TY - GEN
T1 - Identifying Candidate Diseases-related Metabolites Based on Disease Similarity
AU - Wang, Yongtian
AU - Juan, Liran
AU - Liu, Chunpu
AU - Zang, Tianyi
AU - Wang, Yadong
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/21
Y1 - 2019/1/21
N2 - Functional related metabolites have a close relationship with complex diseases, and are often associated with the same or similar diseases. Therefore, identification of disease related metabolites is significant for understanding comprehensively pathogenesis of disease, aiming at improving the clinical medicine. Considering that a large number of metabolic markers of diseases need to be explored, we propose a method to identify potential disease-related metabolites based on metabolite functional similarity network. Firstly, we calculate the similarity of metabolites based on modified recommendation strategy of Collaborative Filtering utilizing the similarities between diseases. Next, a disease associated metabolite network is built with similarities between metabolites as weight. Finally, we utilize random walking with restart in this network to find more unknown metabolic markers related to one disease. This method offers researchers a new way to identify potential disease-related metabolic markers.
AB - Functional related metabolites have a close relationship with complex diseases, and are often associated with the same or similar diseases. Therefore, identification of disease related metabolites is significant for understanding comprehensively pathogenesis of disease, aiming at improving the clinical medicine. Considering that a large number of metabolic markers of diseases need to be explored, we propose a method to identify potential disease-related metabolites based on metabolite functional similarity network. Firstly, we calculate the similarity of metabolites based on modified recommendation strategy of Collaborative Filtering utilizing the similarities between diseases. Next, a disease associated metabolite network is built with similarities between metabolites as weight. Finally, we utilize random walking with restart in this network to find more unknown metabolic markers related to one disease. This method offers researchers a new way to identify potential disease-related metabolic markers.
KW - Collaborative Filtering
KW - metabolite Nenvork
KW - random Walking with Restart
KW - similarity of Metabolites
UR - https://www.scopus.com/pages/publications/85062574159
U2 - 10.1109/BIBM.2018.8621318
DO - 10.1109/BIBM.2018.8621318
M3 - 会议稿件
AN - SCOPUS:85062574159
T3 - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
SP - 1281
EP - 1285
BT - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
A2 - Schmidt, Harald
A2 - Griol, David
A2 - Wang, Haiying
A2 - Baumbach, Jan
A2 - Zheng, Huiru
A2 - Callejas, Zoraida
A2 - Hu, Xiaohua
A2 - Dickerson, Julie
A2 - Zhang, Le
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Y2 - 3 December 2018 through 6 December 2018
ER -