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Netboost: boosting-supported network analysis improves high-dimensional omics prediction in acute myeloid leukemia and Huntington's disease

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Item Type:Article
Title:Netboost: boosting-supported network analysis improves high-dimensional omics prediction in acute myeloid leukemia and Huntington's disease
Creators Name:Schlosser, P. and Knaus, J. and Schmutz, M. and Döhner, K. and Plass, C. and Bullinger, L. and Claus, R. and Binder, H. and Lübbert, M. and Schumacher, M.
Abstract:State-of-the art selection methods fail to identify weak but cumulative effects of features found in many high-dimensional omics datasets. Nevertheless, these features play an important role in certain diseases. We present Netboost, a three-step dimension reduction technique. First, a boosting-based filter is combined with the topological overlap measure to identify the essential edges of the network. Second, sparse hierarchical clustering is applied on the selected edges to identify modules and finally module information is aggregated by the first principal components. We demonstrate the application of the newly developed Netboost in combination with CoxBoost for survival prediction of DNA methylation and gene expression data from 180 acute myeloid leukemia (AML) patients and show, based on cross-validated prediction error curve estimates, its prediction superiority over variable selection on the full dataset as well as over an alternative clustering approach. The identified signature related to chromatin modifying enzymes was replicated in an independent dataset, the phase II AMLSG 12-09 study. In a second application we combine Netboost with Random Forest classification and improve the disease classification error in RNA-sequencing data of Huntington's disease mice. Netboost is a freely available Bioconductor R package for dimension reduction and hypothesis generation in high-dimensional omics applications.
Keywords:Acute Myeloid Leukemia, Boosting, Cox Proportional Hazards Model, Dimension Reduction, DNA Methylation, Feature Selection, Gene Expression, Hierarchical Clustering, Huntington's Disease, Random Forest, Survival, Animals, Mice
Source:IEEE/ACM Transactions on Computational Biology and Bioinformatics
Publisher:IEEE Computer Society
Page Range:2635-2648
Date:1 November 2021
Official Publication:https://doi.org/10.1109/TCBB.2020.2983010
PubMed:View item in PubMed

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