NDLI: Machine Learning Model for Identifying Gene Biomarkers for Breast Cancer Treatment Survival

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Machine Learning Model for Identifying Gene Biomarkers for Breast Cancer Treatment Survival

Content Provider	ACM Digital Library
Author	ElMaraghy, Waguih Ngom, Alioune Abou Tabl, Ashraf Alkhateeb, Abed
Abstract	Studying the breast cancer survival genes information will help to enhance the treatment and save more patents life by identifying the genes biomarker to recommend the proper treatment type. That is why it is now a great challenge for researchers to have more research on breast cancer specially with the great enhancement in the fields of bioinformatics, data mining, and machine learning techniques which were a new revolution in the cancer treatment. A dataset contains the survival information and treatments methods for 1980 female breast cancer patient is used for building the prediction model, the gene expression are the features of the learning model [1], where the combination of the survival and treatments information are the classes. A hierarchal model that consists of hybrid feature selection and classification method are utilized to differentiate a class from the rest of the classes. The results show that a few number of gene biomarkers (gene signature) at each node which can determine the class with accuracy around 99% for survival living / deceased based on treatments which is vital to ensure that the patients will have the best potential response to a specific therapy. This signatures will be used as a predictor of survival in breast cancer.
Starting Page	607
Ending Page	607
Page Count	1
File Format	PDF
ISBN	9781450347228
DOI	10.1145/3107411.3108217
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Feature selection Treatment Classification Machine learning Breast cancer Survival Biomarker gens
Content Type	Text
Resource Type	Article

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