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A novel NIH research grant recommender using BERT
Research grants are important for researchers to sustain a good position in academia. There are many grant opportunities available from different funding agencies. However, finding relevant grant announcements is challenging and time-consuming for researchers. To resolve the problem, we proposed a g...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9844873/ https://www.ncbi.nlm.nih.gov/pubmed/36649346 http://dx.doi.org/10.1371/journal.pone.0278636 |
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author | Zhu, Jie Patra, Braja Gopal Wu, Hulin Yaseen, Ashraf |
author_facet | Zhu, Jie Patra, Braja Gopal Wu, Hulin Yaseen, Ashraf |
author_sort | Zhu, Jie |
collection | PubMed |
description | Research grants are important for researchers to sustain a good position in academia. There are many grant opportunities available from different funding agencies. However, finding relevant grant announcements is challenging and time-consuming for researchers. To resolve the problem, we proposed a grant announcements recommendation system for the National Institute of Health (NIH) grants using researchers’ publications. We formulated the recommendation as a classification problem and proposed a recommender using state-of-the-art deep learning techniques: i.e. Bidirectional Encoder Representations from Transformers (BERT), to capture intrinsic, non-linear relationship between researchers’ publications and grants announcements. Internal and external evaluations were conducted to assess the system’s usefulness. During internal evaluations, the grant citations were used to establish grant-publication ground truth, and results were evaluated against Recall@k, Precision@k, Mean reciprocal rank (MRR) and Area under the Receiver Operating Characteristic curve (ROC-AUC). During external evaluations, researchers’ publications were clustered using Dirichlet Process Mixture Model (DPMM), recommended grants by our model were then aggregated per cluster through Recency Weight, and finally researchers were invited to provide ratings to recommendations to calculate Precision@k. For comparison, baseline recommenders using Okapi Best Matching (BM25), Term-Frequency Inverse Document Frequency (TF-IDF), doc2vec, and Naïve Bayes (NB) were also developed. Both internal and external evaluations (all metrics) revealed favorable performances of our proposed BERT-based recommender. |
format | Online Article Text |
id | pubmed-9844873 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-98448732023-01-18 A novel NIH research grant recommender using BERT Zhu, Jie Patra, Braja Gopal Wu, Hulin Yaseen, Ashraf PLoS One Research Article Research grants are important for researchers to sustain a good position in academia. There are many grant opportunities available from different funding agencies. However, finding relevant grant announcements is challenging and time-consuming for researchers. To resolve the problem, we proposed a grant announcements recommendation system for the National Institute of Health (NIH) grants using researchers’ publications. We formulated the recommendation as a classification problem and proposed a recommender using state-of-the-art deep learning techniques: i.e. Bidirectional Encoder Representations from Transformers (BERT), to capture intrinsic, non-linear relationship between researchers’ publications and grants announcements. Internal and external evaluations were conducted to assess the system’s usefulness. During internal evaluations, the grant citations were used to establish grant-publication ground truth, and results were evaluated against Recall@k, Precision@k, Mean reciprocal rank (MRR) and Area under the Receiver Operating Characteristic curve (ROC-AUC). During external evaluations, researchers’ publications were clustered using Dirichlet Process Mixture Model (DPMM), recommended grants by our model were then aggregated per cluster through Recency Weight, and finally researchers were invited to provide ratings to recommendations to calculate Precision@k. For comparison, baseline recommenders using Okapi Best Matching (BM25), Term-Frequency Inverse Document Frequency (TF-IDF), doc2vec, and Naïve Bayes (NB) were also developed. Both internal and external evaluations (all metrics) revealed favorable performances of our proposed BERT-based recommender. Public Library of Science 2023-01-17 /pmc/articles/PMC9844873/ /pubmed/36649346 http://dx.doi.org/10.1371/journal.pone.0278636 Text en © 2023 Zhu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Zhu, Jie Patra, Braja Gopal Wu, Hulin Yaseen, Ashraf A novel NIH research grant recommender using BERT |
title | A novel NIH research grant recommender using BERT |
title_full | A novel NIH research grant recommender using BERT |
title_fullStr | A novel NIH research grant recommender using BERT |
title_full_unstemmed | A novel NIH research grant recommender using BERT |
title_short | A novel NIH research grant recommender using BERT |
title_sort | novel nih research grant recommender using bert |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9844873/ https://www.ncbi.nlm.nih.gov/pubmed/36649346 http://dx.doi.org/10.1371/journal.pone.0278636 |
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