TY - GEN
T1 - Protein Family Classification with Multi-Layer Graph Convolutional Networks
AU - Zhang, Da
AU - Kabuka, Mansur R.
N1 - Funding Information:
Acknowledgements. This work is partly supported by grant R44CA206782 from the National Cancer Institute, part of the U.S. National Institutes of Health (NIH).
Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/21
Y1 - 2019/1/21
N2 - Next-generation sequencing techniques provide us with an opportunity for generating sequenced proteins and identifying the biological families and functions of the proteins. However, compared with identified proteins, uncharacterized proteins consist of a notable percentage of the predicted proteins in bioinformatics research field. However, previous clustering based algorithms heavily relying on large data samples are not accurate enough to assign protein families given a small amount of family annotated proteins. Therefore, considering limited protein data with annotated protein families, a more accurate and faster protein family prediction method is required. In this paper, we apply the Multi-layer Graph Convolutional Networks (GCN) architecture on a Protein-Protein Interaction (PPI) network with limited characterized proteins to explore the performance of protein family classification by taking into account both of the network topology and physicochemical protein amino acid features.
AB - Next-generation sequencing techniques provide us with an opportunity for generating sequenced proteins and identifying the biological families and functions of the proteins. However, compared with identified proteins, uncharacterized proteins consist of a notable percentage of the predicted proteins in bioinformatics research field. However, previous clustering based algorithms heavily relying on large data samples are not accurate enough to assign protein families given a small amount of family annotated proteins. Therefore, considering limited protein data with annotated protein families, a more accurate and faster protein family prediction method is required. In this paper, we apply the Multi-layer Graph Convolutional Networks (GCN) architecture on a Protein-Protein Interaction (PPI) network with limited characterized proteins to explore the performance of protein family classification by taking into account both of the network topology and physicochemical protein amino acid features.
KW - Graph Convolutional Networks
KW - Protein Family Classification
KW - Protein-Protein Interaction Network
UR - http://www.scopus.com/inward/record.url?scp=85062561171&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062561171&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2018.8621520
DO - 10.1109/BIBM.2018.8621520
M3 - Conference contribution
AN - SCOPUS:85062561171
T3 - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
SP - 2390
EP - 2393
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 -