M
Mauricio Barahona
Researcher at Imperial College London
Publications - 266
Citations - 11931
Mauricio Barahona is an academic researcher from Imperial College London. The author has contributed to research in topics: Complex network & Computer science. The author has an hindex of 44, co-authored 252 publications receiving 10076 citations. Previous affiliations of Mauricio Barahona include California Institute of Technology & Massachusetts Institute of Technology.
Papers
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Journal ArticleDOI
Synchronization in small-world systems.
TL;DR: Applied to networks of low redundancy, the small-world route produces synchronizability more efficiently than standard deterministic graphs, purely random graphs, and ideal constructive schemes.
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SC3: consensus clustering of single-cell RNA-seq data
Vladimir Yu. Kiselev,Kristina Kirschner,Michael T. Schaub,Michael T. Schaub,Tallulah S. Andrews,Andrew Yiu,Tamir Chandra,Tamir Chandra,Kedar Nath Natarajan,Kedar Nath Natarajan,Wolf Reik,Wolf Reik,Wolf Reik,Mauricio Barahona,Anthony R. Green,Martin Hemberg +15 more
TL;DR: It is demonstrated that SC3 is capable of identifying subclones from the transcriptomes of neoplastic cells collected from patients and achieves high accuracy and robustness by combining multiple clustering solutions through a consensus approach.
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Transcriptome-wide noise controls lineage choice in mammalian progenitor cells
Hannah H. Chang,Martin Hemberg,Martin Hemberg,Mauricio Barahona,Donald E. Ingber,Sui Huang,Sui Huang +6 more
TL;DR: Clonal heterogeneity of gene expression level is not due to independent noise in the expression of individual genes, but reflects metastable states of a slowly fluctuating transcriptome that is distinct in individual cells and may govern the reversible, stochastic priming of multipotent progenitor cells in cell fate decision.
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Stability of graph communities across time scales.
TL;DR: In this paper, the authors introduce the stability of a partition, a measure of its quality as a community structure based on the clustered autocovariance of a dynamic Markov process taking place on the network.
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Laplacian Dynamics and Multiscale Modular Structure in Networks
TL;DR: In this article, the stability of a network partition is defined in terms of the statistical properties of a dy namical process taking place on the graph, and the connection between community detection and Laplacian dynamics enables them to establish dynamically motivated stability measures linked to distinct null models.