genomics information can be reliably included in datasets for clinical predictions and it can prove to be valuable in clinical diagnostics and precision medicine.
    Citation
    Subhani, M. and Anjum, A. (2019) 'Multiclass disease predictions based on integrated clinical and genomics datasets', The Eleventh International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies. Novotel, Athens, 2-6 June. IARA: Wilmington, pp. 20-27.
    Publisher
    IARIA
    URI
    http://hdl.handle.net/10545/624222
    Additional Links
    https://www.iaria.org/conferences2019/AwardsBIOTECHNO19.html
    https://www.thinkmind.org/index.php?view=article&articleid=biotechno_2019_2_20_70033
    Type
    Meetings and Proceedings
    Language
    en
    ISSN
    23084383
    ISBN
    9781612087177
    Collections
    Department of Electronics, Computing & Maths

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