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The disease similarity landscape explorer

Welcome to LanDis, the PaccanaroLab's disease similarity landscape explorer. LanDis allows you to search over 28 Million pairwise similarities between OMIM diseases, that we obtain performing a thorough ontological analysis of hereditable diseases. For more information on the method, click here. We have also implemented an Explorer designed to provide an intuitive way of visualising and navigating the complex landscape of hereditable diseases.

If you found our data useful, please cite:

  LanDis: The disease similarity landscape explorer

Horacio Caniza, Juan Cáceres, Mateo Torres, and Alberto Paccanaro
European Journal of Human Genetics (2024), (doi:10.1038/s41431-023-01511-9)

  A network medicine approach to quantify distance between hereditary disease modules on the interactome

Horacio Caniza, Alfonso E. Romero and Alberto Paccanaro
Scientific Reports 5, Article number: 17658 (2015) (doi:10.1038/srep17658)

An informal introduction to our disease similarity method

Large scale proteomics data has helped clarify the relationship between a disease phenotype and its causes, to a point where it is now clear that a disease is a wider perturbation in the underlying biological networks. Nevertheless, there is relatively little molecular information about the hereditable diseases described in OMIM To put this in perspective, 45% of the known hereditable diseases in OMIM have no known causal genes, and for the remaining ones, the information might not be complete.

On the other hand, there is an abundance of phenotype descriptions that, although not well suited for machine processing, provide comprehensive information on the various diseases. This information reflects the complexity of each disease's molecular foundations and thus quantifying similarity at this level would reflect the closeness at molecular level between two diseases. Accurately quantifying this molecular closeness could shine a light on the discovery of new disease genes and possibly help identify new targets for drugs.

By annotating the diseases in OMIM with the MeSH terms associated to the publications they reference, we are able to obtain high-quality annotations for the diseases. These annotations, in combination with the structure of the MeSH ontologies allows us to obtain a single number that characterises molecular similarity between the diseases with high accuracy.


We have developed a way to explore a diseases' "neighbourhood". Select the disease you want by typing its name or MIM number and click 'Explore'. You can test the explorer with the provided example by just clicking 'Explore'.


Type the name or MIM number of the diseases you want to compare and press 'Search' to obtain the results. If you want to test the explorer with the provided example, just click the 'Search' button.


Disease similarity (2023)

File format help

OMIM to MeSH mapping (2023)

File format help
The similarity file provided was copmuted using 2019 data. Data is provided in a .zip compressed file, nevertheless it is still 140 Mb. in size. Once decompressed, the list file shows the similarity between two elements by pairing them up one by one. Every line consists of three columns. In case you selected a genewise calculation:
  • Column 1 are MIM numbers
  • Column 2 are MIM numbers
  • Column 3 contains the value representing the similarity between the elements in column 1 and column 2
The OMIM to MeSH file is a tab separated file in which the first column is the OMIM disease identifier and the remaining columns are the MeSH terms unique identifiers assigned to it.


Developed by

This explorer was developed by Juan Cáceres, Horacio Caniza. and Mateo Torres

Source Code

Source code is available from this GitHub repository.


Any problems or bugs can be reported to horacio -at- upa.edu.py

Thanks to

LanDis benefited greatly from the help of Diego Galeano

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.