A Functional Network of Yeast Genes Using Gene Ontology Information

2008 IEEE International Conference on Bioinformatics and Biomedicine ◽

10.1109/bibm.2008.60 ◽

2008 ◽

Author(s):

Erliang Zeng ◽

Giri Narasimhan ◽

Lisa Schneper ◽

Kalai Mathee

Keyword(s):

Gene Ontology ◽

Functional Network ◽

Yeast Genes ◽

Gene Ontology Information

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Functional Network Analysis with the Subcellular Location and Gene Ontology Information in Human Allergic Asthma

Genetic Testing and Molecular Biomarkers ◽

10.1089/gtmb.2011.0325 ◽

2012 ◽

Vol 16 (11) ◽

pp. 1287-1292 ◽

Cited By ~ 1

Author(s):

Bo Diao ◽

Ying Liu ◽

Yi Zhang ◽

Qin Liu ◽

Wen-Jie Lu ◽

...

Keyword(s):

Gene Ontology ◽

Network Analysis ◽

Allergic Asthma ◽

Subcellular Location ◽

Functional Network ◽

Gene Ontology Information

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Assigning gene ontology categories (GO) to yeast genes using text-based supervised learning methods

Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004. ◽

10.1109/csb.2004.1332476 ◽

2004 ◽

Cited By ~ 2

Author(s):

T. Izumitani ◽

H. Taira ◽

H. Kazawa ◽

E. Maeda

Keyword(s):

Gene Ontology ◽

Supervised Learning ◽

Learning Methods ◽

Yeast Genes

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Gene Function Prediction and Functional Network: The Role of Gene Ontology

Intelligent Systems Reference Library - Data Mining: Foundations and Intelligent Paradigms ◽

10.1007/978-3-642-23151-3_7 ◽

2012 ◽

pp. 123-162 ◽

Cited By ~ 1

Author(s):

Erliang Zeng ◽

Chris Ding ◽

Kalai Mathee ◽

Lisa Schneper ◽

Giri Narasimhan

Keyword(s):

Gene Ontology ◽

Gene Function ◽

Function Prediction ◽

Functional Network ◽

Gene Function Prediction

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Incorporating Gene Ontology Information in Gene Expression Data Clustering Using Multiobjective Evolutionary Optimization: Application in Yeast Cell Cycle Data

Multi-Objective Optimization ◽

10.1007/978-981-13-1471-1_3 ◽

2018 ◽

pp. 55-78

Author(s):

Anirban Mukhopadhyay

Keyword(s):

Gene Expression ◽

Cell Cycle ◽

Gene Ontology ◽

Yeast Cell ◽

Gene Expression Data ◽

Data Clustering ◽

Yeast Cell Cycle ◽

Expression Data ◽

Gene Expression Data Clustering ◽

Gene Ontology Information

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Detecting Essential Proteins Based on Network Topology, Gene Expression Data, and Gene Ontology Information

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2016.2615931 ◽

2018 ◽

Vol 15 (1) ◽

pp. 109-116 ◽

Cited By ~ 14

Author(s):

Wei Zhang ◽

Jia Xu ◽

Yuanyuan Li ◽

Xiufen Zou

Keyword(s):

Gene Expression ◽

Gene Ontology ◽

Gene Expression Data ◽

Network Topology ◽

Expression Data ◽

Essential Proteins ◽

Gene Ontology Information

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PREDICTION OF ESSENTIAL PROTEINS BASED ON EDGE CLUSTERING COEFFICIENT AND GENE ONTOLOGY INFORMATION

Journal of Biological System ◽

10.1142/s0218339014500119 ◽

2014 ◽

Vol 22 (03) ◽

pp. 339-351 ◽

Cited By ~ 3

Author(s):

JIAWEI LUO ◽

NAN ZHANG

Keyword(s):

Gene Ontology ◽

Network Topology ◽

Clustering Coefficient ◽

Ppi Network ◽

Essential Proteins ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Survival And Development ◽

Gene Ontology Information ◽

Better Than

Essential proteins are important for the survival and development of organisms. Lots of centrality algorithms based on network topology have been proposed to detect essential proteins and achieve good results. However, most of them only focus on the network topology, but ignore the false positive (FP) interactions in protein–protein interaction (PPI) network. In this paper, gene ontology (GO) information is proposed to measure the reliability of the edges in PPI network and we propose a novel algorithm for identifying essential proteins, named EGC algorithm. EGC algorithm integrates topology character of PPI network and GO information. To validate the performance of EGC algorithm, we use EGC and other nine methods (DC, BC, CC, SC, EC, LAC, NC, PEC and CoEWC) to identify the essential proteins in the two different yeast PPI networks: DIP and MIPS. The results show that EGC is better than the other nine methods, which means adding GO information can help in predicting essential proteins.

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