forgeNet: a graph deep neural network model using tree-based ensemble classifiers for feature graph construction

Abstract

A unique challenge in predictive model building for omics data has been the small number of samples (n) versus the large amount of features (p). This "n p" property brings difficulties for disease outcome classification using deep learning techniques. Sparse learning by incorporating external gene network information such as the graph-embedded deep feedforward network (GEDFN) [19] model has been a solution to this issue. However, such methods require an existing feature graph, and potential mis-specification of the feature graph can be harmful on classification and feature selection. To address this limitation and develop a robust classification model without relying on external knowledge, we propose a forest graph-embedded deep feedforward network (forgeNet) model, to integrate the GEDFN architecture with a forest feature graph extractor, so that the feature graph can be learned in a supervised manner and specifically constructed for a given prediction task. To validate the method's capability, we experimented the forgeNet model with both synthetic and real datasets. The resulting high classification accuracy suggests that the method is a valuable addition to sparse deep learning models for omics data.

Active disease-related compound identification based on capsule network

“…The molecular descriptor is utilized to represent each compound. In order to reflect the effectiveness of CapsNet, SVM [ 47 ], RF [ 48 ], gcForest [ 49 ] and forgeNet [ 50 ] are also used to screen the effective compounds in traditional Chinese medicine prescriptions for treating diseases. ROC curve and AUC value are utilized to evaluate the performance of the classifiers.…”

Deep Neural Network for Somatic Mutation Classification

“…Recently, Kong and Yu presented a novel classifier based on the feature graph and deep neural network, namely, forgeNet. forgeNet was utilized to process RNA-seq data from public databases, and the results proved that this method was valuable for classification and feature selection for biology data [36]. us, in this paper, forgeNet is utilized to detect somatic mutations from the sequencing data.…”

Drug Components-Disease Network Related to Acute Lung Injury Inference Based on Forest Graph-embedded Deep Feedforward Network

“…(1) Data preparation. Two key target genes: signal transducer and activator of transcription 3 (STAT3), and nuclear transcription factor-κ B/p65 (nuclear factor kappa, B/p65, REAL) were proved to be mainly involved in the key pathways related to acute lung injury (ALI), and losely related to ALI diseases in the literature [40]. Then, the BindingDB database is searched for the known active compounds of two key target genes [38].…”