Name
Abstract | ||
|---|---|---|
Samples of patients with or without disease can be diagnosed by serum proteomic pattern. Protein mass spectra are created by applying Surface-Enhanced Laser Desorption and Ionization (SELDI). A clinic diagnostic test to improve cancer pathology may be accomplished by this technology. In this paper, aim at FDA-NCI Clinical Proteomics Program Databank, first preprocess carefully data, sort the key features according to class separability criteria and extract the key features according to principal component analysis(PCA), set the size of the hidden layer neurons based on experience. Percentage correct classification is 100%. The results of experiment are analyzed according to confusion matrix and the receiver operating characteristic plot. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 13TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD) | cancer detection, rank features, feed-forward network, PCA, neurons |
Field | DocType | Citations |
Receiver operating characteristic,Confusion matrix,Feed forward network,Pattern recognition,Computer science,sort,Cancer detection,Artificial intelligence,Artificial neural network,Class separability,Principal component analysis,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shengyu Pei | 1 | 0 | 0.34 |
| Lang Tong | 2 | 5677 | 559.91 |
| Xia Li | 3 | 287 | 43.42 |
| Jin Jiang | 4 | 0 | 2.37 |
| Jingyu Huang | 5 | 0 | 0.34 |