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Dennis Shasha

Affiliations: 
New York University, New York, NY, United States 
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"Dennis Shasha"
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Courant Institute of Mathematical Sciences, New York University, New York, USA

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Parents

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Children

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David A. Tanzer grad student 2001 NYU
Yunyue Zhu grad student 2004 NYU
Tyler Neylon grad student 2006 NYU
Aristotelis Tsirigos grad student 2006 NYU
Zhihua Wang grad student 2006 NYU
Xiaojian Zhao grad student 2006 NYU
Huang-Wen Chen grad student 2010 NYU
Christopher S. Poultney grad student 2010 NYU
Eric Hielscher grad student 2013 NYU
Alexander Rubinsteyn grad student 2014 NYU
Noah Youngs grad student 2014 NYU

Collaborators

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Gloria M. Coruzzi collaborator NYU Courant (Plant Biology Tree)
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Publications

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Dussarrat T, Nilo-Poyanco R, Moyano TC, et al. (2024) Phylogenetically diverse wild plant species use common biochemical strategies to thrive in the Atacama Desert. Journal of Experimental Botany
Shen B, Coruzzi G, Shasha D. (2023) EnsInfer: a simple ensemble approach to network inference outperforms any single method. Bmc Bioinformatics. 24: 114
Cirrone J, Brooks MD, Bonneau R, et al. (2020) Author Correction: OutPredict: multiple datasets can improve prediction of expression and inference of causality. Scientific Reports. 10: 14141
Cirrone J, Brooks MD, Bonneau R, et al. (2020) OutPredict: multiple datasets can improve prediction of expression and inference of causality. Scientific Reports. 10: 6804
Brooks MD, Cirrone J, Pasquino AV, et al. (2019) Network Walking charts transcriptional dynamics of nitrogen signaling by integrating validated and predicted genome-wide interactions. Nature Communications. 10: 1569
Varala K, Marshall-Colón A, Cirrone J, et al. (2018) Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants. Proceedings of the National Academy of Sciences of the United States of America
Servajean M, Joly A, Shasha D, et al. (2017) Crowdsourcing Thousands of Specialized Labels: A Bayesian Active Training Approach Ieee Transactions On Multimedia. 19: 1376-1391
Micale G, Giugno R, Ferro A, et al. (2017) Fast analytical methods for finding significant labeled graph motifs Data Mining and Knowledge Discovery. 32: 504-531
Jiang Y, Oron TR, Clark WT, et al. (2016) An expanded evaluation of protein function prediction methods shows an improvement in accuracy. Genome Biology. 17: 184
Youngs N, Shasha D, Bonneau R. (2016) Positive-Unlabeled Learning in the Face of Labeling Bias Proceedings - 15th Ieee International Conference On Data Mining Workshop, Icdmw 2015. 639-645
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