getTermMatrix

Receives a sorted hashmap with found entities from a dictionary

Analysis and visualization of similarities between epilepsy ontologies based on text mining results by comparing ranked lists of co-occurring drug terms in the BioASQ corpus. The ranked result lists of neurological drug terms co-occurring with terms from the epilepsy ontologies EpSO, ESSO, EPILONT, EPISEM and FENICS undergo further analysis. The source data to create the ranked lists of drug names is produced using the text mining workflows described in Mueller, Bernd and Hagelstein, Alexandra (2016) <doi:10.4126/FRL01-006408558>, Mueller, Bernd et al. (2017) <doi:10.1007/978-3-319-58694-6_22>, Mueller, Bernd and Rebholz-Schuhmann, Dietrich (2020) <doi:10.1007/978-3-030-43887-6_52>, and Mueller, Bernd et al. (2022) <doi:10.1186/s13326-021-00258-w>.

Bernd Mueller

epos

Epilepsy Ontologies' Similarities

getTermMatrix function

<dl><dt>dictionary</dt>
<dd>Character vector that is the name of a dicitonary having pre-calculated stats. This can be
MeSH, DrugBank, Agrovoc, EpSO, ESSO, or EPILONT</dd>
<dt>database</dt>
<dd>the name of the MongoDB database to be used</dd>
<dt>collection</dt>
<dd>the name of the MongoDB collection to be used</dd></dl>

Arguments

Receives a sorted hashmap with found entities from a dictionary — getTermMatrix

<dl>

<dt>dictionary</dt>
<dd>Character vector that is the name of a dicitonary having pre-calculated stats. This can be
MeSH, DrugBank, Agrovoc, EpSO, ESSO, or EPILONT</dd>


<dt>database</dt>
<dd>the name of the MongoDB database to be used</dd>


<dt>collection</dt>
<dd>the name of the MongoDB collection to be used</dd>

</dl>

getTermMatrix: Receives a sorted hashmap with found entities from a dictionary

Description

Usage

Value

Arguments

Examples