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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
Visualizing novel connections and genetic similarities across diseases using a network-medicine based approach
B. Ferolito,Italo Faria do Valle,Hanna Gerlovin,Lauren Costa,Juan P. Casas,J. Michael Gaziano,David R. Gagnon,Edmon Begoli,Albert-László Barabási,Kelly Cho +9 more
TL;DR: In this paper , a network-based approach was used to evaluate shared variants among thousands of traits in the GWAS catalog repository and showed that the network can reveal clusters of diseases mechanistically related.
Journal Article
Bimodality in Network Control
Posted ContentDOI
FoodMine: Exploring Food Contents in Scientific Literature
Forrest Hooton,Giulia Menichetti,Albert-László Barabási,Albert-László Barabási,Albert-László Barabási +4 more
TL;DR: FoodMine is built, an algorithm that uses natural language processing to identify papers from PubMed that potentially report on the chemical composition of garlic and cocoa, finding that the chemicals identified tend to have direct health relevance, reflecting the scientific community’s focus on health-related chemicals in the authors' food.
Journal ArticleDOI
Improving the generalizability of protein-ligand binding predictions with AI-Bind
Robin Walters,Zohair Shafi,Omair Ahmed,Michael Sebek,Deisy Morselli Gysi,Rose Yu,Tina Eliassi-Rad,Albert-László Barabási,Giulia Menichetti +8 more
TL;DR: AI-Bind as discussed by the authors is a pipeline that combines network-based sampling strategies with unsupervised pre-training to improve binding predictions for novel proteins and ligands, which is a high-throughput approach to identify drug-target combinations with the potential of becoming a powerful tool in drug discovery.