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- W4360585066 abstract "Cancer was among the first three in the list of leading causes of death worldwide in the year 2020 according to World Health Organization (WHO). Though cancer is one of the major focus areas of research for scientists and researchers, cancer biomarkers detection, disease outcomes prediction, prognosis, from Gene Expression Data (GED), and other types of data like medical image data, is still a challenging task due to noise, complexity, and high dimensionality of data. Transcriptomic data has great potential to explore biomarkers for drug targets, risk assessment, prognosis and finding the cancer type and stage. The machine learning (ML) approaches are being used to identify hidden patterns in GED in different types of cancer. In this proposed paper, we studied related works done for cancer subtypes identification, building classifiers for biomarkers identifications, and feature selections through supervised learning techniques using transcriptomic data. Then we discussed it with the case studies. Limitations and challenges are also identified which are faced by researchers in the way of applying ML on GED." @default.
- W4360585066 created "2023-03-24" @default.
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- W4360585066 date "2022-12-14" @default.
- W4360585066 modified "2023-09-26" @default.
- W4360585066 title "Supervised Learning in Diagnosis, Prognosis, and Identification of Subtypes of Cancer" @default.
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- W4360585066 doi "https://doi.org/10.1109/ic3i56241.2022.10073444" @default.
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