Large-scale protein-protein post-translational modification extraction with distant supervision and confidence calibrated BioBERT
Motivation Protein-protein interactions (PPIs) are critical to normal cellular function and are related to many disease pathways. A range of protein functions are mediated and regulated by protein interactions through post-translational modifications (PTM). However, only 4% of PPIs are annotated wit...
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| Published in | BMC bioinformatics Vol. 23; no. 1; pp. 4 - 23 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
04.01.2022
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1471-2105 1471-2105 |
| DOI | 10.1186/s12859-021-04504-x |
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| Abstract | Motivation
Protein-protein interactions (PPIs) are critical to normal cellular function and are related to many disease pathways. A range of protein functions are mediated and regulated by protein interactions through post-translational modifications (PTM). However, only 4% of PPIs are annotated with PTMs in biological knowledge databases such as IntAct, mainly performed through manual curation, which is neither time- nor cost-effective. Here we aim to facilitate annotation by extracting PPIs along with their pairwise PTM from the literature by using distantly supervised training data using deep learning to aid human curation.
Method
We use the IntAct PPI database to create a distant supervised dataset annotated with interacting protein pairs, their corresponding PTM type, and associated abstracts from the PubMed database. We train an ensemble of BioBERT models—dubbed PPI-BioBERT-x10—to improve confidence calibration. We extend the use of ensemble average confidence approach with confidence variation to counteract the effects of class imbalance to extract high confidence predictions.
Results and conclusion
The PPI-BioBERT-x10 model evaluated on the test set resulted in a modest F1-micro 41.3 (P =5 8.1, R = 32.1). However, by combining high confidence and low variation to identify high quality predictions, tuning the predictions for precision, we retained 19% of the test predictions with 100% precision. We evaluated PPI-BioBERT-x10 on 18 million PubMed abstracts and extracted 1.6 million (546507 unique PTM-PPI triplets) PTM-PPI predictions, and filter
≈
5700
(4584 unique) high confidence predictions. Of the 5700, human evaluation on a small randomly sampled subset shows that the precision drops to 33.7% despite confidence calibration and highlights the challenges of generalisability beyond the test set even with confidence calibration. We circumvent the problem by only including predictions associated with multiple papers, improving the precision to 58.8%. In this work, we highlight the benefits and challenges of deep learning-based text mining in practice, and the need for increased emphasis on confidence calibration to facilitate human curation efforts. |
|---|---|
| AbstractList | Motivation
Protein-protein interactions (PPIs) are critical to normal cellular function and are related to many disease pathways. A range of protein functions are mediated and regulated by protein interactions through post-translational modifications (PTM). However, only 4% of PPIs are annotated with PTMs in biological knowledge databases such as IntAct, mainly performed through manual curation, which is neither time- nor cost-effective. Here we aim to facilitate annotation by extracting PPIs along with their pairwise PTM from the literature by using distantly supervised training data using deep learning to aid human curation.
Method
We use the IntAct PPI database to create a distant supervised dataset annotated with interacting protein pairs, their corresponding PTM type, and associated abstracts from the PubMed database. We train an ensemble of BioBERT models—dubbed PPI-BioBERT-x10—to improve confidence calibration. We extend the use of ensemble average confidence approach with confidence variation to counteract the effects of class imbalance to extract high confidence predictions.
Results and conclusion
The PPI-BioBERT-x10 model evaluated on the test set resulted in a modest F1-micro 41.3 (P =5 8.1, R = 32.1). However, by combining high confidence and low variation to identify high quality predictions, tuning the predictions for precision, we retained 19% of the test predictions with 100% precision. We evaluated PPI-BioBERT-x10 on 18 million PubMed abstracts and extracted 1.6 million (546507 unique PTM-PPI triplets) PTM-PPI predictions, and filter
≈
5700
(4584 unique) high confidence predictions. Of the 5700, human evaluation on a small randomly sampled subset shows that the precision drops to 33.7% despite confidence calibration and highlights the challenges of generalisability beyond the test set even with confidence calibration. We circumvent the problem by only including predictions associated with multiple papers, improving the precision to 58.8%. In this work, we highlight the benefits and challenges of deep learning-based text mining in practice, and the need for increased emphasis on confidence calibration to facilitate human curation efforts. Motivation We use the IntAct PPI database to create a distant supervised dataset annotated with interacting protein pairs, their corresponding PTM type, and associated abstracts from the PubMed database. We train an ensemble of BioBERT models--dubbed PPI-BioBERT-x10--to improve confidence calibration. We extend the use of ensemble average confidence approach with confidence variation to counteract the effects of class imbalance to extract high confidence predictions. The PPI-BioBERT-x10 model evaluated on the test set resulted in a modest F1-micro 41.3 (P =5 8.1, R = 32.1). However, by combining high confidence and low variation to identify high quality predictions, tuning the predictions for precision, we retained 19% of the test predictions with 100% precision. We evaluated PPI-BioBERT-x10 on 18 million PubMed abstracts and extracted 1.6 million (546507 unique PTM-PPI triplets) PTM-PPI predictions, and filter [formula omitted] (4584 unique) high confidence predictions. Of the 5700, human evaluation on a small randomly sampled subset shows that the precision drops to 33.7% despite confidence calibration and highlights the challenges of generalisability beyond the test set even with confidence calibration. We circumvent the problem by only including predictions associated with multiple papers, improving the precision to 58.8%. In this work, we highlight the benefits and challenges of deep learning-based text mining in practice, and the need for increased emphasis on confidence calibration to facilitate human curation efforts. Motivation Protein-protein interactions (PPIs) are critical to normal cellular function and are related to many disease pathways. A range of protein functions are mediated and regulated by protein interactions through post-translational modifications (PTM). However, only 4% of PPIs are annotated with PTMs in biological knowledge databases such as IntAct, mainly performed through manual curation, which is neither time- nor cost-effective. Here we aim to facilitate annotation by extracting PPIs along with their pairwise PTM from the literature by using distantly supervised training data using deep learning to aid human curation. Method We use the IntAct PPI database to create a distant supervised dataset annotated with interacting protein pairs, their corresponding PTM type, and associated abstracts from the PubMed database. We train an ensemble of BioBERT models--dubbed PPI-BioBERT-x10--to improve confidence calibration. We extend the use of ensemble average confidence approach with confidence variation to counteract the effects of class imbalance to extract high confidence predictions. Results and conclusion The PPI-BioBERT-x10 model evaluated on the test set resulted in a modest F1-micro 41.3 (P =5 8.1, R = 32.1). However, by combining high confidence and low variation to identify high quality predictions, tuning the predictions for precision, we retained 19% of the test predictions with 100% precision. We evaluated PPI-BioBERT-x10 on 18 million PubMed abstracts and extracted 1.6 million (546507 unique PTM-PPI triplets) PTM-PPI predictions, and filter [formula omitted] (4584 unique) high confidence predictions. Of the 5700, human evaluation on a small randomly sampled subset shows that the precision drops to 33.7% despite confidence calibration and highlights the challenges of generalisability beyond the test set even with confidence calibration. We circumvent the problem by only including predictions associated with multiple papers, improving the precision to 58.8%. In this work, we highlight the benefits and challenges of deep learning-based text mining in practice, and the need for increased emphasis on confidence calibration to facilitate human curation efforts. Keywords: Protein-protein interaction, Post-translational modifications, BioBERT, Natural language processing, Deep learning, Distant supervision Abstract Motivation Protein-protein interactions (PPIs) are critical to normal cellular function and are related to many disease pathways. A range of protein functions are mediated and regulated by protein interactions through post-translational modifications (PTM). However, only 4% of PPIs are annotated with PTMs in biological knowledge databases such as IntAct, mainly performed through manual curation, which is neither time- nor cost-effective. Here we aim to facilitate annotation by extracting PPIs along with their pairwise PTM from the literature by using distantly supervised training data using deep learning to aid human curation. Method We use the IntAct PPI database to create a distant supervised dataset annotated with interacting protein pairs, their corresponding PTM type, and associated abstracts from the PubMed database. We train an ensemble of BioBERT models—dubbed PPI-BioBERT-x10—to improve confidence calibration. We extend the use of ensemble average confidence approach with confidence variation to counteract the effects of class imbalance to extract high confidence predictions. Results and conclusion The PPI-BioBERT-x10 model evaluated on the test set resulted in a modest F1-micro 41.3 (P =5 8.1, R = 32.1). However, by combining high confidence and low variation to identify high quality predictions, tuning the predictions for precision, we retained 19% of the test predictions with 100% precision. We evaluated PPI-BioBERT-x10 on 18 million PubMed abstracts and extracted 1.6 million (546507 unique PTM-PPI triplets) PTM-PPI predictions, and filter $$\approx 5700$$ ≈ 5700 (4584 unique) high confidence predictions. Of the 5700, human evaluation on a small randomly sampled subset shows that the precision drops to 33.7% despite confidence calibration and highlights the challenges of generalisability beyond the test set even with confidence calibration. We circumvent the problem by only including predictions associated with multiple papers, improving the precision to 58.8%. In this work, we highlight the benefits and challenges of deep learning-based text mining in practice, and the need for increased emphasis on confidence calibration to facilitate human curation efforts. Motivation Protein-protein interactions (PPIs) are critical to normal cellular function and are related to many disease pathways. A range of protein functions are mediated and regulated by protein interactions through post-translational modifications (PTM). However, only 4% of PPIs are annotated with PTMs in biological knowledge databases such as IntAct, mainly performed through manual curation, which is neither time- nor cost-effective. Here we aim to facilitate annotation by extracting PPIs along with their pairwise PTM from the literature by using distantly supervised training data using deep learning to aid human curation. Method We use the IntAct PPI database to create a distant supervised dataset annotated with interacting protein pairs, their corresponding PTM type, and associated abstracts from the PubMed database. We train an ensemble of BioBERT models—dubbed PPI-BioBERT-x10—to improve confidence calibration. We extend the use of ensemble average confidence approach with confidence variation to counteract the effects of class imbalance to extract high confidence predictions. Results and conclusion The PPI-BioBERT-x10 model evaluated on the test set resulted in a modest F1-micro 41.3 (P =5 8.1, R = 32.1). However, by combining high confidence and low variation to identify high quality predictions, tuning the predictions for precision, we retained 19% of the test predictions with 100% precision. We evaluated PPI-BioBERT-x10 on 18 million PubMed abstracts and extracted 1.6 million (546507 unique PTM-PPI triplets) PTM-PPI predictions, and filter \(\approx 5700\) (4584 unique) high confidence predictions. Of the 5700, human evaluation on a small randomly sampled subset shows that the precision drops to 33.7% despite confidence calibration and highlights the challenges of generalisability beyond the test set even with confidence calibration. We circumvent the problem by only including predictions associated with multiple papers, improving the precision to 58.8%. In this work, we highlight the benefits and challenges of deep learning-based text mining in practice, and the need for increased emphasis on confidence calibration to facilitate human curation efforts. Protein-protein interactions (PPIs) are critical to normal cellular function and are related to many disease pathways. A range of protein functions are mediated and regulated by protein interactions through post-translational modifications (PTM). However, only 4% of PPIs are annotated with PTMs in biological knowledge databases such as IntAct, mainly performed through manual curation, which is neither time- nor cost-effective. Here we aim to facilitate annotation by extracting PPIs along with their pairwise PTM from the literature by using distantly supervised training data using deep learning to aid human curation. We use the IntAct PPI database to create a distant supervised dataset annotated with interacting protein pairs, their corresponding PTM type, and associated abstracts from the PubMed database. We train an ensemble of BioBERT models-dubbed PPI-BioBERT-x10-to improve confidence calibration. We extend the use of ensemble average confidence approach with confidence variation to counteract the effects of class imbalance to extract high confidence predictions. The PPI-BioBERT-x10 model evaluated on the test set resulted in a modest F1-micro 41.3 (P =5 8.1, R = 32.1). However, by combining high confidence and low variation to identify high quality predictions, tuning the predictions for precision, we retained 19% of the test predictions with 100% precision. We evaluated PPI-BioBERT-x10 on 18 million PubMed abstracts and extracted 1.6 million (546507 unique PTM-PPI triplets) PTM-PPI predictions, and filter [Formula: see text] (4584 unique) high confidence predictions. Of the 5700, human evaluation on a small randomly sampled subset shows that the precision drops to 33.7% despite confidence calibration and highlights the challenges of generalisability beyond the test set even with confidence calibration. We circumvent the problem by only including predictions associated with multiple papers, improving the precision to 58.8%. In this work, we highlight the benefits and challenges of deep learning-based text mining in practice, and the need for increased emphasis on confidence calibration to facilitate human curation efforts. Protein-protein interactions (PPIs) are critical to normal cellular function and are related to many disease pathways. A range of protein functions are mediated and regulated by protein interactions through post-translational modifications (PTM). However, only 4% of PPIs are annotated with PTMs in biological knowledge databases such as IntAct, mainly performed through manual curation, which is neither time- nor cost-effective. Here we aim to facilitate annotation by extracting PPIs along with their pairwise PTM from the literature by using distantly supervised training data using deep learning to aid human curation.MOTIVATIONProtein-protein interactions (PPIs) are critical to normal cellular function and are related to many disease pathways. A range of protein functions are mediated and regulated by protein interactions through post-translational modifications (PTM). However, only 4% of PPIs are annotated with PTMs in biological knowledge databases such as IntAct, mainly performed through manual curation, which is neither time- nor cost-effective. Here we aim to facilitate annotation by extracting PPIs along with their pairwise PTM from the literature by using distantly supervised training data using deep learning to aid human curation.We use the IntAct PPI database to create a distant supervised dataset annotated with interacting protein pairs, their corresponding PTM type, and associated abstracts from the PubMed database. We train an ensemble of BioBERT models-dubbed PPI-BioBERT-x10-to improve confidence calibration. We extend the use of ensemble average confidence approach with confidence variation to counteract the effects of class imbalance to extract high confidence predictions.METHODWe use the IntAct PPI database to create a distant supervised dataset annotated with interacting protein pairs, their corresponding PTM type, and associated abstracts from the PubMed database. We train an ensemble of BioBERT models-dubbed PPI-BioBERT-x10-to improve confidence calibration. We extend the use of ensemble average confidence approach with confidence variation to counteract the effects of class imbalance to extract high confidence predictions.The PPI-BioBERT-x10 model evaluated on the test set resulted in a modest F1-micro 41.3 (P =5 8.1, R = 32.1). However, by combining high confidence and low variation to identify high quality predictions, tuning the predictions for precision, we retained 19% of the test predictions with 100% precision. We evaluated PPI-BioBERT-x10 on 18 million PubMed abstracts and extracted 1.6 million (546507 unique PTM-PPI triplets) PTM-PPI predictions, and filter [Formula: see text] (4584 unique) high confidence predictions. Of the 5700, human evaluation on a small randomly sampled subset shows that the precision drops to 33.7% despite confidence calibration and highlights the challenges of generalisability beyond the test set even with confidence calibration. We circumvent the problem by only including predictions associated with multiple papers, improving the precision to 58.8%. In this work, we highlight the benefits and challenges of deep learning-based text mining in practice, and the need for increased emphasis on confidence calibration to facilitate human curation efforts.RESULTS AND CONCLUSIONThe PPI-BioBERT-x10 model evaluated on the test set resulted in a modest F1-micro 41.3 (P =5 8.1, R = 32.1). However, by combining high confidence and low variation to identify high quality predictions, tuning the predictions for precision, we retained 19% of the test predictions with 100% precision. We evaluated PPI-BioBERT-x10 on 18 million PubMed abstracts and extracted 1.6 million (546507 unique PTM-PPI triplets) PTM-PPI predictions, and filter [Formula: see text] (4584 unique) high confidence predictions. Of the 5700, human evaluation on a small randomly sampled subset shows that the precision drops to 33.7% despite confidence calibration and highlights the challenges of generalisability beyond the test set even with confidence calibration. We circumvent the problem by only including predictions associated with multiple papers, improving the precision to 58.8%. In this work, we highlight the benefits and challenges of deep learning-based text mining in practice, and the need for increased emphasis on confidence calibration to facilitate human curation efforts. |
| ArticleNumber | 4 |
| Audience | Academic |
| Author | Li, Yuan Pires, Douglas E. V. Elangovan, Aparna Verspoor, Karin Davis, Melissa J. |
| Author_xml | – sequence: 1 givenname: Aparna surname: Elangovan fullname: Elangovan, Aparna organization: School of Computing and Information Systems, The University of Melbourne – sequence: 2 givenname: Yuan surname: Li fullname: Li, Yuan organization: School of Computing and Information Systems, The University of Melbourne – sequence: 3 givenname: Douglas E. V. surname: Pires fullname: Pires, Douglas E. V. organization: School of Computing and Information Systems, The University of Melbourne – sequence: 4 givenname: Melissa J. surname: Davis fullname: Davis, Melissa J. organization: The Walter and Eliza Hall Institute of Medical Research, Department of Clinical Pathology, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne – sequence: 5 givenname: Karin surname: Verspoor fullname: Verspoor, Karin email: karin.verspoor@rmit.edu.au organization: School of Computing and Information Systems, The University of Melbourne, School of Computing Technologies, RMIT University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34983371$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_3390_jpm14121157 crossref_primary_10_1186_s13643_024_02470_y crossref_primary_10_1038_s41568_024_00784_6 crossref_primary_10_1007_s44163_024_00197_2 crossref_primary_10_1016_j_mcpro_2023_100682 |
| Cites_doi | 10.1093/database/bav009 10.1186/1471-2105-8-50 10.1093/bioinformatics/btz682 10.18653/v1/W17-2323 10.1093/nar/gkj141 10.1093/nar/gku1267 10.18653/v1/2020.blackboxnlp-1.21 10.1109/TCBB.2014.2372765 10.1016/S2589-7500(20)30186-2 10.1109/ACCESS.2019.2927253 10.1093/nar/gku1055 10.18653/v1/N19-1423 10.1093/nar/gks1094 10.1093/database/bav020 10.1093/database/bax040 10.1093/nar/gkx1104 10.1016/j.artmed.2004.07.016 10.1016/j.ijhcs.2019.05.008 10.1016/j.knosys.2018.11.020 10.1093/nar/gkt1115 10.1155/2015/918710 10.1093/nar/gky1131 10.1007/s10994-021-05946-3 10.1093/database/bay122 10.3115/1690219.1690287 10.1038/nmeth.1931 10.18653/v1/2021.eacl-main.113 10.18653/v1/W17-2304 10.1093/nar/gky1049 |
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| Keywords | Deep learning Distant supervision Natural language processing Post-translational modifications Protein-protein interaction BioBERT |
| Language | English |
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| References | 4504_CR30 H Zhang (4504_CR26) 2019; 7 TU Consortium (4504_CR5) 2018; 47 R Bunescu (4504_CR11) 2005; 33 4504_CR14 R Raisamo (4504_CR37) 2019; 131 4504_CR13 H Huang (4504_CR20) 2017; 46 4504_CR35 4504_CR32 J Futoma (4504_CR24) 2020; 2 4504_CR33 S Orchard (4504_CR3) 2013; 42 D Szklarczyk (4504_CR18) 2018; 47 S Orchard (4504_CR27) 2012; 9 S Orchard (4504_CR1) 2013; 42 S Orchard (4504_CR4) 2015 E Hüllermeier (4504_CR34) 2021; 110 GR Brown (4504_CR29) 2014; 43 S Pyysalo (4504_CR12) 2007; 8 M Torii (4504_CR17) 2015; 12 4504_CR9 4504_CR28 4504_CR25 N Srivastava (4504_CR31) 2014; 15 L Mottin (4504_CR36) 2017 4504_CR23 4504_CR22 J Lee (4504_CR10) 2019; 36 4504_CR6 4504_CR7 4504_CR8 Q Chen (4504_CR19) 2018 A Franceschini (4504_CR15) 2012; 41 CO Tudor (4504_CR16) 2015 PV Hornbeck (4504_CR21) 2014; 43 GR Mishra (4504_CR2) 2006; 34 |
| References_xml | – year: 2015 ident: 4504_CR4 publication-title: Database doi: 10.1093/database/bav009 – volume: 8 start-page: 50 issue: 1 year: 2007 ident: 4504_CR12 publication-title: BMC Bioinf doi: 10.1186/1471-2105-8-50 – ident: 4504_CR6 – volume: 36 start-page: 1234 issue: 4 year: 2019 ident: 4504_CR10 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btz682 – ident: 4504_CR25 doi: 10.18653/v1/W17-2323 – volume: 34 start-page: 411 year: 2006 ident: 4504_CR2 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkj141 – volume: 43 start-page: 512 issue: D1 year: 2014 ident: 4504_CR21 publication-title: Nucleic Acids Res doi: 10.1093/nar/gku1267 – ident: 4504_CR35 – ident: 4504_CR33 doi: 10.18653/v1/2020.blackboxnlp-1.21 – volume: 12 start-page: 17 issue: 1 year: 2015 ident: 4504_CR17 publication-title: IEEE/ACM Trans Comput Biol Bioinf doi: 10.1109/TCBB.2014.2372765 – volume: 2 start-page: 489 issue: 9 year: 2020 ident: 4504_CR24 publication-title: Lancet Digital Health doi: 10.1016/S2589-7500(20)30186-2 – volume: 7 start-page: 89354 year: 2019 ident: 4504_CR26 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2927253 – volume: 43 start-page: 36 issue: D1 year: 2014 ident: 4504_CR29 publication-title: Nucleic Acids Res doi: 10.1093/nar/gku1055 – ident: 4504_CR30 doi: 10.18653/v1/N19-1423 – ident: 4504_CR7 – ident: 4504_CR22 – volume: 15 start-page: 1929 issue: 56 year: 2014 ident: 4504_CR31 publication-title: J Mach Learn Res – volume: 41 start-page: 808 issue: D1 year: 2012 ident: 4504_CR15 publication-title: Nucleic Acids Res doi: 10.1093/nar/gks1094 – year: 2015 ident: 4504_CR16 publication-title: Database doi: 10.1093/database/bav020 – year: 2017 ident: 4504_CR36 publication-title: Database doi: 10.1093/database/bax040 – ident: 4504_CR13 – volume: 46 start-page: 542 issue: D1 year: 2017 ident: 4504_CR20 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkx1104 – volume: 33 start-page: 139 issue: 2 year: 2005 ident: 4504_CR11 publication-title: Artif Intell Med doi: 10.1016/j.artmed.2004.07.016 – volume: 131 start-page: 131 year: 2019 ident: 4504_CR37 publication-title: Int J Human-Comput Stud. doi: 10.1016/j.ijhcs.2019.05.008 – ident: 4504_CR32 doi: 10.1016/j.knosys.2018.11.020 – volume: 42 start-page: 358 issue: (D1) year: 2013 ident: 4504_CR3 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkt1115 – ident: 4504_CR28 doi: 10.1155/2015/918710 – volume: 47 start-page: 607 issue: D1 year: 2018 ident: 4504_CR18 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gky1131 – volume: 110 start-page: 457 issue: 3 year: 2021 ident: 4504_CR34 publication-title: Mach Learn doi: 10.1007/s10994-021-05946-3 – year: 2018 ident: 4504_CR19 publication-title: Database doi: 10.1093/database/bay122 – ident: 4504_CR9 doi: 10.3115/1690219.1690287 – volume: 9 start-page: 345 issue: 4 year: 2012 ident: 4504_CR27 publication-title: Nature Methods doi: 10.1038/nmeth.1931 – ident: 4504_CR8 – ident: 4504_CR23 doi: 10.18653/v1/2021.eacl-main.113 – ident: 4504_CR14 doi: 10.18653/v1/W17-2304 – volume: 42 start-page: 358 issue: D1 year: 2013 ident: 4504_CR1 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkt1115 – volume: 47 start-page: 506 issue: D1 year: 2018 ident: 4504_CR5 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gky1049 |
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Protein-protein interactions (PPIs) are critical to normal cellular function and are related to many disease pathways. A range of protein functions... Protein-protein interactions (PPIs) are critical to normal cellular function and are related to many disease pathways. A range of protein functions are... Motivation We use the IntAct PPI database to create a distant supervised dataset annotated with interacting protein pairs, their corresponding PTM type, and... Motivation Protein-protein interactions (PPIs) are critical to normal cellular function and are related to many disease pathways. A range of protein functions... Abstract Motivation Protein-protein interactions (PPIs) are critical to normal cellular function and are related to many disease pathways. A range of protein... |
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| SubjectTerms | Algorithms Analysis Annotations Automation BioBERT Bioinformatics Biomedical and Life Sciences Calibration Computational Biology/Bioinformatics Computational linguistics Computer Appl. in Life Sciences Data Mining Datasets Deep learning Distant supervision Enzymes Humans Language processing Life Sciences Machine learning Methods Microarrays Natural language interfaces Natural language processing Noise Phosphorylation Post-translation Post-translational modification Post-translational modifications Predictions Protein interaction Protein Processing, Post-Translational Protein-protein interaction Protein-protein interactions Proteins PubMed Supervision Test sets Translation Uniqueness |
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| Title | Large-scale protein-protein post-translational modification extraction with distant supervision and confidence calibrated BioBERT |
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