Hierarchical Bayesian Neural Network for Gene Expression Temporal Patterns
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| 024 | 7 | 0 | |a 10.2202/1544-6115.1038 |2 doi |
| 035 | |a (NATIONALLICENCE)gruyter-10.2202/1544-6115.1038 | ||
| 245 | 0 | 0 | |a Hierarchical Bayesian Neural Network for Gene Expression Temporal Patterns |h [Elektronische Daten] |c [Yulan Liang, Arpad G Kelemen] |
| 520 | 3 | |a There are several important issues to be addressed for gene expression temporal patterns' analysis: first, the correlation structure of multidimensional temporal data; second, the numerous sources of variations with existing high level noise; and last, gene expression mostly involves heterogeneous multiple dynamic patterns. We propose a Hierarchical Bayesian Neural Network model to account for the input correlations of time course gene array data. The variations in absolute gene expression levels and the noise can be estimated with the hierarchical Bayesian setting. The network parameters and the hyperparameters were simultaneously optimized with Monte Carlo Markov Chain simulation. Results show that the proposed model and algorithm can well capture the dynamic feature of gene expression temporal patterns despite the high noise levels, the highly correlated inputs, the overwhelming interactions, and other complex features typically present in microarray data. We test and demonstrate the proposed models with yeast cell cycle temporal data sets. The model performance of Hierarchical Bayesian Neural Network was compared to other popular machine learning methods such as Nearest Neighbor, Support Vector Machine, and Self Organized Map. | |
| 540 | |a ©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston | ||
| 690 | 7 | |a Statistical Theory and Methods |2 nationallicence | |
| 690 | 7 | |a Microarrays |2 nationallicence | |
| 690 | 7 | |a Hierarchical Bayesian Neural Networks |2 nationallicence | |
| 690 | 7 | |a Heterogeneous gene expression temporal patterns |2 nationallicence | |
| 690 | 7 | |a Hyper-prior |2 nationallicence | |
| 690 | 7 | |a Monte Carlo Markov Chain |2 nationallicence | |
| 700 | 1 | |a Liang |D Yulan |u Department of Biostatistics, University at Buffalo |4 aut | |
| 700 | 1 | |a Kelemen |D Arpad G. |u Department of Computer and Information Science, Niagara University, Department of Biostatistics, University at Buffalo |4 aut | |
| 773 | 0 | |t Statistical Applications in Genetics and Molecular Biology |d De Gruyter |g 3/1(2004-09-03), 1-23 |q 3:1<1 |1 2004 |2 3 |o sagmb | |
| 856 | 4 | 0 | |u https://doi.org/10.2202/1544-6115.1038 |q text/html |z Onlinezugriff via DOI |
| 908 | |D 1 |a research article |2 jats | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.2202/1544-6115.1038 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Liang |D Yulan |u Department of Biostatistics, University at Buffalo |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Kelemen |D Arpad G. |u Department of Computer and Information Science, Niagara University, Department of Biostatistics, University at Buffalo |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Statistical Applications in Genetics and Molecular Biology |d De Gruyter |g 3/1(2004-09-03), 1-23 |q 3:1<1 |1 2004 |2 3 |o sagmb | ||
| 900 | 7 | |b CC0 |u http://creativecommons.org/publicdomain/zero/1.0 |2 nationallicence | |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-gruyter | ||