E
Enrico Maiorino
Researcher at Brigham and Women's Hospital
Publications - 37
Citations - 1450
Enrico Maiorino is an academic researcher from Brigham and Women's Hospital. The author has contributed to research in topics: Multifractal system & Recurrent neural network. The author has an hindex of 13, co-authored 33 publications receiving 1149 citations. Previous affiliations of Enrico Maiorino include Northeastern University & Sapienza University of Rome.
Papers
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Journal ArticleDOI
Discovering the genes mediating the interactions between chronic respiratory diseases in the human interactome.
Enrico Maiorino,Enrico Maiorino,Seung Han Baek,Feng Guo,Xiaobo Zhou,Parul H. Kothari,Edwin K. Silverman,Albert-László Barabási,Albert-László Barabási,Scott T. Weiss,Benjamin A. Raby,Amitabh Sharma +11 more
TL;DR: In this article, a Flow Centrality (FC) based approach was proposed to identify the genes mediating the interaction between two diseases in a protein-protein interaction network, focusing on asthma and COPD.
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A generative model for protein contact networks
TL;DR: In this paper, a generative model for protein contact networks (PCNs) is proposed by focusing primarily on mesoscopic properties elaborated from the spectra of the graph Laplacian.
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A Novel Neural Networks Ensemble Approach for Modeling Electrochemical Cells
Massimiliano Luzi,Maurizio Paschero,Antonello Rizzi,Enrico Maiorino,Fabio Massimo Frattale Mascioli +4 more
TL;DR: A novel neural networks ensemble approach for modeling electrochemical cells, achieving very promising performances in both the system identification accuracy and the SoC estimation task.
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Single Cell Transcriptomic Analysis Reveals Organ Specific Pericyte Markers and Identities
TL;DR: Specific pericyte markers are identified among lung, heart, kidney, and bladder and differentially expressed genes and functional relationships between mural cells are revealed and are found to be conserved in human heart and lung pericytes.
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An Agent-Based Algorithm exploiting Multiple Local Dissimilarities for Clusters Mining and Knowledge Discovery
TL;DR: A multi-agent algorithm able to automatically discover relevant regularities in a given dataset, determining at the same time the set of configurations of the adopted parametric dissimilarity measure that yield compact and separated clusters is proposed.