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Analysis of the Symptoms-Disease Network database using communities.

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made-with-python MIT Licence Ask Me Anything ! Open Source Love Tweet

Project Symptoms-Disease Network

Overview

Analysis of the Symptoms-Disease Network database using communities.

Usage

The complete code of this project is in this repository, in the file: symptoms-disease-network.ipynb.

If the notebook does not load or takes a long time to load, please refresh the page.

Approach

The steps developed in this project were as follows:

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Dataset

The dataset was extracted from the paper: "Human symptoms–disease network" Link.

More exactly, here we employ the "Supplementary Data 3": Term co-occurrences between symptoms and diseases measured by TF-IDF weighted values. This table includes 147,978 records of symptom and disease relationships.

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All Networks: bipartite graph and their projections

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Communities of Diseases Network using Louvain

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Bipartite graphs of each community by symptoms and diseases

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Table of the characterization of each community by symptoms

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Predict the disease category based on symptoms

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References

  • Zhou, X., Menche, J., Barabási, AL. et al. Human symptoms–disease network. Nat Commun 5, 4212 (2014). https://doi.org/10.1038/ncomms5212
  • Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. doi:10.1088/1742-5468/2008/10/p10008

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