Name
Abstract | ||
|---|---|---|
This work presents a tool which is an online implementation of the best machine learning-based model obtained after an exhaustive computational study. Twelve techniques were applied to schizophrenia data to obtain the results of this study and, with these, Quantitative Genotype - Disease Relationships (QDGRs) for disease prediction. Thus, the tool offers the possibility to introduce SNP sequences (which contain the SNPs considered in the study) in order to classify a patient. In the future, QDGR models could be extended to other diseases. The model implemented online is a linear neural network. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-21498-1_32 | IWANN (2) |
Keywords | DocType | Volume |
web tool,disease prediction,schizophrenia snp sequence classification,best machine,learning-based model,exhaustive computational study,qdgr model,linear neural network,online implementation,quantitative genotype,disease relationships,snp sequence,snp,neural networks,schizophrenia,machine learning,bioinformatics,data mining | Conference | 6692 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vanessa Aguiar-Pulido | 1 | 10 | 3.28 |
| José A. Seoane | 2 | 76 | 9.29 |
| Cristian R. Munteanu | 3 | 100 | 10.27 |
| Alejandro Pazos | 4 | 273 | 38.07 |