Statnikov, Alexander - Center for Health Informatics and Bioinformatics, New York University

Advanced Search

Statnikov, Alexander - Center for Health Informatics and Bioinformatics, New York University

Algorithms for Large Scale Markov Blanket Discovery Ioannis Tsamardinos, Constantin F. Aliferis, Alexander Statnikov
NUMERICAL METHODS FOR IMAGE RECONSTRUCTION FOR THE CALIBRATION OF THE NASA-GLENN ICING RESEARCH WIND
An Algorithm For Generation of Large Bayesian Networks A. R. Statnikov, I. Tsamardinos, C.F. Aliferis
HITON: A Novel Markov Blanket Algorithm for Optimal Variable Selection C.F. Aliferis M.D., Ph.D., I. Tsamardinos Ph.D., A. Statnikov M.S.
JMLR: Workshop and Conference Proceedings 3: 1-33 WCCI2008 workshop on causality Design and Analysis of the
Last updated -10/22/2010 Alexander Statnikov
Using the GEMS System for Cancer Diagnosis and Biomarker Discovery from Microarray Gene Expression Data
As technology development in the biological sciences continues to rap-
Identifying Markov Blankets with Decision Tree Induction Lewis Frey1
Time and Sample Efficient Discovery of Markov Blankets and Direct Causal Relations
LARGE-SCALE FEATURE SELECTION USING MARKOV BLANKET INDUCTION FOR THE PREDICTION OF PROTEIN-DRUG BINDING
Methods for Multi-Category Cancer Diagnosis from Gene Expression Data: A Comprehensive Evaluation to Inform Decision Support System Development
Causal Explorer: A Causal Probabilistic Network Learning Toolkit for Biomedical Discovery
Causal Explorer: A Matlab Library of Algorithms for Causal Discovery and Variable Selection for Classification
Are Random Forests Better than Support Vector Machines for Microarray-Based Cancer Classification?
Using SVM Weight-Based Methods to Identify Causally Relevant and Non-Causally Relevant Variables
Generating Realistic Large Bayesian Networks by Tiling Ioannis Tsamardinos Alexander Statnikov Laura E. Brown Constantin F. Aliferis
Extracting Drug-Drug Interaction Articles from MEDLINE to Improve the Content of Drug Databases
Machine Learning Models For Classification Of Lung Cancer and Selection of Genomic Markers Using Array Gene Expression Data
Why Classification Models Using Array Gene Expression Data Perform So Well: A Preliminary
Using the GEMS System for Supervised Analysis of Cancer Microarray Gene Expression Data
Using GEMS for Cancer Diagnosis and Biomarker Discovery from Microarray Gene Expression Data Alexander Statnikov
Applying Decision Support Models in the Presence of Incomplete Evidence Alexander Statnikov, Eva Kasparova, Constantin F. Aliferis
Scaling-Up Bayesian Network Learning to Thousands of Variables Using Local Learning Techniques
AUTOMATIC CANCER DIAGNOSTIC DECISION SUPPORT SYSTEM FOR GENE EXPRESSION DOMAIN