- author = "Jiancheng Zhong and Jianxin Wang and Wei Peng and Zhen Zhang and Yi Pan",
- title = "Prediction of essential proteins based on gene expression programming",
- journal = "BMC Genomics",
- year = "2013",
- volume = "14",
- number = "(Suppl 4)",
- pages = "S7",
- month = oct # "~01",
- note = "Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2012: Genomics",
- keywords = "genetic algorithms, genetic programming, gene expression programming",
- ISSN = "1471-2164",
- publisher = "BioMed Central Ltd.",
- bibsource = "OAI-PMH server at www.biomedcentral.com",
- language = "en",
- oai = "oai:biomedcentral.com:1471-2164-14-S4-S7",
- type = "Research",
-
URL = "
http://www.biomedcentral.com/1471-2164/14/S4/S7",
-
abstract = "Background Essential proteins are indispensable for
cell survive. Identifying essential proteins is very
important for improving our understanding the way of a
cell working. There are various types of features
related to the essentiality of proteins. Many methods
have been proposed to combine some of them to predict
essential proteins. However, it is still a big
challenge for designing an effective method to predict
them by integrating different features, and explaining
how these selected features decide the essentiality of
protein. Gene expression programming (GEP) is a
learning algorithm and what it learns specifically is
about relationships between variables in sets of data
and then builds models to explain these relationships.
Results In this work, we propose a GEP-based method to
predict essential protein by combing some biological
features and topological features. We carry out
experiments on S. cerevisiae data. The experimental
results show that the our method achieves better
prediction performance than those methods using
individual features. Moreover, our method outperforms
some machine learning methods and performs as well as a
method which is obtained by combining the outputs of
eight machine learning methods. Conclusions
The accuracy of predicting essential proteins can been improved by using GEP method to combine some topological features and biological features.",
