Name
Abstract | ||
|---|---|---|
Clinical proteomics is suffering from high hopes generated by reports on apparent biomarkers, most of which could not be later substantiated via validation. This has brought into focus the need for improved methods of finding a panel of clearly defined biomarkers. To examine this problem, urinary proteome data was collected from healthy adult males and females, and analysed to find biomarkers that differentiated between genders. We believe that models that incorporate sparsity in terms of variables are desirable for biomarker selection, as proteomics data typically contains a huge number of variables (peptides) and few samples making the selection process potentially unstable. This suggests the application of a two-level hierarchical Bayesian probit regression model for variable selection which assumes a prior that favours sparseness. The classification performance of this method is shown to improve that of the Probabilistic K-Nearest Neighbour model. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-04031-3_13 | PRIB |
Keywords | Field | DocType |
biomarker selection,apparent biomarkers,selection process,bayesian solution,valid proteomic biomarkers,urinary proteome data,clinical proteomics,probabilistic k-nearest neighbour model,regression model,proteomics data,classification performance,variable selection,bayesian inference,feature selection | Probit model,Bayesian inference,Feature selection,Proteomics,Computer science,Biomarker (medicine),Artificial intelligence,Probabilistic logic,Bioinformatics,Classifier (linguistics),Machine learning,Bayesian probability | Conference |
Volume | ISSN | Citations |
5780 | 0302-9743 | 1 |
PageRank | References | Authors |
0.40 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keith Harris | 1 | 14 | 1.60 |
| Mark Girolami | 2 | 1382 | 141.16 |
| Harald Mischak | 3 | 15 | 2.31 |