Summary for survey of neural networked-based cancer prediction models from microarray data

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Presented by

Rao Fu, Siqi Li, Yuqin Fang, Zeping Zhou

Introduction

Microarray technology is widely used in analyzing genetic diseases since it can help researchers to detect genetic information rapidly. In the study of cancer, the researchers use this technology to compare normal and abnormal cancerous tissues so that they can understand better about the pathology of cancer. However, what might affect the accuracy and computation time of this cancer model is the high dimensionality of the gene expressions. To cope with this problem, we need to use the feature selection method or feature creation method. One of the most powerful methods in machine learning is neural networks. In this paper, we will review the latest neural network-based cancer prediction models by presenting the methodology of preprocessing, filtering, prediction, and clustering gene expressions.

Background

The current architecture is built on the network-in-network approach proposed by Lin et al.[1] for the purpose of increase the representation power of the neural networks. They added additional 1 X 1 convolutional layers, serving as dimension reduction modules to significantly reduce the number of parameters of the model. The paper also took inspiration from the Regions with Convolutional Neural Networks (R-CNN) proposed by Girshick et al. [2]. The overall detection problem is divided into two subproblems: to first utilize low-level cues for potential object proposals, and to then use CNN to classify object categories.