IM_SELATCN: DEEP LEARNING BASED SEQUENTIAL LABELING MODEL FOR TYPE AND INDEX MUTATION DETECTION IN BREAST AND LUNG CANCER DNA SEQUENCES - Novia Wisesty, Untari
Early detection of cancer is indispensable, as the number of new cases and deaths caused by cancer increases every year. One of the factors that increases the death rate due to cancer is the lateness of the patient's self-examination, causing delayed diagnosis. The delay causes the cancer to be in a higher stage, making the treatment less effective. Early detection of cancer can be achieved by carrying out a DNA test from a patient's blood sample. Meanwhile, a solid tumor biopsy is relatively difficult to perform if the cancer does not form a tumor or if the location of the organ infected with cancer is difficult to reach. Cancer is characterized by abnormalities in DNA that can be caused by hereditary or cancer-related gene mutations. Mutations that occur can take the form of point mutations, insertions, and deletions. Each type of cancer causes mutations in certain genes.. In the field of bioinformatics, two approaches are generally used to detect mutations, namely the alignment and machine learning approaches. Each approach has its strengths and weaknesses. The alignment approach is superior in detection accuracy, but it has a long test time because, to detect mutations from a new sequence, the sequence must be compared with all available reference sequences. On the other hand, the machine learning approach has a faster test time because the new sequences to be tested are entered into the optimal detection model to obtain the results without comparing them with reference sequences. However, in the studies conducted, the machine learning approach has only classified mutational or normal labels of a sequence and requires tools and other supporting data. Therefore, the proposed dissertation research aims to build a sequential labeling model based on Deep Learning (IM_SelaTCN) to detect the type and index mutations in DNA sequence data. The data used includes the COSMIC dataset for breast and lung cancer, which was acquired from the Catalog of Somatic Mutations in Cancer (COSMIC) database, as well as the RSCM dataset acquired from breast