Netboost: Boosting-Supported Network Analysis Improves High-Dimensional Omics Prediction in Acute Myeloid Leukemia and Huntington's Disease

• Published in: IEEE/ACM transactions on computational biology and bioinformatics 2021-11, Vol.18 (6), p.2635-2648
• Main Authors: Schlosser, Pascal, Knaus, Jochen, Schmutz, Maximilian, Dohner, Konstanze, Plass, Christoph, Bullinger, Lars, Claus, Rainer, Binder, Harald, Lubbert, Michael, Schumacher, Martin
• Format: Article
• Language: English
• Subjects: Acute myeloid leukemia; Aggregates; Algorithms; Animals; Boosting; Chromatin; Classification; Cluster Analysis; Clustering; Computational Biology - methods; cox proportional hazards model; Datasets; dimension reduction; Dimensional analysis; Dimensionality reduction; Disease; Diseases; DNA; DNA methylation; DNA Methylation - genetics; Feature extraction; feature selection; Female; Gene expression; Gene sequencing; hierarchical clustering; Humans; Huntington Disease - diagnosis; Huntington Disease - genetics; Huntington Disease - mortality; huntington’s disease; Leukemia; Leukemia, Myeloid, Acute - diagnosis; Leukemia, Myeloid, Acute - genetics; Leukemia, Myeloid, Acute - mortality; Machine Learning; Male; Mice; Modules; Myeloid leukemia; Network analysis; Predictions; Proportional Hazards Models; random forest; Reduction; Survival
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2020.2983010

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.