Abstract
Study about brain connectivity provides important bio-markers for predicting brain related disorders and also for analyzing normal human functions. Findings of this study are reported in the form of neuroscience research articles. We propose a tool, ConnExt-BioBERT, to mine relevant scientific literature for curating a large resource of reported connections between regions of the brain. We have utilized the popular transfer learning technique that has been trained on large datasets, the Bidirectional Encoder Representations for Transformers (BERT) to apply it to a narrowband subject area of extracting brain regions and potential connection mentions from a set of 53,000 full-text neuroscience articles (53kNeuroFullText) indexed on PubMed. Evaluation of ConnExt-BioBERT has been performed on a benchmark dataset of abstracts and on a dataset of seven full-text articles annotated by a domain expert. Additionally, connections retrieved by the tool on 53kNeuroFullText have been evaluated using a manually curated resource, Brain Architecture Management System (BAMS). A web-application has been developed for search over extracted brain region connections on 53kNeuroFullText. This application is currently being used by neuroscience researchers to quickly retrieve brain connectivity information reported by various authors. Large scale text mining of brain-connectivity information reported in neuroscience literature, aids in progressing research in the area of neurological disorders and further helps diagnosis and treatment of the same.
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Sharma, A., Jayakumar, J., Sankaran, N., Mitra, P.P., Chakraborti, S., Sreenivasa Kumar, P. (2021). ConnExt-BioBERT: Leveraging Transfer Learning for Brain-Connectivity Extraction from Neuroscience Articles. In: Mahmud, M., Kaiser, M.S., Vassanelli, S., Dai, Q., Zhong, N. (eds) Brain Informatics. BI 2021. Lecture Notes in Computer Science(), vol 12960. Springer, Cham. https://doi.org/10.1007/978-3-030-86993-9_22
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