Automated extraction of potential migraine biomarkers using a semantic graph

Abstract

Semantic knowledge graphs composed of information integrated from multiple and varying sources can assist researchers in identifying potential disease biomarkers.

A systematic review on literature-based discovery workflow

“…Different to the threshold-based removal, Xun et al (2017) have followed Law of conformity to remove general terms by analysing the temporal change of terms, and Jha & Jin (2016b) have considered outliers of the box-plot as the general terms removal mechanism. Semantic Category Filter: This technique typically utilises the semantic type or group information provided by UMLS (Lever et al, 2017;Vlietstra et al, 2017). UMLS currently provides 127 semantic types (https://semanticnetwork.nlm.nih.gov/ SemanticNetworkArchive.html) and each medical concept is classified to one or more of these semantic types based on the relevance.…”

“…Relations-based Measures: Relations/predicate based measures (a sub-class of knowledgebased measures) analyse the relations extracted from resources such as SemRep to rank/threshold associations. Scoring measures such as Semantic relations frequency (Hristovski et al, 2010), Predicate independence (Rastegar-Mojarad et al, 2015), Predicate interdependence (Rastegar-Mojarad et al, 2015), Edge frequency-based weight , Edge traversal probability (Vlietstra et al, 2017), Relationship traversal probability (Vlietstra et al, 2017), Source traversal probability (Jha & Jin, 2016b), and Impact Factor (Huang et al, 2016) are examples of this category.…”

“…Resources to acquire background domain knowledge: The main purposes of extracting the domain knowledge are; (1) input data preparation (e.g., concept extraction), (2) filtering the noisy, uninteresting or unrelated associations (e.g., semantic type filtering), (3) prepare a ranking mechanism (e.g., hierarchical ranking), (4) evaluate the results (e.g., compare results with curated databases), and (5) training data preparation. The popular domain dependent resources used in the discipline are; -UMLS: (Lever et al, 2017;Vlietstra et al, 2017;Preiss & Stevenson, 2017) -MeSH: (Baek et al, 2017;Xun et al, 2017;Pusala et al, 2017) -SemMedDB/Semantic Medline: (Vlietstra et al, 2017;Cairelli et al, 2015) -Gene Ontology: (Baek et al, 2017;Huang et al, 2016; -Entrez Gene Database: (Baek et al, 2017;Liang, Wang & Wang, 2013;Kwofie et al, 2011) -Kyoto Encyclopedia of Genes and Genomes (KEGG): (Kwofie et al, 2011) -HGNC/HUGO: (Petric et al, 2014;Ding et al, 2013;Maciel et al, 2011) -UNIPROT: (Baek et al, 2017;Vlietstra et al, 2017), Swiss-Prot (Jelier et al, 2008) -Therapeutic Target Database (TTD): (Yang et al, 2017;Maciel et al, 2011) -LocusLink: (Smalheiser, 2005;Hristovski et al, 2003) -Online Mendelian Inheritance in Man (OMIM) (Hristovski et al, 2003;Wren et al, 2004) -Drug Bank: (Vlietstra et al, 2017;Maciel et al, 2011;Ding et al, 2013) -Comparative Toxicogenomics Database (CTD): (Vlietstra et al, 2017;Yang et al, 2017) -BioGRID: (Huang et al, 2016;Crichton et al, 2018) -Gene2pubmed: (Cheung,...…”

“…-MetaMap (Medical concept extraction): (Preiss & Stevenson, 2017;Preiss & Stevenson, 2016;Cairelli et al, 2015) -SemRep (Semantic predications extraction): (Vlietstra et al, 2017;Preiss, Stevenson & Gaizauskas, 2015) -Genia Tagger (Biological NER): (Lever et al, 2017;Özgür et al, 2010) -ABNER (Biological NER): (Liang, Wang & Wang, 2013) -Peregrine software (Biological NER): (Jelier et al, 2008) -DAVID tool (Gene annotation enrichment analysis): (Maver et al, 2013;Özgür et al, 2010) -RankProd Package (Meta analysis): (Maver et al, 2013) -BioTeKS Text Analysis Engine (Text annotation): (Berardi et al, 2005) -PubTator (PubMed citations annotation): (Crichton et al, 2018) -MedLEE (Structure and encode clinical reports): (Malec et al, 2016) -BioMedLEE (Semantic predications extraction): (Hristovski et al, 2006) -EpiphaNet (Interactive visual representation): (Malec et al, 2016) -SciMiner (literature mining and functional enrichment analysis): (Hur et al, 2010) -Biovista (Drug repurposing, Systems literature analysis environment): (Persidis, Deftereos & Persidis, 2004) Among the content analysis tools, we observed that MetaMap and SemRep are the most popular selections 3 . MetaMap is a tool that recognises UMLS concepts in the text whereas SemRep is used to extract semantic predications from the text.…”

“…To facilitate tasks such as network construction and visualisation, the following resources have been utilised in the literature; Neo4j (Vlietstra et al, 2017), JUNG , Gephi (Song, Heo & Ding, 2015), NetworkX (Wilkowski et al, 2011), and Large Graph Layout (LGL) (Ittipanuvat et al, 2014).…”