# Difference between revisions of "Summary for survey of neural networked-based cancer prediction models from microarray data"

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== Background == | == Background == | ||

− | + | '''Neural Network''' | |

+ | Neural networks are often used to solve non-linear complex problems. It is an operational model consisting of a large number of neurons connected to each other by different weights. In this network structure, each neuron is related to an activation function. The difference between the output of the neural network and the desired output is what we called error. | ||

+ | Backpropagation mechanism is one of the most commonly used algorithms in solving neural network problems. By using this algorithm, we optimize the objective function by propagating back the generated error through the network to adjust the weights. | ||

+ | In the next sections, we will use the above algorithm but with different network architectures and different number of neurons to review the neural network-based cancer prediction models for learning the gene expression features. | ||

− | + | '''Cancer prediction models''' | |

− | + | High dimensionality and the spatial structure are the two main factors that can affect the accuracy of the cancer prediction models. They add irrelevant noisy features to our selected models. We have 3 ways to determine the accuracy of a model. | |

− | + | The first is called ROC curve. It reflects the sensitivity of the response to the same signal stimulus under different criteria. To test its validity, we need to consider it with the confidence interval. Usually, a model is good one when its ROC is greater than 0.7. Another way to measure the performance of a model is to use CI, which explains the concordance probability of the predicted and observed survival. The closer its value to 0.7, the better the model is. The third measurement method is using the Brier score. A brier score measures the average difference between the observed and the estimated survival rate in a given period of time. It ranges from 0 to 1, and a lower score indicates higher accuracy. | |

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## Revision as of 13:13, 16 November 2020

## 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

**Neural Network**
Neural networks are often used to solve non-linear complex problems. It is an operational model consisting of a large number of neurons connected to each other by different weights. In this network structure, each neuron is related to an activation function. The difference between the output of the neural network and the desired output is what we called error.
Backpropagation mechanism is one of the most commonly used algorithms in solving neural network problems. By using this algorithm, we optimize the objective function by propagating back the generated error through the network to adjust the weights.
In the next sections, we will use the above algorithm but with different network architectures and different number of neurons to review the neural network-based cancer prediction models for learning the gene expression features.

**Cancer prediction models**
High dimensionality and the spatial structure are the two main factors that can affect the accuracy of the cancer prediction models. They add irrelevant noisy features to our selected models. We have 3 ways to determine the accuracy of a model.
The first is called ROC curve. It reflects the sensitivity of the response to the same signal stimulus under different criteria. To test its validity, we need to consider it with the confidence interval. Usually, a model is good one when its ROC is greater than 0.7. Another way to measure the performance of a model is to use CI, which explains the concordance probability of the predicted and observed survival. The closer its value to 0.7, the better the model is. The third measurement method is using the Brier score. A brier score measures the average difference between the observed and the estimated survival rate in a given period of time. It ranges from 0 to 1, and a lower score indicates higher accuracy.