Name
Title | ||
|---|---|---|
Experimental comparison of classification methods for key kinase identification for neurite elongation. |
Abstract | ||
|---|---|---|
Kinases in a developing neuron play important roles in elongating a neurite with their complex interactions. To elucidate the effect of each kinase on neurite elongation and regeneration from a small set of experiments, we applied machine learning methods to synthetic datasets based on a biologically feasible model. The result showed the ridged partial least squares (RPLS) algorithm performed better than other standard algorithms such as naive Bayes classifier, support vector machines and random forest classification. This suggests the effectiveness of dimension reduction done in RPLS. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/EMBC.2013.6610301 | EMBC |
Keywords | Field | DocType |
biomechanics,biologically feasible model,naive bayes classifier,ridged partial least square algorithm,neurophysiology,machine learning methods,kinase identification,learning (artificial intelligence),synthetic datasets,rpls algorithm,enzymes,biochemistry,molecular biophysics,least squares approximations,bayes methods,complex interactions,neurite elongation,random forest classification,medical computing,elongation,support vector machines,classification methods,vectors,learning artificial intelligence,chemicals,vegetation | Standard algorithms,Dimensionality reduction,Computer science,Partial least squares regression,Artificial intelligence,Computational biology,Neurite,Random forest,Computer vision,Naive Bayes classifier,Least squares support vector machine,Support vector machine,Machine learning | Conference |
Volume | ISSN | Citations |
2013 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 2 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuji Yoshida | 1 | 0 | 0.34 |
| Kei Majima | 2 | 0 | 0.34 |
| Tatsuya Yamada | 3 | 0 | 0.34 |
| Yuki Maruno | 4 | 0 | 0.34 |
| Yuichi Sakumura | 5 | 0 | 0.34 |
| K. Ikeda | 6 | 241 | 55.17 |