dice

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

dice function

<dl><dt>ainterb</dt>
<dd>integer value with number of intersecting elements between set a and b</dd>
<dt>lengtha</dt>
<dd>integer value with the number of items in set a</dd>
<dt>lengthb</dt>
<dd>integer value with the number of items in set b</dd></dl>

Arguments

Calculate dice similarity metric — dice

<dl>

<dt>ainterb</dt>
<dd>integer value with number of intersecting elements between set a and b</dd>


<dt>lengtha</dt>
<dd>integer value with the number of items in set a</dd>


<dt>lengthb</dt>
<dd>integer value with the number of items in set b</dd>

</dl>

dice: Calculate dice similarity metric

Description

Usage

Value

Arguments

Examples