Cecilia Arighi - eMIND: automatic collection of protein variation impacts in Alzheimer’s disease
Channel: Biocuration Conference 2023
Date: 2023-06-21

*Title:* eMIND: Enabling automatic collection of protein variation impacts in Alzheimer’s disease from the literature

*Session:* April 25 - Ai And Text Mining 2 - 01

*Abstract:* Alzheimer’s disease and related dementias (AD/ADRDs) are among the most common forms of dementia, and yet no effective treatments have been developed. To gain insight into the disease mechanism, capturing the connection of genetic variations to their impacts, at the disease and molecular levels, is essential. The scientific literature continues to be a main source for reporting experimental information about the impact of variants. Thus, the development of automatic methods to identify publications and extract the information from the unstructured text would facilitate collecting and organizing information for reuse. We developed eMIND, a deep learning-based text mining system that supports the automatic extraction of annotations of variants and their impacts in AD/ADRDs. In particular, we use this method to capture the impacts of protein-coding variants affecting a selected set of protein properties, such as protein activity/function, structure and post-translational modifications. A major hypothesis we are testing is that the structure and words used in statements that describe the impact of one entity on another entity or event/process are not specific to the two objects under consideration. Thus, a BERT model was fine-tuned using a training dataset with 8,245 positive and 11,496 negative impact relations derived from impact relations involving microRNAs. We conducted a preliminary evaluation on the efficacy of eMIND on a small manually annotated corpus (60 abstracts) consisting of variant impact relations from AD/ADRDs literature, and obtained a recall of 0.84 and a precision of 0.94. The publications and extracted information by eMIND are integrated into the UniProtKB computationally mapped bibliography to expand annotations on protein entries. eMIND’s text-mined output are presented using controlled vocabularies and ontologies for variant, disease and impact along with the evidence sentences. Evaluation of eMIND on a larger test dataset is ongoing. A sample of annotated abstracts can be accessed at URL: https://research.bioinformatics.udel.edu/itextmine/emind. Funding: This work has been funded by NIA supplement grant to UniProt 3U24HG007822-07S1, NIH/NHGRI: UniProt - Enhancing functional genomics data access for the Alzheimer's Disease (AD) and dementia-related protein research communities. Acknowledgements: We would like to acknowledge the UniProt Consortium (https://www.uniprot.org/help/uniprot_staff).