Protein Family Classification with Multi-Layer Graph Convolutional Networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
EditorsHarald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2390-2393
Number of pages4
ISBN (Electronic)9781538654880
DOIs
StatePublished - Jan 21 2019
Event2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain
Duration: Dec 3 2018Dec 6 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
CountrySpain
CityMadrid
Period12/3/1812/6/18

Fingerprint

Proteins
Protein Interaction Maps
Computational Biology
Bioinformatics
Network architecture
Cluster Analysis
Amino acids
Topology
Amino Acids
Research

Keywords

  • Graph Convolutional Networks
  • Protein Family Classification
  • Protein-Protein Interaction Network

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

Cite this

Zhang, D., & Kabuka, M. R. (2019). Protein Family Classification with Multi-Layer Graph Convolutional Networks. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, ... L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (pp. 2390-2393). [8621520] (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621520

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 proceedingConference contribution

Zhang, D & Kabuka, MR 2019, Protein Family Classification with Multi-Layer Graph Convolutional Networks. in H Schmidt, D Griol, H Wang, J Baumbach, H Zheng, Z Callejas, X Hu, J Dickerson & L Zhang (eds), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018., 8621520, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Institute of Electrical and Electronics Engineers Inc., pp. 2390-2393, 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Spain, 12/3/18. https://doi.org/10.1109/BIBM.2018.8621520
Zhang D, Kabuka MR. Protein Family Classification with Multi-Layer Graph Convolutional Networks. In Schmidt H, Griol D, Wang H, Baumbach J, Zheng H, Callejas Z, Hu X, Dickerson J, Zhang L, editors, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 2390-2393. 8621520. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). https://doi.org/10.1109/BIBM.2018.8621520
Zhang, Da ; Kabuka, Mansur R. / Protein Family Classification with Multi-Layer Graph Convolutional Networks. Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. editor / 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. pp. 2390-2393 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).
@inproceedings{ca8429e3b01e40c19c2ad25aa5bfc1c2,
title = "Protein Family Classification with Multi-Layer Graph Convolutional Networks",
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.",
keywords = "Graph Convolutional Networks, Protein Family Classification, Protein-Protein Interaction Network",
author = "Da Zhang and Kabuka, {Mansur R.}",
year = "2019",
month = "1",
day = "21",
doi = "10.1109/BIBM.2018.8621520",
language = "English (US)",
series = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2390--2393",
editor = "Harald Schmidt and David Griol and Haiying Wang and Jan Baumbach and Huiru Zheng and Zoraida Callejas and Xiaohua Hu and Julie Dickerson and Le Zhang",
booktitle = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",

}

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 -