Class-Incremental Learning With Deep Generative Feature Replay for DNA Methylation-Based Cancer Classification

Abstract

Developing lifelong learning algorithms are mandatory for computational systems biology. Recently, many studies have shown how to extract biologically relevant information from high-dimensional data to understand the complexity of cancer by taking the benefit of deep learning (DL). Unfortunately, new cancer growing up into the hundred types that make systems difficult to classify them efficiently. In contrast, the current state-of-the-art continual learning (CL) methods are not designed for the dynamic characteristics of high-dimensional data. And data security and privacy are some of the main issues in the biomedical field. This paper addresses three practical challenges for class-incremental learning (Class-IL) such as data privacy, high-dimensionality, and incremental learning problems. To solve this, we propose a novel continual learning approach, called Deep Generative Feature Replay (DGFR), for cancer classification tasks. DGFR consists of an incremental feature selection (IFS) and a scholar network (SN). IFS is used for selecting the most significant CpG sites from high-dimensional data. We investigate different dimensions to find an optimal number of selected CpG sites. SN employs a deep generative model for generating pseudo data without accessing past samples and a neural network classifier for predicting cancer types. We use a variational autoencoder (VAE), which has been successfully applied to this research field in previous works. All networks are sequentially trained on multiple tasks in the Class-IL setting. We evaluated the proposed method on the publicly available DNA methylation data. The experimental results show that the proposed DGFR achieves a significantly superior quality of cancer classification tasks with various state-of-the-art methods in terms of accuracy.

Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data

“…Applications of DL to assist physicians and scientists in clinical settings using epigenomic data have been relatively unexplored until very recently. Except for one paper published in 2016, 21 out of 22 papers reviewed were published in the last 5 years and a majority of the models were developed by USA and China research teams [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. This suggests a novel field of study that gains an increasing interest.…”

“…A class-incremental learning approach called Deep Generative Feature Reply was proposed for cancer classification tasks with superior accuracy [ 34 ]. The model is composed of an incremental feature selection for selecting the most significant CpG sites and a scholar network in which a VAE acted as a generator for generating pseudo data without accessing past samples and a neural network classifier acted as a predictor for cancer types.…”

Explainable Artificial Intelligence Based Framework for Non-Communicable Diseases Prediction

“…Moreover, it should be noted that most researchers in previous studies aimed to apply the feature selection techniques for not only higher accuracy but also an improvement in understanding the causes of NCDs. The NCDs predictive results of previous studies implied that DNN, SVM, Ensemble classifiers achieved the best performances when compared with other baseline models [28][29].…”

“…Various studies have focused on the accuracy enhancement of NCDs diagnostic models concerning feature selection techniques and refined machine-learning classifiers [18][19][20][21][22][23][24][25][26][27][28][29].…”

Deep Learning Feature Extraction Approach for Hematopoietic Cancer Subtype Classification

“…Over the years, various approaches for data mining have been applied on many cancer research studies. Specifically, a deep learning method was applied in this area [ 19 , 20 , 21 , 22 , 23 ]. Ahmed M et al [ 19 ] developed a breast cancer classification model using deep belief networks in an unsupervised part for learning input feature statistics.…”