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A DIseAse MOdule Detection (DIAMOnD) algorithm derived from a systematic analysis of connectivity patterns of disease proteins in the human interactome.

Ghiassian SD, Menche J, Barabási AL - PLoS Comput. Biol. (2015)

Bottom Line: 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.We find that disease associated proteins do not reside within locally dense communities and instead identify connectivity significance as the most predictive quantity.We study the performance of the algorithm using well-controlled synthetic data and systematically validate the identified neighborhoods for a large corpus of diseases.

View Article: PubMed Central - PubMed

Affiliation: Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, Massachusetts, United States of America; Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

The DIAMOnD algorithm.(A) At each step of the iterative algorithm, the connectivity significance of all immediate neighbors of disease proteins is calculated. Next, the most significantly connected node (lowest p-value) is integrated into the module, thus expanding the module by one node per iteration step. (B) Subgraph of the Interactome highlighting the seed proteins for macular degeneration and the first 50 corresponding DIAMOnD proteins. In the beginning, two separate clusters grow independently until they merge at iteration step 50. Note that DIAMOnD also proposes proteins that do not have direct connections to seed proteins, e.g. at iteration steps 12 and 15. The squares mark seed proteins whose removal leads to large differences in the resulting DIAMOnD modules. The three leftmost squares, for example, enable the identification of a protein at iteration step 23, which in turn triggers the inclusion of the cluster of proteins depicted underneath, which would be absent otherwise.
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pcbi.1004120.g002: The DIAMOnD algorithm.(A) At each step of the iterative algorithm, the connectivity significance of all immediate neighbors of disease proteins is calculated. Next, the most significantly connected node (lowest p-value) is integrated into the module, thus expanding the module by one node per iteration step. (B) Subgraph of the Interactome highlighting the seed proteins for macular degeneration and the first 50 corresponding DIAMOnD proteins. In the beginning, two separate clusters grow independently until they merge at iteration step 50. Note that DIAMOnD also proposes proteins that do not have direct connections to seed proteins, e.g. at iteration steps 12 and 15. The squares mark seed proteins whose removal leads to large differences in the resulting DIAMOnD modules. The three leftmost squares, for example, enable the identification of a protein at iteration step 23, which in turn triggers the inclusion of the cluster of proteins depicted underneath, which would be absent otherwise.

Mentions: Building on the observation that the connectivity significance is highly distinctive for known disease proteins, we propose the following algorithm to infer yet unknown disease proteins (Fig. 2A), and hence to identify the respective disease module:


A DIseAse MOdule Detection (DIAMOnD) algorithm derived from a systematic analysis of connectivity patterns of disease proteins in the human interactome.

Ghiassian SD, Menche J, Barabási AL - PLoS Comput. Biol. (2015)

The DIAMOnD algorithm.(A) At each step of the iterative algorithm, the connectivity significance of all immediate neighbors of disease proteins is calculated. Next, the most significantly connected node (lowest p-value) is integrated into the module, thus expanding the module by one node per iteration step. (B) Subgraph of the Interactome highlighting the seed proteins for macular degeneration and the first 50 corresponding DIAMOnD proteins. In the beginning, two separate clusters grow independently until they merge at iteration step 50. Note that DIAMOnD also proposes proteins that do not have direct connections to seed proteins, e.g. at iteration steps 12 and 15. The squares mark seed proteins whose removal leads to large differences in the resulting DIAMOnD modules. The three leftmost squares, for example, enable the identification of a protein at iteration step 23, which in turn triggers the inclusion of the cluster of proteins depicted underneath, which would be absent otherwise.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4390154&req=5

pcbi.1004120.g002: The DIAMOnD algorithm.(A) At each step of the iterative algorithm, the connectivity significance of all immediate neighbors of disease proteins is calculated. Next, the most significantly connected node (lowest p-value) is integrated into the module, thus expanding the module by one node per iteration step. (B) Subgraph of the Interactome highlighting the seed proteins for macular degeneration and the first 50 corresponding DIAMOnD proteins. In the beginning, two separate clusters grow independently until they merge at iteration step 50. Note that DIAMOnD also proposes proteins that do not have direct connections to seed proteins, e.g. at iteration steps 12 and 15. The squares mark seed proteins whose removal leads to large differences in the resulting DIAMOnD modules. The three leftmost squares, for example, enable the identification of a protein at iteration step 23, which in turn triggers the inclusion of the cluster of proteins depicted underneath, which would be absent otherwise.
Mentions: Building on the observation that the connectivity significance is highly distinctive for known disease proteins, we propose the following algorithm to infer yet unknown disease proteins (Fig. 2A), and hence to identify the respective disease module:

Bottom Line: 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.We find that disease associated proteins do not reside within locally dense communities and instead identify connectivity significance as the most predictive quantity.We study the performance of the algorithm using well-controlled synthetic data and systematically validate the identified neighborhoods for a large corpus of diseases.

View Article: PubMed Central - PubMed

Affiliation: Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, Massachusetts, United States of America; Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus