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Gene mutation detection for breast cancer disease: A review
Wisesty U.N.a,b, Mengko T.R.a, Purwarianti A.a
a School of Electrical and Information Engineering, Bandung Institute of Technology, Bandung, Indonesia
b School of Computing, Telkom University, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© Published under licence by IOP Publishing Ltd.Breast cancer is one of the most common diseases suffered, especially by women, in the world, and about two billion new cases of patients with breast cancer in 2018. Therefore, it is very important to detect cancer early. Early detection of cancer can be done through the analysis of DNA abnormalities from blood cell samples, where the sampling does not require surgery, non-invasive and painless, and can reduce the sampling cost. DNA abnormalities can occur due to heredity or gene mutation. This paper presents a systematic review that includes an explanation of DNA sequences, gene mutations that occur in breast cancer, and bioinformatics techniques for detecting breast cancer. From several studies that have been conducted in the medical field there are mutations in the BRCA1, BRCA2, and PALB2 genes, where mutations in these genes can cause an increased risk of breast cancer. Other gene mutations associated with cancer risk are ATM, BARD1, CDH1, CHEK2, MRE11A, NBN, TP53, PTEN, RAD50, RECQL, RINT1. In bioinformatics, breast cancer detection based on DNA sequence data is carried out in three phases namely data mapping, feature extraction, and prediction / classification. The methods that can be used are Voss mapping and its variations for data mapping, statistical feature representation approach and Wavelet analysis for feature extraction, and regression approaches, probability models, Support Vector Machines, Neural Networks and Deep Learning for classification.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1088/1757-899X/830/3/032051[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]