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Albert-László Barabási
Researcher at Northeastern University
Publications - 463
Citations - 217721
Albert-László Barabási is an academic researcher from Northeastern University. The author has contributed to research in topics: Complex network & Network science. The author has an hindex of 152, co-authored 438 publications receiving 200119 citations. Previous affiliations of Albert-László Barabási include Budapest University of Technology and Economics & Lawrence Livermore National Laboratory.
Papers
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Journal ArticleDOI
Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols
Italo Faria do Valle,Harvey G. Roweth,Harvey G. Roweth,Michael W. Malloy,Michael W. Malloy,Sofia Moco,Denis Barron,Elisabeth M. Battinelli,Elisabeth M. Battinelli,Joseph Loscalzo,Joseph Loscalzo,Albert-László Barabási,Albert-László Barabási,Albert-László Barabási +13 more
TL;DR: A network medicine framework to uncover mechanisms for the effects of polyphenols on health by considering the molecular interactions between polyphenol protein targets and proteins associated with diseases is developed and experimentally confirms that rosmarinic acid inhibits platelet aggregation and α-granule secretion through inhibition of protein tyrosine phosphorylation.
Journal ArticleDOI
Control of fluxes in metabolic networks
TL;DR: An efficient computational framework is developed which helps gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism.
Book ChapterDOI
Power Laws in Biological Networks
TL;DR: The properties of biological networks are discussed, discussing their scale-free and hierarchical features, and the cellular utilization of the metabolic network is dominated by “hot-spots”, rep-resenting connected high-flux pathways.
Journal ArticleDOI
Success in books: a big data approach to bestsellers
TL;DR: There is a universal pattern to book sales and a statistical model is introduced to explain the time evolution of sales that reproduces the entire sales trajectory of a book but also predicts the total number of copies it will sell in its lifetime, based on its early sales numbers.