Common cancer biomarkers of breast and ovarian types identified through artificial intelligence

Pawar, Shrikant and Liew, Tuck Onn * and Stanam, A. and Lahiri, Chandrajit * (2020) Common cancer biomarkers of breast and ovarian types identified through artificial intelligence. Chemical Biology & Drug Design. ISSN 1747-0277

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Biomarkers can offer great promise for improving prevention and treatment of complex diseases such as cancer, cardiovascular diseases, and diabetes. These can be used as either diagnostic or predictive or as prognostic biomarkers. The revolution brought about in biological big data analytics by artificial intelligence (AI) has the potential to identify a broader range of genetic differences and support the generation of more robust biomarkers in medicine. AI is invigorating biomarker research on various fronts, right from the cataloguing of key mutations driving the complex diseases like cancer to the elucidation of molecular networks underlying diseases. In this study, we have explored the potential of AI through machine learning approaches to propose that these methods can act as recommendation systems to sort and prioritize important genes and finally predict the presence of specific biomarkers. Essentially, we have utilized microarray datasets from open‐source databases, like GEO, for breast, lung, colon, and ovarian cancer. In this context, different clustering analyses like hierarchical and k‐means along with random forest algorithm have been utilized to classify important genes from a pool of several thousand genes. To this end, network centrality and pathway analysis have been implemented to identify the most potential target as CREB1.

Item Type: Article
Subjects: Q Science > QH Natural history > QH301 Biology
Divisions: Others > Non Sunway Academics
Sunway University > School of Engineering and Technology [formerly School of Science and Technology until 2020] > Dept. Biological Sciences moved to SMLS wef 2021
Depositing User: Dr Janaki Sinnasamy
Related URLs:
Date Deposited: 30 Sep 2020 07:32
Last Modified: 30 Sep 2020 07:32

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