Natan Nagar
Tel Aviv University
7 Papers
19 Citations
Natan Nagar is an academic researcher from Tel Aviv University. The author has contributed to research in topics: Medicine & Proteome. The author has an hindex of 2, co-authored 4 publications.
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Papers
COVID-19 pandemic-related lockdown: response time is more important than its strictness.
Gil Loewenthal,Shiran Abadi,Oren Avram,Keren Halabi,Noa Ecker,Natan Nagar,Itay Mayrose,Tal Pupko +7 more
TL;DR: A data‐driven model enabled the extraction of lockdown characteristics which were crossed with observed mortality rates to show that the time at which social distancing was initiated is highly correlated with the number of deaths and the immediate response has a prolonged effect on COVID‐19 death toll.
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Harnessing Machine Learning To Unravel Protein Degradation in Escherichia coli.
Natan Nagar,Noa Ecker,Gil Loewenthal,Oren Avram,Daniella Ben-Meir,Dvora Biran,Eliora Z. Ron,Tal Pupko +7 more
- 02 Feb 2021
TL;DR: In this paper, the authors used a combined computational-experimental approach to explore protein degradation in E. coli and showed that fast-degrading proteins can be identified using a combination of various protein properties, including structural, physicochemical, and protein-protein interaction network descriptors.
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Harnessing machine learning to unravel protein degradation in Escherichia coli
Tal Pupko,Natan Nagar,Noa Ecker,Gil Loewenthal,Oren Avram,Daniella Ben-Meir,Dvora Biran,Eliora Z. Ron +7 more
TL;DR: A quantitative pulsed-SILAC method followed by mass spectrometry analysis is employed to determine the half-lives for the proteome of exponentially growing Escherichia coli, and dozens of novel proteins that are fast-degrading are identified.
EvoRator: Prediction of Residue-level Evolutionary Rates from Protein Structures Using Machine Learning.
TL;DR: EvoRator as mentioned in this paper is a web server that implements a machine-learning regression algorithm to predict site-specific evolutionary rates from protein structures, which can be used to detect sites that are likely conserved due to functional constraints.
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The evolutionary dynamics that retain long neutral genomic sequences in face of indel deletion bias: a model and its application to human introns
TL;DR: The models developed develop a set of increasingly complex models of indel-dynamics that incorporate border-induced selection and show that short conserved sequences within the neutrally evolving sequence help explain: the presence of very long sequences; the high variance of sequence lengths; and the possible emergence of multimodality in sequence length distributions.
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