Name
Title | ||
|---|---|---|
Learning yeast gene functions from heterogeneous sources of data using hybrid weighted Bayesian networks. |
Abstract | ||
|---|---|---|
We developed a machine learning system for determining gene functions from heterogeneous sources of data sets using a Weighted Naive Bayesian Network (WNB). The knowledge of gene functions is crucial for understanding many fundamental biological mechanisms such as regulatory pathways, cell cycles and diseases. Our major goal is to accurately infer functions of putative genes or ORFs (Open Reading Frames) from existing databases using computational methods. However, this task is intrinsically difficult since the underlying biological processes represent complex interactions of multiple entities. Therefore many functional links would be missing when only one or two source of data is used in the prediction. Our hypothesis is that integrating evidence from multiple and complementary sources could significantly improve the prediction accuracy. In this paper, our experimental results not only suggest that the above hypothesis is valid, but also provide guidelines for using the WNB system for data collection, training and predictions. The combined training data sets contain information from gene annotations, gene expressions, clustering outputs, keyword annotations and sequence homology from public databases. The current system is trained and tested on the genes of budding yeast Saccharomyces cerevisiae. Our WNB model can also be used to analyze the contribution of each source of information toward the prediction performance through the weight training process. The contribution analysis could potentially lead to significant scientific discovery by facilitating the interpretation and understanding of the complex relationships between biological entities. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/CSB.2005.38 | CSB |
Keywords | Field | DocType |
public databases,weighted naive bayesian network,belief networks,gene annotation,cellular biophysics,pattern clustering,gene function,yeast,diseases,cell cycles,data sets,current system,gene functions,wnb system,biological mechanism,gene function prediction,learning (artificial intelligence),genetics,learning yeast,sequence homology,microorganisms,gene expression,biology computing,hybrid weighted bayesian networks,biological entity,open reading frames,bayesian network,budding yeast,keyword annotation,wnb model,putative gene,machine learning system,clustering outputs,computational method,machine learning,saccharomyces cerevisiae,data collection,heterogeneous sources,combined training data set,regulatory pathways,weight training,biological process,cell cycle,learning artificial intelligence,open reading frame | Data mining,Data set,Expression (mathematics),Computer science,Mechanism (biology),Artificial intelligence,Cluster analysis,Data collection,Naive Bayes classifier,Bayesian network,Bioinformatics,Gene Annotation,Machine learning | Conference |
ISSN | ISBN | Citations |
1551-7497 | 0-7695-2344-7 | 2 |
PageRank | References | Authors |
0.43 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xutao Deng | 1 | 86 | 8.22 |
| Huimin Geng | 2 | 37 | 7.02 |
| Hesham Ali | 3 | 29 | 5.18 |