Name
Abstract | ||
|---|---|---|
Cancer data analysis is significant to detect the codes that are responsible for cancer diseases. It is significant to find out the coding regions from diseases infected biological data. The infected data will be helpful to design proper drugs and will be supportable in laboratory assessments. Codes bear specific meaning on various features as well as symptoms of diseases. Coding of biological data is a key area to get exact information on animals to discover the desired medicine. In the current work, four different machine learning approaches such as support vector machine (SVM), principal component analysis (PCA) technique, neural mapping skyline filtering (NMSF) and Fisher’s discriminant analysis (FDA) were applied for data reduction and coding area selection. The experimental analysis established that the SVM outperforms PCA and FDA. However, due to the mapping facility, NMSF outperforms SVM. Thus, the NMSF achieved the preeminent results among the four techniques. Matthews’s correlation coefficient was used to evaluate the accuracy, specificity, sensitivity, F-measures and error rate of the four methods that are used to determine the coding area. Detailed experimental analysis included comparison study among the four classifiers for the deoxyribonucleic acid dataset. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s00521-016-2513-3 | Neural Computing and Applications |
Keywords | Field | DocType |
Principal component analysis (PCA), Support vector machine (SVM), Neural mapping skyline filtering (NMSF), Fisher’s discriminant analysis (FDA), Cancer DNA dataset, Matthews’s correlation coefficient (MCC) | Skyline,Biological data,Pattern recognition,Computer science,Support vector machine,Word error rate,Filter (signal processing),Coding (social sciences),Artificial intelligence,Linear discriminant analysis,Principal component analysis,Machine learning | Journal |
Volume | Issue | ISSN |
29 | 4 | 1433-3058 |
Citations | PageRank | References |
7 | 0.43 | 56 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Md Sarwar Kamal | 1 | 40 | 5.77 |
| Nilanjan Dey | 2 | 521 | 78.41 |
| Sonia Farhana Nimmy | 3 | 9 | 1.46 |
| Shamim Hasnat Ripon | 4 | 26 | 2.58 |
| Md. Nawab Yousuf Ali | 5 | 20 | 5.21 |
| Amira S. Ashour | 6 | 203 | 27.96 |
| wahiba ben abdessalem karaa | 7 | 11 | 2.51 |
| Nhu Gia Nguyen | 8 | 25 | 3.45 |
| Fuqian Shi | 9 | 83 | 11.70 |