TY - JOUR
T1 - In Silico Analysis Using Hybrid Support Vector Machine and Second Order of Markov Chain for Multiple Sequence Alignment to Identify the Types of Leukaemia
AU - Irawan, Mohammad Isa
AU - Al Faroby, Mohammad Hamim Zajuli
AU - Dyah Nurhayati, Awik Pudji
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2019/11/7
Y1 - 2019/11/7
N2 - Cancer is a disease that causes an abnormal growth of cells and can attack every part of the body, which is occurred because of a damage in deoxyribonucleic acid (DNA) that leads to a mutation in a vital gene that controls cell division. The biomarker technology that used in clinical practice still used a high cost and need a long time to detect the cancer signs. As the former studies about cancer, the biomarker has been detected in the microarray data. In this paper, we used a support vector machine (SVM) to classify 4 type of leukaemia. Begin with extracting the data feature of sequence DNA from a string into numeric using Second order of Markov chain, SVM classified DNA using 40 data for the training step and 25 data for testing step. In this paper, SVM used 3 types of the kernel, which are linear, Gaussian radial basis function, and polynomial. The results showed that the Gaussian kernel has the best accuracy then other kernel.
AB - Cancer is a disease that causes an abnormal growth of cells and can attack every part of the body, which is occurred because of a damage in deoxyribonucleic acid (DNA) that leads to a mutation in a vital gene that controls cell division. The biomarker technology that used in clinical practice still used a high cost and need a long time to detect the cancer signs. As the former studies about cancer, the biomarker has been detected in the microarray data. In this paper, we used a support vector machine (SVM) to classify 4 type of leukaemia. Begin with extracting the data feature of sequence DNA from a string into numeric using Second order of Markov chain, SVM classified DNA using 40 data for the training step and 25 data for testing step. In this paper, SVM used 3 types of the kernel, which are linear, Gaussian radial basis function, and polynomial. The results showed that the Gaussian kernel has the best accuracy then other kernel.
UR - http://www.scopus.com/inward/record.url?scp=85076117414&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1366/1/012052
DO - 10.1088/1742-6596/1366/1/012052
M3 - Conference article
AN - SCOPUS:85076117414
SN - 1742-6588
VL - 1366
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012052
T2 - 2nd International Conference on Applied and Industrial Mathematics and Statistics 2019, ICoAIMS 2019
Y2 - 23 July 2019 through 25 July 2019
ER -