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- Publisher Website: 10.1007/s00500-005-0494-8
- Scopus: eid_2-s2.0-29544446923
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Article: Gene expression time series modeling with principal component and neural network
Title | Gene expression time series modeling with principal component and neural network |
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Authors | |
Keywords | Gene expression Nonlinear network inference Neural network Time series Principal component analysis |
Issue Date | 2006 |
Citation | Soft Computing, 2006, v. 10, n. 4, p. 351-358 How to Cite? |
Abstract | In this work, gene expression time series models have been constructed by using principal component analysis (PCA) and neural network (NN). The main contribution of this paper is to develop a methodology for modeling numerical gene expression time series. The PCA-NN prediction models are compared with other popular continuous prediction methods. The proposed model can give us the extracted features from the gene expressions time series and the orders of the prediction accuracies. Therefore, the model can help practitioners to gain a better understanding of a cell cycle, and to find the dependency of genes, which is useful for drug discoveries. Based on the results of two public real datasets, the PCA-NN method outperforms the other continuous prediction methods. In the time series model, we adapt Akaike's information criteria (AIC) tests and cross-validation to select a suitable NN model to avoid the overparameterized problem. © Springer-Verlag Berlin Heidelberg 2005. |
Persistent Identifier | http://hdl.handle.net/10722/276785 |
ISSN | 2023 Impact Factor: 3.1 2023 SCImago Journal Rankings: 0.810 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Ao, S. I. | - |
dc.contributor.author | Ng, M. K. | - |
dc.date.accessioned | 2019-09-18T08:34:39Z | - |
dc.date.available | 2019-09-18T08:34:39Z | - |
dc.date.issued | 2006 | - |
dc.identifier.citation | Soft Computing, 2006, v. 10, n. 4, p. 351-358 | - |
dc.identifier.issn | 1432-7643 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276785 | - |
dc.description.abstract | In this work, gene expression time series models have been constructed by using principal component analysis (PCA) and neural network (NN). The main contribution of this paper is to develop a methodology for modeling numerical gene expression time series. The PCA-NN prediction models are compared with other popular continuous prediction methods. The proposed model can give us the extracted features from the gene expressions time series and the orders of the prediction accuracies. Therefore, the model can help practitioners to gain a better understanding of a cell cycle, and to find the dependency of genes, which is useful for drug discoveries. Based on the results of two public real datasets, the PCA-NN method outperforms the other continuous prediction methods. In the time series model, we adapt Akaike's information criteria (AIC) tests and cross-validation to select a suitable NN model to avoid the overparameterized problem. © Springer-Verlag Berlin Heidelberg 2005. | - |
dc.language | eng | - |
dc.relation.ispartof | Soft Computing | - |
dc.subject | Gene expression | - |
dc.subject | Nonlinear network inference | - |
dc.subject | Neural network | - |
dc.subject | Time series | - |
dc.subject | Principal component analysis | - |
dc.title | Gene expression time series modeling with principal component and neural network | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/s00500-005-0494-8 | - |
dc.identifier.scopus | eid_2-s2.0-29544446923 | - |
dc.identifier.volume | 10 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 351 | - |
dc.identifier.epage | 358 | - |
dc.identifier.isi | WOS:000233725500010 | - |
dc.identifier.issnl | 1432-7643 | - |