Abstract
Introduction: Precision medicine requires the accurate identification of genes and pathways that mechanistically define a disease phenotype. Modern omics may deliver this, but has until now yielded...
Highlights
Precision medicine requires the accurate identification of genes and pathways that mechanistically define a disease phenotype
To evaluate the results produced by Grand Forest, we extracted gene modules from gene expression data from patients diagnosed with breast cancer, non-small cell lung cancer, ulcerative colitis, Huntington’s disease, and amyotrophic lateral sclerosis (ALS)
We evaluated the biological relevance of the extracted gene modules by investigating how congruent the genes in the extracted modules were with published curated molecular pathways related to the phenotype of each data set
Summary
Precision medicine requires the accurate identification of genes and pathways that mechanistically define a disease phenotype. Methods: We here present Grand Forest, an ensemble learning method that extends random forests and integrates experimental data with molecular interaction networks to discover relevant endophenotypes and their defining gene modules. Using the unsupervised method to discover gene modules from unlabeled data, lung cancer patients could be de novo stratified into clinically relevant molecular subgroups. By classifying patients as different subtypes, the aim is to stratify patients into groups with distinct clinical traits, such as expected survival time, risk of disease recurrence, or response to treatment. To this end, significant effort a Simon J.
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