DeepCC: a novel deep learning-based framework for cancer molecular subtype classification

Abstract

Molecular subtyping of cancer is a critical step towards more individualized therapy and provides important biological insights into cancer heterogeneity. Although gene expression signature-based classification has been widely demonstrated to be an effective approach in the last decade, the widespread implementation has long been limited by platform differences, batch effects, and the difficulty to classify individual patient samples. Here, we describe a novel supervised cancer classification framework, deep cancer subtype classification (DeepCC), based on deep learning of functional spectra quantifying activities of biological pathways. In two case studies about colorectal and breast cancer classification, DeepCC classifiers and DeepCC single sample predictors both achieved overall higher sensitivity, specificity, and accuracy compared with other widely used classification methods such as random forests (RF), support vector machine (SVM), gradient boosting machine (GBM), and multinomial logistic regression algorithms. Simulation analysis based on random subsampling of genes demonstrated the robustness of DeepCC to missing data. Moreover, deep features learned by DeepCC captured biological characteristics associated with distinct molecular subtypes, enabling more compact within-subtype distribution and between-subtype separation of patient samples, and therefore greatly reduce the number of unclassifiable samples previously. In summary, DeepCC provides a novel cancer classification framework that is platform independent, robust to missing data, and can be used for single sample prediction facilitating clinical implementation of cancer molecular subtyping.

Construction and Validation of Novel Diagnostic and Prognostic DNA Methylation Signatures for Hepatocellular Carcinoma

“…After preselection, 1306 candidate markers were available for further analysis. For each marker, the highest balanced accuracy (BA) for optimal β-value threshold was obtained as follows (Chang and Chen, 2019;Gao et al, 2019):…”

Machine learning analysis of exome trios to contrast the genomic architecture of autism and schizophrenia

“…For example, they have been used to distinguish between age-related cognitive decline and dementias based on neurocognitive tests [25]. Further, they have also been successfully used to distinguish and study different cancer types based on gene expressions [26,27] and DNA methylation patterns [28].…”

Developing Sustainable Classification of Diseases via Deep Learning and Semi-Supervised Learning

“…To tackle these problems, a classifier named the forest deep neural network (FDNN) has been developed to integrate a deep neural network architecture with a supervised forest feature detector in RNA-seq expression datasets [18]. In addition, cancer subtype classification with deep learning can be used for single sample prediction to facilitate clinical implementation of cancer molecular subtyping [19]. The deep forest (DF) model, a decision tree ensemble approach with a non-neural network style deep model, is used in this work because it has been shown to achieve good performance in many tasks [20].…”