Chemical Entity Normalization for Successful Translational Development of Alzheimer's Disease and Dementia Therapeutics
| Autor: | Mullin, Sarah, McDougal, Robert, Cheung, Kei-Hoi, Kilicoglu, Halil, Beck, Amanda, Zeiss, Caroline J |
|---|---|
| Rok vydání: | 2021 |
| Předmět: | |
| DOI: | 10.5281/zenodo.5838528 |
| Popis: | Despite advances in identifying the biological basis of Alzheimer���s disease (AD) and dementia, there remain few chemical therapeutic interventions. One major challenge is the poor translation of effective therapies from animals to humans. Text mining translation-related characteristics, such as chemical interventions, can help to address this challenge. However, normalization to a standardized ontology that contains hierarchical relations and molecule structure information, is challenging. We provide a reproducible hierarchical primarily dictionary-based method to normalize chemical mentions from PubTator to Chemical Entities of Biological Interest (ChEBI), a fully curated database and OBO Foundry ontology for molecular entities. To generate this mapping we make use of external synonym databases, ChEBI parent-child relationships, and nearby context words. We found 277,844 PubMed abstracts related to Alzheimer���s and dementia in PubTator. Of the total 55,574 chemical mentions found in the article title, we normalized 49,966 mentions to 3,507 unique ChEBI entities. In addition, we were able to identify potential new candidate entities related to AD and dementia from the remaining 9.4%. Patterns that emerge from aggregation of standardized chemical interventions can help ascertain translational potential. In addition, effective and correct normalization in text mining is important for future downstream applications, such as improved efficacy and drug design. Funding: Yale Alzheimer's Disease Research Center NIH/NIA 5P30AG066508, NIH/NLM T15 Grant 5T15LM007056-35 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|