JCT - Faculty Research Profile - 2017-2021

98 Dr. Amitay Moshe Cell phone: 0528-974390 Email: mamitay@g.jct.ac.il Google scholar: https://scholar.google.co.il/citations?user=X1-hnV4AAAAJ&hl=iw  Machine learning and data science in health.  Next generation sequencing bioinformatics analysis. Peer-Reviewed Papers in Refereed Journals 1. Moshe Amitay and Moshe Goldstein. Evaluating the Peptide Structure Prediction Capabilities of a Purely Ab-Initio Method, Protein Engineering, Design and Selection (PEDS). 2017, 30, 723–727. Q1, impact factor 2.043. 2. Vanessa R. Pegos, Louis Hey, Jacob LaMirande, Rachel Pfeffer, Rosalie Lipsh, Moshe Amitay, Daniel Gonzalez and Mikael Elias Purification, characterization, crystallization and S-SAD phasing of the Phosphate-Binding Protein from Polaromonas, Acta Crystallographica F. 2017, 73, 342-346. Q3, impact factor 0.99. 3. G. Adamker, T. Holzer, I. Karakis, M. Amitay, E. Anis, S. R. Singer and Z. BarnettItzhaki Prediction of Shigellosis outcomes in Israel using machine learning classifiers, Epidemiology and Hygiene, 2018;146(11):1445-1451. Q2, impact factor 2.075. 4. Malka Aker, Shirly Ohanona, Shira Fisher, Efrat Katsman, Shirit Dvorkin, Efrat Kopelowitz, Moshe Goldstein, Zohar Barnett-Itzhaki and Moshe Amitay. CDB—a database for protein heterodimeric complexes. Protein Engineering, Design and Selection, 2018. 31, 10, 361. Q1, impact factor 2.043. 5. Branislav Kordic, Marko Popovic, Miroslav Popovic, Moshe Goldstein, Moshe Amitay, David Dayan, A Protein Structure Prediction Program Architecture Based on a Software Transactional Memory. ECBS 2019: 1:1-1:9,. Impact factor: 0.87. 6. Ilan Levy, Isabella Karakis, Tamar Berman, Moshe Amitay, Zohar Barnett-Itzhaki, A hybrid model for evaluating exposure of the general population in Israel to air pollutants Environmental Monitoring and Assessment, 2020, 192,4. Q2, impact factor: 1.96. 7. Z. Barnett-Itzhaki, M. Elbaz, R. Butterman, D. Amar, M. Amitay, C. Racowsky, R. Orvieto, R. Hauser, A. Baccarelli, R. Machtinger Machine learning vs. classic statistics for the prediction of IVF outcomes, Journal of Assisted Reproduction and Genetics, 2020, 37, 2405–2 Q1, Impact factor: 2.95.

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