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Noël Malod-Dognin

Researcher at University College London

Publications -  52
Citations -  2430

Noël Malod-Dognin is an academic researcher from University College London. The author has contributed to research in topics: Medicine & Gene. The author has an hindex of 20, co-authored 44 publications receiving 1959 citations. Previous affiliations of Noël Malod-Dognin include Centrum Wiskunde & Informatica & University of California, Irvine.

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A global genetic interaction network maps a wiring diagram of cellular function

TL;DR: A global genetic interaction network highlights the functional organization of a cell and provides a resource for predicting gene and pathway function and how coherent sets of negative or positive genetic interactions connect protein complex and pathways to map a functional wiring diagram of the cell.
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Revealing the Hidden Language of Complex Networks

TL;DR: This work discovers that the interaction between a small number of roles, played by nodes in a network, can characterize a network's structure and also provide a clear real-world interpretation, and develops a framework for analysing and comparing networks, which outperforms all existing ones.
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Integrative methods for analyzing big data in precision medicine

TL;DR: This work outlines key challenges in precision medicine and present recent advances in data integration‐based methods to uncover personalized information from big data produced by various omics studies.
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L-GRAAL: Lagrangian graphlet-based network aligner.

TL;DR: L-GRAAL's results are the first to show that topological information is more important than sequence information for uncovering functionally conserved interactions, and is compared with the state-of-the-art network aligners on the largest available PPI networks from BioGRID.
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Graphlet-based Characterization of Directed Networks.

TL;DR: The canonical correlation analysis framework is extended, allowing domain-specific interpretation of a directed network’s topology, and insights into preservation of enzyme function from the network wiring patterns rather than from sequence data are yielded.