TY - GEN
T1 - Deep belief networks and Bayesian networks for prognosis of acute lymphoblastic leukemia
AU - Ghaisani, Fakhirah D.
AU - Mufidah, Ratna
AU - Wasito, Ito
AU - Faturrahman, Moh
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/8/10
Y1 - 2017/8/10
N2 - Cancer is one of main non-communicable diseases. Acute Lymphoblastic Leukemia (ALL), a type of white blood cancer, is one of the most common pediatric cancers. Analysis of cancer prognosis is necessary to determine the proper treatment for each patient. However, cancer data analysis is challenging because multiple risk factors may influence the prognosis of cancer, including gene and clinical condition of patient. This study aims to develop prediction model for cancer prognosis using clinical and gene expression (microarray) data. In this research, manifold learning is applied to microarray data to reduce its dimension, then two Deep Belief Network (DBN) models for both clinical and microarray data are trained separately. Probabilities obtained from Clinical DBN model and Microarray DBN model are integrated using softmax nodes on Bayesian Network structure. Based on various experiments, the best integration model obtained is DBN+BN 32 with prediction accuracy 84.2% for 2-years survival, 70.2% for 3-years, 68.4% for 4-years, and 73.7% for 5-years. This prediction model can be used in cancer analysis and help doctor to decide proper treatment for patient.
AB - Cancer is one of main non-communicable diseases. Acute Lymphoblastic Leukemia (ALL), a type of white blood cancer, is one of the most common pediatric cancers. Analysis of cancer prognosis is necessary to determine the proper treatment for each patient. However, cancer data analysis is challenging because multiple risk factors may influence the prognosis of cancer, including gene and clinical condition of patient. This study aims to develop prediction model for cancer prognosis using clinical and gene expression (microarray) data. In this research, manifold learning is applied to microarray data to reduce its dimension, then two Deep Belief Network (DBN) models for both clinical and microarray data are trained separately. Probabilities obtained from Clinical DBN model and Microarray DBN model are integrated using softmax nodes on Bayesian Network structure. Based on various experiments, the best integration model obtained is DBN+BN 32 with prediction accuracy 84.2% for 2-years survival, 70.2% for 3-years, 68.4% for 4-years, and 73.7% for 5-years. This prediction model can be used in cancer analysis and help doctor to decide proper treatment for patient.
KW - Acute lymphoblastic leukemia
KW - Bayesian network
KW - Cancer
KW - Data integration
KW - Deep belief network
KW - Dimensionality reduction
KW - Leukemia
KW - Manifold learning
KW - Microarray.
UR - http://www.scopus.com/inward/record.url?scp=85039054705&partnerID=8YFLogxK
U2 - 10.1145/3127942.3127947
DO - 10.1145/3127942.3127947
M3 - Conference contribution
AN - SCOPUS:85039054705
T3 - ACM International Conference Proceeding Series
SP - 102
EP - 106
BT - Proceedings of 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017
PB - Association for Computing Machinery
T2 - 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017
Y2 - 10 August 2017 through 13 August 2017
ER -
