Automated Matchmaking of Researcher Biosketches and Funder Requests for Proposals Using Deep Neural Networks.

This study developed an automated matchmaking system using deep neural networks to enhance the efficiency of pairing researcher biosketches with funders' requests for proposals (RFPs). In thus U.S., with over 900 federal grant programs and 86,000+ foundations, researchers often spend up to 200 hours on each application due to low success rates, forcing them to apply multiple times a year. Our approach improves on existing systems by fixing issues like unreliable keyword searches, and one-size-fits-all recommendations. We analyzed 12,991 biosketches from a research institution and 2,234 RFPs from the National Institutes of Health, spanning 2014 to 2019. Employing four advanced deep-learning models, utilizing cross and Siamese encoding strategies, we benchmarked their performance against conventional predictive models such as logistic regression and support vector machines. The most effective model integrated BERT with cross-encoding, a post-BERT BiLSTM layer, and back translation (BC2BT), achieving an F1-score of 71.15%. These results demonstrate the potential of sophisticated natural language processing techniques to automate complex matchmaking tasks in the research funding sector. This approach not only improves the precision of matching researchers to suitable funding opportunities but also sets a promising foundation for future advancements in automated funding mechanisms.