title: Knowledge discovery for pancreatic cancer using inductive logic programming.
abstract: Pancreatic cancer is a devastating disease and predicting the status of the patients becomes an important and urgent issue. The authors explore the applicability of inductive logic programming (ILP) method in the disease and show that the accumulated clinical laboratory data can be used to predict disease characteristics, and this will contribute to the selection of therapeutic modalities of pancreatic cancer. The availability of a large amount of clinical laboratory data provides clues to aid in the knowledge discovery of diseases. In predicting the differentiation of tumour and the status of lymph node metastasis in pancreatic cancer, using the ILP model, three rules are developed that are consistent with descriptions in the literature. The rules that are identified are useful to detect the differentiation of tumour and the status of lymph node metastasis in pancreatic cancer and therefore contributed significantly to the decision of therapeutic strategies. In addition, the proposed method is compared with the other typical classification techniques and the results further confirm the superiority and merit of the proposed method.
authors: Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong, People's Republic of China. yushanqiu2526374@163.com.
authors: Yushan
authors: Y
authors: Qiu
authors: Hepatobiliary and Pancreatic Surgery Division, National Cancer Center Hospital, Tokyo, Japan.
authors: Kazuaki
authors: K
authors: Shimada
authors: Pathology Division, National Cancer Center Research Institute, Tokyo, Japan.
authors: Nobuyoshi
authors: N
authors: Hiraoka
authors: Department of Surgery, Fukuoka University, Fukuoka, Japan.
authors: Kensei
authors: K
authors: Maeshiro
authors: Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong, People's Republic of China.
authors: Wai-Ki
authors: WK
authors: Ching
authors: Department of Bioinformatics, Faculty of Engineering, Soka University, Tokyo, Japan.
authors: Kiyoko F
authors: KF
authors: Aoki-Kinoshita
authors: Division of Clinical Laboratories, National Cancer Center Hospital, Tokyo, Japan.
authors: Koh
authors: K
authors: Furuta
mesh_terms: D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000097:blood*; D003198:Computer Simulation; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D016015:Logistic Models; D008207:Lymphatic Metastasis; D010190:Pancreatic Neoplasms / Q000097:blood / Q000175:diagnosis*; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor