Paper Info
Title
Predictive Model for Yeast Protein Functions Using Modular Neural Approach
Abstract
In this paper we use a modular neural network to predict the molecular functions of yeast proteins. To solve this class problem, our proposed approach decomposes the originalproblem into a set of solvable 2-class subproblems using class information. Each 2-class problem has a set of positive and negative data. The yeast data is not equally distributedin function classes and hinders the learning of each neural network. We adopt a sampling strategy that generates a set of new class data to the subordinate class in order to balance the positive and negative data set. In data preparation, the biological concept of "guilt-by-interaction" is used for covering possible interaction partners among proteins of known functions. The proposed framework has been tested as a predictive model of yeast protein functions where the data source is stored in a relational database. In the experiments,the proposed system shows an average accuracy of 91:0% in the test set.
Year
DOI
Venue
2003
10.1109/BIBE.2003.1188984
BIBE
Keywords
Field
DocType
predictive model,data preparation,class information,subordinate class,data source,distributedin function class,modular neural approach,yeast data,yeast protein,negative data,test set,class problem,new class data,neural network,prediction model,testing,neural networks,relational database,molecular biophysics,relational databases,neural nets,genetics,proteins,computer networks,computer science,predictive models
Data source,Relational database,Computer science,Modular neural network,Yeast,Artificial intelligence,Sampling (statistics),Bioinformatics,Modular design,Artificial neural network,Machine learning,Test set
Conference
ISBN
Citations 
PageRank 
0-7695-1907-5
0
0.34
References 
Authors
3
4
Name
Order
Citations
PageRank
Doosung Hwang1474.75
Farshad Fotouhi21023122.73
Russell L. Finley Jr.3573.06
William I. Grosky4696223.08