Pathway-level disease data mining through hyper-box principles
| Autor: | Frank O. Nestle, Sophia Tsoka, Aristotelis Kittas, Chrysanthi Ainali, Lazaros G. Papageorgiou, Lingjian Yang |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Statistics and Probability
Molecular interactions General Immunology and Microbiology Association rule learning Microarray analysis techniques Applied Mathematics Gene Expression Profiling Gene sets Disease classification Genome-wide association study Breast Neoplasms General Medicine Disease Biology Models Theoretical computer.software_genre General Biochemistry Genetics and Molecular Biology Modeling and Simulation Data Mining Humans Psoriasis Data mining General Agricultural and Biological Sciences computer Gene |
| Zdroj: | Mathematical biosciences. 260 |
| ISSN: | 1879-3134 |
| Popis: | In microarray data analysis, traditional methods that focus on single genes are increasingly replaced by methods that analyse functional units corresponding to biochemical pathways, as these are considered to offer more insight into gene expression and disease associations. However, the development of robust pipelines to relate genotypic functional modules to disease phenotypes through known molecular interactions is still at its early stages. In this article we first discuss methodologies that employ groups of genes in disease classification tasks that aim to link gene expression patterns with disease outcome. Then we present a pathway-based approach for disease classification through a mathematical programming model based on hyper-box principles. Association rules derived from the model are extracted and discussed with respect to pathway-specific molecular patterns related to the disease. Overall, we argue that the use of gene sets corresponding to disease-relevant pathways is a promising route to uncover expression-to-phenotype relations in disease classification and we illustrate the potential of hyper-box classification in assessing the predictive power of functional pathways and uncover the effect of specific genes in the prediction of disease phenotypes. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect