The joint analysis of shared genes and symptoms on a multi-layered disease network – consisting of two layers representing gene and symptom relationships – uncovers an alternative grouping of diseases.

On a global scale, diseases that shared genes also tended to share symptoms. An algorithm specifically designed to identify patterns in multi-layered networks determined groups of diseases that were highly similar to each other both genetically and symptomatically.

This approach holds the potential to transcend today’s clinical observation-based disease classification systems. It may pave the way for a molecular-based disease classification, the discovery of novel disease relationships, and ultimately personalized diagnosis and treatment.

Figure: The multiplex disease network. (A) Tripartite network of symptoms (green nodes on the left), diseases (pink nodes in the middle) and genes (blue nodes on the right). Symptoms and genes that are shared between diseases are shown in darker text. (B) Phenotype- and genotype-based disease-disease networks where diseases are connected in the genotype layer (blue) if they share at least one gene and connected in the phenotype layer (green) if they share at least one symptom. The thickness of the edge is proportional to the number of common genes or symptoms. (C) The two networks are considered as layers of a multiplex system, where nodes are the diseases and colored links encode their interactions. Disease-disease interactions that are present in both layers are denoted “overlapping links.”

Click here to read the peer-reviewed article online

Two data sets are released, corresponding to network reconstruction based on GWAS (Genome-wide Association Study) and OMIM (Online Mendelian Inheritance in Man) catalogs, respectively.

In both cases, the format is as follows:

disorder;disorder_OMIM;disorder_cat;symptom;symptom_cat;gene_symb;gene_OMIM;gene_cytoloc

where:

• disorder: Disorder name
• disorder_OMIM: OMIM identifier of the disorder
• disorder_cat: Disorder category
• symptom: Symptom name
• symptom_cat: Symptom category
• gene_symb: Gene symbol(s)
• gene_OMIM: OMIM identifier of the gene
• gene_cytoloc: Cytogenetic location

The above information is sufficient to build the bipartite networks representing Disease-Symptom interactions and Disease-Gene interactions. Each bipartite network can be easily projected on its Disease component, to create two Disease-Disease interaction networks, encoding Phenotype and Genotype information.

More specifically:

• R users: with igraph use the bipartite_projection function
• Python users: with networkx use the projected_graph function

File: DSG_network_from_GWAS.csv

File: DSG_network_from_OMIM.csv

If you use this data sets for your research, please cite the following article:

A. Halu, M. De Domenico, A. Arenas & A. Sharma, The multiplex network of human diseases, NPJ Systems Biology and Applications 5, 15 (2019)

Click here to read the article online

MultiplexDiseasome is made available under the Open Database License: http:https://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http:https://opendatacommons.org/licenses/dbcl/1.0/

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Manlio De Domenico
Center for Information and Communication Technology
Fondazione Bruno Kessler, Italy

Email: [email protected]
Web: https://comunelab.fbk.eu/