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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0278636 |
| _version_ | 1797944037839208448 |
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| author | Jie Zhu Braja Gopal Patra Hulin Wu Ashraf Yaseen |
| author_facet | Jie Zhu Braja Gopal Patra Hulin Wu Ashraf Yaseen |
| author_sort | Jie Zhu |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-10T20:32:21Z |
| format | Article |
| id | doaj.art-f6e1fe42f07e4e8cb6b41e3ed3c1b271 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-10T20:32:21Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-f6e1fe42f07e4e8cb6b41e3ed3c1b2712023-01-25T05:33:11ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01181e027863610.1371/journal.pone.0278636A novel NIH research grant recommender using BERT.Jie ZhuBraja Gopal PatraHulin WuAshraf YaseenResearch 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.https://doi.org/10.1371/journal.pone.0278636 |
| spellingShingle | Jie Zhu Braja Gopal Patra Hulin Wu Ashraf Yaseen A novel NIH research grant recommender using BERT. PLoS ONE |
| 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 |
| url | https://doi.org/10.1371/journal.pone.0278636 |
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