Abstract | ||
---|---|---|
As interest within bioinformatics has been vastly increased, efforts to predict functional role of proteins have been made using diverse approaches. In this paper, we discuss a protein function prediction method that utilizes protein molecular information including protein interaction data. The proposed method takes the given problem into account as a K-class classification problem and resolves the new problem by using a modular neural network based predictive approach. The simulation demonstrates that the proposed approach predicts the functional roles of Yeast proteins with unknown functional knowledge and is competitive to the other methodologies in KDD Cup 2001 competition. |
Year | DOI | Venue |
---|---|---|
2005 | 10.1007/11551188_43 | ICAPR (1) |
Keywords | Field | DocType |
k-class classification problem,diverse approach,protein function prediction method,utilizes protein,unknown functional knowledge,protein interaction data,modular neural approach,predictive approach,new problem,yeast protein,predictive model,functional role,prediction model,protein function prediction | Computer science,Modular neural network,Yeast Proteins,Artificial intelligence,Protein function,Modular design,Artificial neural network,Protein function prediction,Machine learning | Conference |
Volume | ISSN | ISBN |
3686 | 0302-9743 | 3-540-28757-4 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Doosung Hwang | 1 | 47 | 4.75 |
Ungmo Kim | 2 | 58 | 11.90 |
Jae-Hun Choi | 3 | 29 | 5.57 |
Jeho Park | 4 | 20 | 4.56 |
Jang-Hee Yoo | 5 | 244 | 17.91 |