• 沒有找到結果。

Artificial Neural Network Prediction for Cancer Survival Time by Gene Expression Data

N/A
N/A
Info
下載
Protected

Academic year: 2021

Share "Artificial Neural Network Prediction for Cancer Survival Time by Gene Expression Data"

Copied!
1
0
0

加載中.... (立即查看全文)

立即下載 ( 1 頁 )

全文

(1)

Artificial Neural Network Prediction for Cancer

Survival Time by Gene Expression Data

邱泓文

Chen YC;Yang WW;Chiu HW

Abstract

This study aimed at training artificial neural networks (ANN) to predict survival time in cancer patients by using microarray and clinical data. We analyzed public microarrays and clinical data sets in different kinds of cancer. We selected 15-30 genes (correlation coefficient>0.4) as ANN variables to train networks. The results shows ANN can predict survival time from Microarray data gene expression and the prediction made by the proposed neural models show a good agreement with the measurements.

參考文獻

立即下載 ( PDF - 1 頁 - 32.73 KB )

相關文件

A smoothed NR neural network for solving nonlinear convex programs with second-order cone constraints

In this paper, we have studied a neural network approach for solving general nonlinear convex programs with second-order cone constraints.. The proposed neural network is based on

-Artificial Neural Network(ANN) -

Categories of Network Types by Broad Learning Method.

Support Vector Classification and Regression

Predict daily maximal load of January 1999 A time series prediction problem.. Data

The TSQL2 Data Model

We showed that the BCDM is a unifying model in that conceptual instances could be mapped into instances of five existing bitemporal representational data models: a first normal

An introduction11

“The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease.” Science 313(5795):..

Slide credit from 1

Idea: condition the neural network on all previous words and tie the weights at each time step. Assumption: temporal

Connectivity Map

“The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease.” Science 313(5795): 1929–1935.

碩 士 論 文 中 華 大 學

To solve this problem, this study proposed a novel neural network model, Ecological Succession Neural Network (ESNN), which is inspired by the concept of ecological succession