Selected Publications

CS Education Related

1.      Amir Rubinstein, Noam Parzanchevski, and Yossi Tamarov. 2019. In-Depth Feedback on Programming Assignments Using Pattern Recognition and Real-Time Hints. In Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE '19). ACM, New York, NY, USA, 243-244. DOI: https://doi.org/10.1145/3304221.3325552

2.      Amir Rubinstein and Benny Chor (2014): Computational Thinking in Life Science Education. PLoS Comput Biol 10(11): e1003897. doi:10.1371/journal.pcbi.1003897

3.      Amir Rubinstein (2014), Computer Science for Non-Technological Cyber Programs, Frontier in Education (FIE) Conference, Madrid

4.      Orna Miller and Amir Rubinstein (2011): Work in progress: Courses dedicated to the development of logical and algorithmic thinking, 41st ASEE/IEEE Frontiers in Education Conference, 2011, Rapid City, SD

 

Computational Biology Related

1.      Hadas E. Sloin, Gennaro Ruggiero, Amir Rubinstein, Sima Smadja Storz, Nicholas S. Foulkes, Yoav Gothilf (2018): Interactions between the circadian clock and TGF-β signaling pathway in zebrafish. PLoS One, 13(6), e0199777.

2.      Yeheskel, Adva, Adam Reiter, Metsada Pasmanik-Chor, and Amir Rubinstein (2018): "Simulation and visualization of multiple KEGG pathways using BioNSi." F1000Research 6.

3.      Amir Rubinstein and Yona Kassir (2017): A Computational Approach to Study Gene Expression Networks. Meiosis: 325-334.

4.      Amir Rubinstein, Noga Bracha, Liat Rudner, Noga Zucker, Hadas E. Sloin and Benny Chor (2016): BioNSi: A Discrete Biological Network Simulator Tool. J. Proteome Res.

5.      Amir Rubinstein, Ofir Hazan, Benny Chor, Ron Y. Pinter and Yona Kassir (2013): The effective application of a discrete transition model to explore cell-cycle regulation in yeast. BMC Research Notes 6:311.

6.      Amir Rubinstein, Vyacheslav Gurevich, Zohar Kasulin-Boneh, Lilach Pnueli, Yona Kassir, and Ron Y. Pinter (2007): Faithful Modeling of Transient Expression and Its Application to Elucidating Negative Feedback Regulation, Proceedings of the National Academy of Sciences (PNAS), Vol. 104, No. 15, pp. 6241-6246.

 

PATENT

1.      Moshkovich, Dany, and Amir Rubinstein (2014): Semantic intensity based decomposition of software systems. U.S. Patent No. 8,769,515. 1 Jul. 2014.

 

 

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