2020
DOI: 10.1111/cbdd.13672
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Common cancer biomarkers of breast and ovarian types identified through artificial intelligence

Abstract: 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… Show more

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Cited by 7 publications
(4 citation statements)
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“…k-means and agglomerative clustering follow different approaches, i.e., “top-down” and “bottom-up” approaches, respectively. Clustering has been widely used in the field of medicine and biomedical sciences for applications such as selecting new candidate drugs for lung cancer [ 14 ], molecular descriptor analysis [ 15 ] or clustering the gene profiles of distinct types of cancer [ 16 ].…”
Section: Methodsmentioning
confidence: 99%
“…k-means and agglomerative clustering follow different approaches, i.e., “top-down” and “bottom-up” approaches, respectively. Clustering has been widely used in the field of medicine and biomedical sciences for applications such as selecting new candidate drugs for lung cancer [ 14 ], molecular descriptor analysis [ 15 ] or clustering the gene profiles of distinct types of cancer [ 16 ].…”
Section: Methodsmentioning
confidence: 99%
“…Clustering-type discovery involves the grouping of patients based on biomarker profiles that can be analyzed using hierarchical clustering [ 90 ], K-means clustering [ 91 ], or Gaussian-mixture [ 92 ] algorithms. It is also common to combine multiple algorithms to improve biomarker quality [ 93 ] or to identify the most effective algorithm [ 88 ]. By applying AI techniques to high-throughput omics data, new biomarkers have been identified for a variety of diseases, including cancer [ 88 ], diabetes [ 94 ], and infectious diseases [ 95 ].…”
Section: Artificial Intelligence In Biosensingmentioning
confidence: 99%
“…The data may be described as "big" because of its volume, velocity (rate of accrual) or variety of formats (Figure 1). The development of this field provides a great opportunity in the multimorbidity domain to gain insights for patient benefit through the linkage and efficient analysis of large and complementary datasets (Burstein et al, 2019;Pawar et al, 2020). In this review, we consider the types of data that are commonly recorded, with some examples of their application in the multimorbidity setting.…”
Section: Introductionmentioning
confidence: 99%