A DIseAse MOdule Detection (DIAMOnD) Algorithm Derived from a Systematic Analysis of Connectivity Patterns of Disease Proteins in the Human Interactome

By Susan Dina Ghiassian, Jörg Menche, Albert-László Barabási. PLoS Comput. Biol., 2015

371 citations recorded by the source corpus.

The observation that disease associated proteins often interact with each other has fueled the development of network-based approaches to elucidate the molecular mechanisms of human disease. Such approaches build on the assumption that protein interaction networks can be viewed as maps in which diseases can be identified with localized perturbation within a certain neighborhood. The identification of these neighborhoods, or disease modules, is therefore a prerequisite of a detailed investigation of a particular pathophenotype. While numerous heuristic methods exist that successfully pinpoint disease associated modules, the basic underlying connectivity patterns remain largely unexplored. In this work we aim to fill this gap by analyzing the network properties of a comprehensive corpus of 70 complex diseases. We find that disease associated proteins do not reside within locally dense communities and instead identify connectivity significance as the most predictive quantity. This quantity inspires the design of a novel Disease Module Detection (DIAMOnD) algorithm to identify the full disease module around a set of known disease proteins. We study the performance of the algorithm using well-controlled synthetic data and systematically validate the identified neighborhoods for a large corpus of diseases.

Subfields: Computational Theory and Mathematics, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/ac4cd45817025cd41872b494d59d39f0f0bd5562

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Citations in the corpus

Artificial intelligence in cancer target identification and drug discovery (2022, methodology): “For example, Ghiassian et al. 120 used the DIseAse MOdule Detection (DIAMOnD) method 121 to identify the local modules within the interconnected map of molecular components.”
Large-Scale Discovery of Disease-Disease and Disease-Gene Associations (2016, background): “It should be noted that there are other ways to generate interactome networks of human diseases 5,27,28,51,52 , however, these are not easily applicable for a general disease phenotyping task this study addresses, and as such are not included.”