Abstract
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.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 |
| Editors | Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 2390-2393 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781538654880 |
| DOIs | |
| State | Published - Jan 21 2019 |
| Event | 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain Duration: Dec 3 2018 → Dec 6 2018 |
Publication series
| Name | Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 |
|---|
Conference
| Conference | 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 |
|---|---|
| Country | Spain |
| City | Madrid |
| Period | 12/3/18 → 12/6/18 |
Fingerprint
Keywords
- Graph Convolutional Networks
- Protein Family Classification
- Protein-Protein Interaction Network
ASJC Scopus subject areas
- Biomedical Engineering
- Health Informatics
Cite this
Protein Family Classification with Multi-Layer Graph Convolutional Networks. / Zhang, Da; Kabuka, Mansur R.
Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. ed. / Harald Schmidt; David Griol; Haiying Wang; Jan Baumbach; Huiru Zheng; Zoraida Callejas; Xiaohua Hu; Julie Dickerson; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2390-2393 8621520 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Protein Family Classification with Multi-Layer Graph Convolutional Networks
AU - Zhang, Da
AU - Kabuka, Mansur R.
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
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.
ER -