Breast cancer subtype identification using machine learning techniques

2014 ◽  
Author(s):  
Forough Firoozbakht ◽  
Iman Rezaeian ◽  
Lisa Porter ◽  
Luis Rueda
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Breast Cancer Subtype ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Cancer Subtype ◽  
Subtype Identification

scholarly journals Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey

Life ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 638
Author(s):  
Linjing Liu ◽  
Xingjian Chen ◽  
Olutomilayo Olayemi Petinrin ◽  
Weitong Zhang ◽  
Saifur Rahaman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cancer Detection ◽  
Liquid Biopsy ◽  
Cancer Diagnostics ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Cancer Subtype ◽  
Tumor Origin ◽  
Complex Relationships ◽  
Learning Protocols

With the advances of liquid biopsy technology, there is increasing evidence that body fluid such as blood, urine, and saliva could harbor the potential biomarkers associated with tumor origin. Traditional correlation analysis methods are no longer sufficient to capture the high-resolution complex relationships between biomarkers and cancer subtype heterogeneity. To address the challenge, researchers proposed machine learning techniques with liquid biopsy data to explore the essence of tumor origin together. In this survey, we review the machine learning protocols and provide corresponding code demos for the approaches mentioned. We discuss algorithmic principles and frameworks extensively developed to reveal cancer mechanisms and consider the future prospects in biomarker exploration and cancer diagnostics.

Survey of Breast Cancer Detection Using Machine Learning Techniques in Big Data

2019 ◽  
Vol 21 (3) ◽  
pp. 80-92
Author(s):  
Madhuri Gupta ◽  
Bharat Gupta
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Research Work ◽  
Cancer Recurrence ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Common Disease ◽  
Learning Techniques ◽  
Learning Frameworks ◽  
Processing Engine

Cancer is a disease in which cells in body grow and divide beyond the control. Breast cancer is the second most common disease after lung cancer in women. Incredible advances in health sciences and biotechnology have prompted a huge amount of gene expression and clinical data. Machine learning techniques are improving the prior detection of breast cancer from this data. The research work carried out focuses on the application of machine learning methods, data analytic techniques, tools, and frameworks in the field of breast cancer research with respect to cancer survivability, cancer recurrence, cancer prediction and detection. Some of the widely used machine learning techniques used for detection of breast cancer are support vector machine and artificial neural network. Apache Spark data processing engine is found to be compatible with most of the machine learning frameworks.

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