R
Ron Milo
Researcher at Weizmann Institute of Science
Publications - 240
Citations - 41029
Ron Milo is an academic researcher from Weizmann Institute of Science. The author has contributed to research in topics: Multiple sclerosis & Medicine. The author has an hindex of 69, co-authored 215 publications receiving 32189 citations. Previous affiliations of Ron Milo include Technion – Israel Institute of Technology & Harvard University.
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Journal ArticleDOI
Network Motifs: Simple Building Blocks of Complex Networks
TL;DR: Network motifs, patterns of interconnections occurring in complex networks at numbers that are significantly higher than those in randomized networks, are defined and may define universal classes of networks.
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Revised Estimates for the Number of Human and Bacteria Cells in the Body.
Ron Sender,Shai Fuchs,Ron Milo +2 more
TL;DR: This analysis updates the widely-cited 10:1 ratio, showing that the number of bacteria in the body is actually of the same order as the numberof human cells, and their total mass is about 0.2 kg.
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Network motifs in the transcriptional regulation network of Escherichia coli
TL;DR: This work applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli, and finds that much of the network is composed of repeated appearances of three highly significant motifs.
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The biomass distribution on Earth
TL;DR: The overall biomass composition of the biosphere is assembled, establishing a census of the ≈550 gigatons of carbon (Gt C) of biomass distributed among all of the kingdoms of life and shows that terrestrial biomass is about two orders of magnitude higher than marine biomass and estimate a total of ≈6 Gt C of marine biota, doubling the previous estimated quantity.
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Superfamilies of evolved and designed networks.
Ron Milo,Shalev Itzkovitz,Nadav Kashtan,Reuven Levitt,Shai S. Shen-Orr,Inbal Ayzenshtat,Michal Sheffer,Uri Alon +7 more
TL;DR: An approach to systematically study similarity in the local structure of networks, based on the significance profile (SP) of small subgraphs in the network compared to randomized networks, finds several superfamilies of previously unrelated networks with very similar SPs.