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

Horacio Caniza; Alfonso E Romero; Alberto Paccanaro

doi:10.1038/srep17658

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

Horacio Caniza , Alfonso E Romero , Alberto Paccanaro

Research output: Contribution to journal › Article › peer-review

120 Downloads (Pure)

Abstract

We introduce a MeSH-based method that accurately quantifies similarity between heritable diseases at molecular level. This method effectively brings together the existing information about diseases that is scattered across the vast corpus of biomedical literature.
We prove that sets of MeSH terms provide a highly descriptive representation of heritable disease and that the structure of MeSH provides a natural way of combining individual MeSH vocabularies. We show that our measure can be used effectively in the prediction of candidate disease genes. We developed a web application to query more than 28.5 million relationships between 7,574 hereditary diseases (96% of OMIM) based on our similarity measure.

Original language	English
Article number	17658
Pages (from-to)	1-10
Number of pages	10
Journal	Scientific Reports
Volume	5
Early online date	3 Dec 2015
DOIs	https://doi.org/10.1038/srep17658
Publication status	E-pub ahead of print - 3 Dec 2015

Keywords

Disease similarity
Bioinformatics
Computer Science
Hereditary disease modules
MeSH ontology
Network Medicine
Disease gene prediction

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1038/srep17658Licence: CC BY

Accepted ManuscriptAccepted author manuscript, 1.23 MBLicence: CC BY

A GPU-based high performance system for discovering consensus domain architecture and functional annotation of protein families
Paccanaro, A. (PI)
Biotechnology&BioSci Research BBSRC
1/07/12 → 30/09/13
Project: Research
Development of a graph-theoretic approach to predict protein function by integrating large scale heterogeneous data
Paccanaro, A. (PI) & Bogre, L. (CoI)
Biotechnology&BioSci Research BBSRC
1/06/08 → 31/05/11
Project: Research

Cite this

@article{313bc520c9d8474f8fc6919651c48897,

title = "A network medicine approach to quantify distance between hereditary disease modules on the interactome",

abstract = "We introduce a MeSH-based method that accurately quantifies similarity between heritable diseases at molecular level. This method effectively brings together the existing information about diseases that is scattered across the vast corpus of biomedical literature.We prove that sets of MeSH terms provide a highly descriptive representation of heritable disease and that the structure of MeSH provides a natural way of combining individual MeSH vocabularies. We show that our measure can be used effectively in the prediction of candidate disease genes. We developed a web application to query more than 28.5 million relationships between 7,574 hereditary diseases (96% of OMIM) based on our similarity measure.",

keywords = "Disease similarity, Bioinformatics, Computer Science, Hereditary disease modules, MeSH ontology, Network Medicine, Disease gene prediction",

author = "Horacio Caniza and Romero, {Alfonso E} and Alberto Paccanaro",

year = "2015",

month = dec,

day = "3",

doi = "10.1038/srep17658",

language = "English",

volume = "5",

pages = "1--10",

journal = "Scientific Reports",

issn = "2045-2322",

publisher = "Nature Publishing Group",

}

TY - JOUR

T1 - A network medicine approach to quantify distance between hereditary disease modules on the interactome

AU - Caniza , Horacio

AU - Romero , Alfonso E

AU - Paccanaro, Alberto

PY - 2015/12/3

Y1 - 2015/12/3

N2 - We introduce a MeSH-based method that accurately quantifies similarity between heritable diseases at molecular level. This method effectively brings together the existing information about diseases that is scattered across the vast corpus of biomedical literature.We prove that sets of MeSH terms provide a highly descriptive representation of heritable disease and that the structure of MeSH provides a natural way of combining individual MeSH vocabularies. We show that our measure can be used effectively in the prediction of candidate disease genes. We developed a web application to query more than 28.5 million relationships between 7,574 hereditary diseases (96% of OMIM) based on our similarity measure.

AB - We introduce a MeSH-based method that accurately quantifies similarity between heritable diseases at molecular level. This method effectively brings together the existing information about diseases that is scattered across the vast corpus of biomedical literature.We prove that sets of MeSH terms provide a highly descriptive representation of heritable disease and that the structure of MeSH provides a natural way of combining individual MeSH vocabularies. We show that our measure can be used effectively in the prediction of candidate disease genes. We developed a web application to query more than 28.5 million relationships between 7,574 hereditary diseases (96% of OMIM) based on our similarity measure.

KW - Disease similarity

KW - Bioinformatics

KW - Computer Science

KW - Hereditary disease modules

KW - MeSH ontology

KW - Network Medicine

KW - Disease gene prediction

U2 - 10.1038/srep17658

DO - 10.1038/srep17658

M3 - Article

SN - 2045-2322

VL - 5

SP - 1

EP - 10

JO - Scientific Reports

JF - Scientific Reports

M1 - 17658

ER -

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

Abstract

Keywords

UN SDGs

Access to Document

Projects

A GPU-based high performance system for discovering consensus domain architecture and functional annotation of protein families

Development of a graph-theoretic approach to predict protein function by integrating large scale heterogeneous data

Cite this