Name
Title | ||
|---|---|---|
An approach for deciphering patient-specific variations with application to breast cancer molecular expression profiles. |
Abstract | ||
|---|---|---|
•Selective-voting ensemble classification approach (SVA) is proposed.•SVA is useful in discerning good-prognosis and poor-prognosis breast cancer samples.•SVA adapts the features across the samples revealing patient-specific variations.•Patient-specific networks reveal distinct topologies across poor-prognosis samples. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.jbi.2016.07.022 | Journal of Biomedical Informatics |
Keywords | Field | DocType |
Translational bioinformatics,Precision medicine,Data mining,Molecular profiling | Data mining,Translational bioinformatics,Precision medicine,Breast cancer,Computer science,Support vector machine,Correlation,Artificial intelligence,Nottingham Prognostic Index,Machine learning,Bayes classifier,Bayes' theorem | Journal |
Volume | Issue | ISSN |
63 | C | 1532-0464 |
Citations | PageRank | References |
1 | 0.39 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Radhakrishnan Nagarajan | 1 | 82 | 12.21 |
| Meenakshi Upreti | 2 | 11 | 2.04 |