Summary and Challenges

[1]  J J Chen,et al.  Selection of differentially expressed genes in microarray data analysis , 2007, The Pharmacogenomics Journal.

[2]  M. Bartels,et al.  Overview of the MELD score and the UNOS adult liver allocation system. , 2007, Transplantation proceedings.

[3]  L. Ohno-Machado,et al.  Neural network applications in physical medicine and rehabilitation. , 1999, American journal of physical medicine & rehabilitation.

[4]  Lucila Ohno-Machado,et al.  Effects of SVM parameter optimization on discrimination and calibration for post-procedural PCI mortality , 2007, J. Biomed. Informatics.

[5]  Lucila Ohno-Machado,et al.  A global goodness-of-fit test for receiver operating characteristic curve analysis via the bootstrap method , 2005, J. Biomed. Informatics.

[6]  J. G. Liao,et al.  Logistic regression for disease classification using microarray data: model selection in a large p and small n case , 2007, Bioinform..

[7]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[8]  Lucila Ohno-Machado,et al.  The use of receiver operating characteristic curves in biomedical informatics , 2005, J. Biomed. Informatics.

[9]  Chen-An Tsai,et al.  Significance Analysis of ROC Indices for Comparing Diagnostic Markers: Applications to Gene Microarray Data , 2004, Journal of biopharmaceutical statistics.

[10]  H. Cordell,et al.  SNP Selection in Genome-Wide and Candidate Gene Studies via Penalized Logistic Regression , 2010, Genetic epidemiology.

[11]  L Ohno-Machado,et al.  A comparison of Cox proportional hazards and artificial neural network models for medical prognosis , 1997, Comput. Biol. Medicine.

[12]  Berthold Lausen,et al.  Bootstrap estimated true and false positive rates and ROC curve , 2008 .

[13]  Lucila Ohno-Machado,et al.  Discrimination and calibration of mortality risk prediction models in interventional cardiology , 2005, J. Biomed. Informatics.

[14]  John D. Storey.  The positive false discovery rate: a Bayesian interpretation and the q-value , 2003 .

[15]  L. Ohno-Machado,et al.  Is there an advantage in scoring early embryos on more than one day? , 2009, Human reproduction.

[16]  L Ohno-Machado,et al.  Simplified risk score models accurately predict the risk of major in-hospital complications following percutaneous coronary intervention. , 2001, The American journal of cardiology.

[17]  E. Claus.  Risk models used to counsel women for breast and ovarian cancer: a guide for clinicians , 2004, Familial Cancer.

[18]  Lucila Ohno-Machado,et al.  Supratentorial low-grade glioma resectability: statistical predictive analysis based on anatomic MR features and tumor characteristics. , 2006, Radiology.

[19]  K S Berbaum,et al.  Multireader, multicase receiver operating characteristic methodology: a bootstrap analysis. , 1995, Academic radiology.

[20]  Lucila Ohno-Machado,et al.  Prediction of mortality in an Indian intensive care unit , 2004, Intensive Care Medicine.

[21]  W. Knaus,et al.  APACHE II: a severity of disease classification system. , 1985 .

[22]  Ewout W Steyerberg,et al.  Validation and updating of predictive logistic regression models: a study on sample size and shrinkage , 2004, Statistics in medicine.

[23]  Yudi Pawitan,et al.  False discovery rate, sensitivity and sample size for microarray studies , 2005, Bioinform..

[24]  Tianxi Cai,et al.  The Performance of Risk Prediction Models , 2008, Biometrical journal. Biometrische Zeitschrift.