Not Logged In

Vega, Roberto

Name: Vega, Roberto
Email:
Organization:  
Webpage: none
Interest(s):  
Publications:  
1. R. Vega, R. Greiner. "Finding Effective Ways to (Machine) Learn fMRI-based Classifiers from Multi-Site Data". Machine Learning in Clinical Neuroimaging (MLCN), pp 10, September 2018. PDFview
2. S. Liang, R. Vega, X. Kong, W. Deng, Q. Wang, M. Li, X. Hu, A. Greenshaw, R. Greiner, T. Li. "Neurocognitive Graphs of First-Episode Schizophrenia and Major Depression Based on Cognitive Features". Neuroscience Bulletin, 34(2), pp 312-320, April 2018. view
3. F. Seyednasrollah, D. Koestler, T. Wang, S. Piccolo, R. Vega, R. Greiner, C. Fuchs, E. Gofer, L. Kumar. "A DREAM Challenge to build prediction models for short-term discontinuation of docetaxel in metastatic castration-resistant prostate cancer". Journal of Clinical Oncology: Clinical Cancer Informatics, July 2017. view
4. J. Guinney, T. Wang, P. DREAM Community, R. Greiner, R. Vega, L. Kumar, J. Patel. "Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowd-sourced challenge with open clinical trial data.". Lancet Oncology, 18(1), pp 132-142, January 2017. view
5. R. Vega. "The challenge of applying machine learning techniques to diagnose schizophrenia using multi-site fMRI data". MSc Thesis, University of Alberta, January 2017. PDFview
6. R. Vega, T. Sajed, K. Mathewson, K. Khare, P. Pilarski, R. Greiner, G. Sanchez-Ante, J. Antelis. "Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals". Artificial Intelligence Research, 6(1), September 2016. view

Author List
University of Alberta Logo AICML Logo