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Publications in 2016

In Journal (refereed)

1. A. Narasimhan, S. Ghosh, C. Stretch, R. Greiner, O. Bathe, V. Baracos, S. Damaraju. "Small RNAome profiling from human skeletal muscle: Novel miRNAs and their targets associated with cancer cachexia ". Journal of Cachexia, Sarcopenia and Muscle, December 2016. view
2. Y. Djoumbou Feunang, R. Eisner, C. Knox, L. Chepelev, J. Hastings, G. Owen, E. Fahy, C. Steinbeck, S. Subramanian, E. Bolton, R. Greiner, D. Wishart. "ClassyFire: automated chemical classification with a comprehensive, computable taxonomy". Journal Of Cheminformatics, November 2016. view
3. S. Ghiassian, P. Jin, M. Brown, R. Greiner. "Using Functional or Structural Magnetic Resonance Images and Personal Characteristic Data to Identify ADHD and Autism". PLoS One, November 2016. view
4. 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
5. K. Aggarwal, A. Hindle, F. Timbers, E. Stroulia, T. Rutgers, R. Greiner. "Detecting Duplicate Bug Reports using a Hierarchy of Domain Knowledge Contexts". Journal of Software: Evolution and Process, August 2016. view
6. R. Ramasubbu, M. Brown, F. Cortese, I. Gaxiola, A. Greenshaw, S. Dursun, B. Goodyear, R. Greiner. "Accuracy of Automated Classification of Major Depressive Disorder as a Function of Symptom Severity". NeuroImage: Clinical, July 2016. view
7. F. Allen, A. Pon, R. Greiner, D. Wishart. "Computational prediction of electron ionization mass spectra to assist in GC-MS compound identification". Analytical Chemistry, July 2016. view
8. F. Allen, R. Greiner, D. Wishart. "CFM-ID applied to CASMI 2014". Current Metabolomics, June 2016. view

In Conference (refereed)

9. S. Ravanbakhsh, B. Poczos, R. Greiner. "Boolean matrix factorization and noisy completion via message passing". International Conference on Machine Learning (ICML), June 2016. view
10. S. Ravanbakhsh, B. Poczos, J. Schneider, D. Schuurmans, R. Greiner. "Stochastic Neural Networks with Monotonic Activation Functions". Artificial Intelligence and Statistics, May 2016. view

In Workshop

11. J. Wen, N. Hassanpour, R. Greiner. "Weighted Gaussian Process for Estimating Treatment Effect". December 2016. view
12. D. Chamot, L. Deng, R. Mandal, T. Bjorndahl, S. Ravanbakhsh, J. Grant, M. Wilson, B. Han, A. Serra-Cayuela, E. Dong, R. Greiner, D. Wishart. "Automated kits and software for quantitative metabolomics". Metabolomics Symposium, June 2016. view
13. D. Wishart, Y. Djoumbou Feunang, C. Knox, T. Sajed, F. Allen, Y. Liu, Z. Shi, Z. Budinski, R. Greiner. "New Bioinformatic Tools for Metabolomics". Metabolomics Symposium, June 2016. view

Other Categories

14. L. Kumar. "Survival Prediction using Gene Expression Data - A Topic Modeling Approach". MSc Thesis, Computing Science, Thesis, December 2016. PDFview
15. F. Allen. "Competitive Fragmentation Modeling of Mass Spectra for Metabolite Identification". PhD Thesis, January 2016. PDFview
16. B. Sen. "Generalized Prediction Model for Detection of Psychiatric Disorders". MSc Thesis, University of Alberta, January 2016. PDFview
17. Z. Shi. "Learning to predict the sites of metabolism and metabolic endpoints". MSc Thesis, University of Alberta, January 2016. PDFview
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