ArticleBMC genomics2013
Prediction of essential proteins based on gene expression programming.
Article in BMC genomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Differentially used codons among essential genes in bacteria identified by machine learning-based analysis.Molecular genetics and genomics : MGG · 2024Article
- Essential genes identification model based on sequence feature map and graph convolutional neural network.BMC genomics · 2024Article
- Article
- SGII: Systematic Identification of Essential lncRNAs in Mouse and Human Genome With lncRNA-Protein-Protein Heterogeneous Interaction Network.Frontiers in genetics · 2022Article
- A novel essential protein identification method based on PPI networks and gene expression data.BMC bioinformatics · 2021Article
- Method for Essential Protein Prediction Based on a Novel Weighted Protein-Domain Interaction Network.Frontiers in genetics · 2021Article
- Identifying essential proteins in dynamic protein networks based on an improved h-index algorithm.BMC medical informatics and decision making · 2020Article
- Prediction of essential genes in prokaryote based on artificial neural network.Genes & genomics · 2020Article
- DeepEP: a deep learning framework for identifying essential proteins.BMC bioinformatics · 2019Article
- Identifying mouse developmental essential genes using machine learning.Disease models & mechanisms · 2018Article
- Properties of genes essential for mouse development.PloS one · 2017Article
- A new computational strategy for identifying essential proteins based on network topological properties and biological information.PloS one · 2017Article
- Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways.PloS one · 2017Article
- Lung cancer prediction from microarray data by gene expression programming.IET systems biology · 2016Article
- Evolution of Centrality Measurements for the Detection of Essential Proteins in Biological Networks.Frontiers in physiology · 2016Article
- Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review.Frontiers in physiology · 2016Review
- Rechecking the Centrality-Lethality Rule in the Scope of Protein Subcellular Localization Interaction Networks.PloS one · 2015Article
- Identification of Essential Proteins Based on a New Combination of Local Interaction Density and Protein Complexes.PloS one · 2015Article
- Protein localization analysis of essential genes in prokaryotes.Scientific reports · 2014Article
- Prediction of disease genes using tissue-specified gene-gene network.BMC systems biology · 2014Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEssential 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.
resultsIn 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.
conclusionsThe accuracy of predicting essential proteins can been improved by using GEP method to combine some topological features and biological features.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.