Identifying the molecular pathways more prone to disruption during a pathological process is a key task in network medicine and, more generally, in systems biology. This chapter describes a pipeline that couples a machine learning solution for molecular profiling with a recent network comparison method. The pipeline can identify changes occurring between specific sub-modules of networks built in a case-control biomarker study, discriminating key groups of genes whose interactions are modified by an underlying condition. Different algorithms can be chosen to implement the workflow steps. Three applications on genome-wide data are presented regarding the susceptibility of children to air pollution, and early and late onset of Parkinsonʼs and Alzheimerʼs diseases
Barla, A.; Jurman, G.; Visintainer, R.; Squillario, M.; Filosi, M.; Riccadonna, S.; Furlanello, C. (2014). A Machine learning pipeline for identification of discriminant pathways. In: Springer Handbook of Bio-/Neuroinformatics (editor(s) Kasabov, N.). Berlin, Heidelberg: Springer: 951-968. ISBN: 9783642305733 doi: 10.1007/978-3-642-30574-0_53. handle: http://hdl.handle.net/10449/24773
A Machine learning pipeline for identification of discriminant pathways
Riccadonna, Samantha;
2014-01-01
Abstract
Identifying the molecular pathways more prone to disruption during a pathological process is a key task in network medicine and, more generally, in systems biology. This chapter describes a pipeline that couples a machine learning solution for molecular profiling with a recent network comparison method. The pipeline can identify changes occurring between specific sub-modules of networks built in a case-control biomarker study, discriminating key groups of genes whose interactions are modified by an underlying condition. Different algorithms can be chosen to implement the workflow steps. Three applications on genome-wide data are presented regarding the susceptibility of children to air pollution, and early and late onset of Parkinsonʼs and Alzheimerʼs diseasesFile | Dimensione | Formato | |
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