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A vector space model approach to identify genetically related diseases.

ABSTRACT: The relationship between diseases and their causative genes can be complex, especially in the case of polygenic diseases. Further exacerbating the challenges in their study is that many genes may be causally related to multiple diseases. This study explored the relationship between diseases through the adaptation of an approach pioneered in the context of information retrieval: vector space models.A vector space model approach was developed that bridges gene disease knowledge inferred across three knowledge bases: Online Mendelian Inheritance in Man, GenBank, and Medline. The approach was then used to identify potentially related diseases for two target diseases: Alzheimer disease and Prader-Willi Syndrome.In the case of both Alzheimer Disease and Prader-Willi Syndrome, a set of plausible diseases were identified that may warrant further exploration.This study furthers seminal work by Swanson, et al. that demonstrated the potential for mining literature for putative correlations. Using a vector space modeling approach, information from both biomedical literature and genomic resources (like GenBank) can be combined towards identification of putative correlations of interest. To this end, the relevance of the predicted diseases of interest in this study using the vector space modeling approach were validated based on supporting literature.The results of this study suggest that a vector space model approach may be a useful means to identify potential relationships between complex diseases, and thereby enable the coordination of gene-based findings across multiple complex diseases.

SUBMITTER: Sarkar IN

PROVIDER: S-EPMC3277619 | biostudies-literature | 2012 Mar-Apr

REPOSITORIES: biostudies-literature

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A vector space model approach to identify genetically related diseases.

Sarkar Indra Neil IN

Journal of the American Medical Informatics Association : JAMIA 20120106 2

<h4>Objective</h4>The relationship between diseases and their causative genes can be complex, especially in the case of polygenic diseases. Further exacerbating the challenges in their study is that many genes may be causally related to multiple diseases. This study explored the relationship between diseases through the adaptation of an approach pioneered in the context of information retrieval: vector space models.<h4>Materials and methods</h4>A vector space model approach was developed that br ...[more]

PMID: 22227640

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Project description:BackgroundBiological systems are commonly described as networks of entity interactions. Some interactions are already known and integrate the current knowledge in life sciences. Others remain unknown for long periods of time and are frequently discovered by chance. In this work we present a model to predict these unknown interactions from a textual collection using the vector space model (VSM), a well known and established information retrieval model. We have extended the VSM ability to retrieve information using a transitive closure approach. Our objective is to use the VSM to identify the known interactions from the literature and construct a network. Based on interactions established in the network our model applies the transitive closure in order to predict and rank new interactions.ResultsWe have tested and validated our model using a collection of patent claims issued from 1976 to 2005. From 266,528 possible interactions in our network, the model identified 1,027 known interactions and predicted 3,195 new interactions. Iterating the model according to patent issue dates, interactions found in a given past year were often confirmed by patent claims not in the collection and issued in more recent years. Most confirmation patent claims were found at the top 100 new interactions obtained from each subnetwork. We have also found papers on the Web which confirm new inferred interactions. For instance, the best new interaction inferred by our model relates the interaction between the adrenaline neurotransmitter and the androgen receptor gene. We have found a paper that reports the partial dependence of the antiapoptotic effect of adrenaline on androgen receptor.ConclusionsThe VSM extended with a transitive closure approach provides a good way to identify biological interactions from textual collections. Specifically for the context of literature-based discovery, the extended VSM contributes to identify and rank relevant new interactions even if these interactions occur in only a few documents in the collection. Consequently, we have developed an efficient method for extracting and restricting the best potential results to consider as new advances in life sciences, even when indications of these results are not easily observed from a mass of documents.

Project description:After a request from the European Commission, EFSA's Panel on Animal Health and Welfare summarised the main characteristics of 36 vector-borne diseases (VBDs) in https://efsa.maps.arcgis.com/apps/PublicGallery/index.html?appid=dfbeac92aea944599ed1eb754aa5e6d1. The risk of introduction in the EU through movement of livestock or pets was assessed for each of the 36 VBDs individually, using a semiquantitative Method to INTegrate all relevant RISK aspects (MINTRISK model), which was further modified to a European scale into the http://www3.lei.wur.nl/mintrisk/ModelMgt.aspx. Only eight of the 36 VBD-agents had an overall rate of introduction in the EU (being the combination of the rate of entry, vector transmission and establishment) which was estimated to be above 0.001 introductions per year. These were Crimean-Congo haemorrhagic fever virus, bluetongue virus, West Nile virus, Schmallenberg virus, Hepatozoon canis, Leishmania infantum, Bunyamwera virus and Highlands J. virus. For these eight diseases, the annual extent of spread was assessed, assuming the implementation of available, authorised prevention and control measures in the EU. Further, the probability of overwintering was assessed, as well as the possible impact of the VBDs on public health, animal health and farm production. For the other 28 VBD-agents for which the rate of introduction was estimated to be very low, no further assessments were made. Due to the uncertainty related to some parameters used for the risk assessment or the instable or unpredictability disease situation in some of the source regions, it is recommended to update the assessment when new information becomes available. Since this risk assessment was carried out for large regions in the EU for many VBD-agents, it should be considered as a first screening. If a more detailed risk assessment for a specific VBD is wished for on a national or subnational level, the EFSA-VBD-RISK-model is freely available for this purpose.

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A vector space model approach to identify genetically related diseases.

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A vector space model approach to identify genetically related diseases.

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