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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.
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Journal ArticleDOI
MilkyBase, a database of human milk composition as a function of maternal-, infant- and measurement conditions
TL;DR: The MilkyBase database as discussed by the authors is an Excel workbook of linked sheets, making it easy to input data by non-computationally minded nutritionists, which can be used to find patterns in the milk composition as a function of maternal, infant, and measurement conditions.
Journal ArticleDOI
Maximizing brain networks engagement via individualized connectome-wide target search
Arianna Menardi,Davide Momi,Antonino Vallesi,Albert-László Barabási,Emma K. Towlson,Emiliano Santarnecchi +5 more
TL;DR: In this article , a target search algorithm leveraging on mathematical tools from Network Control Theory (NCT) and whole brain connectomics analysis was implemented to identify the optimal stimulation target(s)- at the individual brain level- capable of reaching maximal engagement of the stimulated networks' nodes.
Posted ContentDOI
Network Medicine Framework Shows Proximity of Polyphenol Targets and Disease Proteins is Predictive of the 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: In this paper, the authors developed a network medicine framework to uncover the mechanistic roles of polyphenols on health by considering the molecular interactions between polyphenol protein targets and proteins associated with diseases.
Journal ArticleDOI
Machine learning prediction of the degree of food processing
TL;DR: In this article , a machine learning algorithm was used to accurately predict the degree of processing for any food, indicating that over 73% of the US food supply is ultra-processed, and that the increased reliance of an individual's diet on ultraprocessed food correlates with higher risk of metabolic syndrome, diabetes, angina, elevated blood pressure and biological age.
Book ChapterDOI
Anomalous Surface Roughening: Experiment and Models
Shlomo Havlin,Shlomo Havlin,Albert-László Barabási,Sergey V. Buldyrev,Chung-Kang Peng,M. Schwartz,M. Schwartz,H. E. Stanley,Tamás Vicsek +8 more
TL;DR: In this paper, the authors study the probability distribution of the height fluctuations in d = 1 + 1 by mapping the surface to a Levy walk and suggest a model based on propagation and pinning of a selfafflne interface in the presence of quenched disorder, with erosion of overhangs.