Abstract
BackgroundBlood cancers (BCs) are responsible for over 720 K yearly deaths worldwide. Their prevalence and mortality-rate uphold the relevance of research related to BCs. Despite the availability of different resources establishing Disease-Disease Associations (DDAs), the knowledge is scattered and not accessible in a straightforward way to the scientific community. Here, we propose SicknessMiner, a biomedical Text-Mining (TM) approach towards the centralization of DDAs. Our methodology encompasses Named Entity Recognition (NER) and Named Entity Normalization (NEN) steps, and the DDAs retrieved were compared to the DisGeNET resource for qualitative and quantitative comparison.ResultsWe obtained the DDAs via co-mention using our SicknessMiner or gene- or variant-disease similarity on DisGeNET. SicknessMiner was able to retrieve around 92% of the DisGeNET results and nearly 15% of the SicknessMiner results were specific to our pipeline.ConclusionsSicknessMiner is a valuable tool to extract disease-disease relationship from RAW input corpus.
Highlights
Blood cancers (BCs) are responsible for over 720 K yearly deaths world‐ wide
SicknessMiner two modules, Named Entity Recognition (NER) and Named Entity Normalization (NEN), were trained using a corpus of 793 PubMed abstracts with over 6.8 K disease mentions mapped to 790 unique concepts either linked to the Medical Subject Headings (MeSH) or the Online Mendelian Inheritance in Man (OMIM) databases
After the NER step, the retrieved text spans related to the entities serve as input to the NEN module, which maps each text span to an entry in a given ontology
Summary
Blood cancers (BCs) are responsible for over 720 K yearly deaths world‐ wide. Their prevalence and mortality-rate uphold the relevance of research related to BCs. Despite the availability of different resources establishing Disease-Disease Associa‐ tions (DDAs), the knowledge is scattered and not accessible in a straightforward way to the scientific community. Integrating knowledge from different sources on multiple diseases facilitates the understanding of Disease-Disease Associations (DDAs). The extraction of patterns from the evidence in relation to multiple diseases can establish networks encompassing such diseases. DDAs can be of various types and can be established considering different criteria as reviewed by Al-Eliwi and co-workers, all benefiting from network analysis approaches [1]. Several strategies were proposed like: (i) Disease Ontology (DO), which integrates concepts
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