Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes.
Christof WinterGlen KristiansenStephan KerstingJanine RoyDaniela AustThomas KnöselPetra RümmeleBeatrix JahnkeVera HentrichFelix RückertMarco NiedergethmannWilko WeichertMarcus BahraHans J. SchlittUtz SettmacherHelmut FriessMarkus W. BüchlerHans-Detlev SaegerMichael SchroederChristian PilarskyRobert GrützmannPublished in: PLoS Comput. Biol. (2012)
Keyphrases
- outcome prediction
- cancer patients
- breast cancer
- gene expression datasets
- cancer classification
- genomic data
- gene selection
- gene expression data
- microarray
- breast cancer patients
- microarray data
- random forest
- gene expression
- clinical data
- cancer diagnosis
- gene expression profiles
- prostate cancer
- clinical trials
- experimental conditions
- protein protein interaction networks
- feature ranking
- feature selection
- cell cycle
- databases
- gene ontology
- high throughput
- logistic regression
- decision trees
- protein protein interactions
- ensemble methods
- high dimensional
- data sets