Structure-Based Deep Learning Framework for Modeling Human–Gut Bacterial Protein Interactions
Open accessAbstract
Background: The interaction network between the human host proteins and the proteins of the gut bacteria is essential for the establishment of human health, and its dysregulation directly contributes to disease development. Despite its great importance, experimental data on protein–protein interactions (PPIs) between these species are sparse due to experimental limitations. Methods: This study presents a deep learning-based framework for predicting PPIs between human and gut bacterial proteins using structural data. The framework leverages graph-based protein representations and variational autoencoders (VAEs) to extract structural embeddings from protein graphs, which are then fused through a Bi-directional Cross-Attention module to predict interactions. The model addresses common challenges in PPI datasets, such as class imbalance, using focal loss to emphasize harder-to-classify samples. Results: The results demonstrated that this framework exhibits robust performance, with high precision and recall across validation and test datasets, underscoring its generalizability. By incorporating proteoforms in the analysis, the model accounts for the structural complexity within proteomes, making predictions biologically relevant. Conclusions: These findings offer a scalable tool for investigating the interactions between the host and the gut microbiota, potentially yielding new treatment targets and diagnostics for disorders linked to the microbiome.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Host-microbe interaction modeling using systems biology, metabolic network reconstruction, and in silico simulations provides insight into potential functional impacts and strain-specific mechanisms, supporting hypothesis generation before clinical validation.
“Host–microbe interaction modeling, using systems biology, metabolic network reconstruction, and in silico simulations, provides insight into potential functional impacts and strain-specific mechanisms, supporting hypothesis generation before clinical validation [158].”
From Current Concepts in Probiotic Safety and Efficacy (2026) · cites this paper as [158]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888 · see citing works
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
STRING v10: protein–protein interaction networks, integrated over the tree of life
Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K. P., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1003 · Cited by 10,436 · see citing works
Pfam: The protein families database in 2021
Mistry, J., Chuguransky, S., Williams, L., Qureshi, M., Salazar, G. A., Sonnhammer, E. L. L., Tosatto, S. C. E., Paladin, L., Raj, S., Richardson, L. J., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa913 · Cited by 7,452 · see citing works
The human microbiome: at the interface of health and disease
Cho, I., & Blaser, M. J. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3182 · Cited by 3,449 · see citing works
Human Protein Reference Database--2009 update
Prasad, T. S. K., Goel, R., Kandasamy, K., Keerthikumar, S., Kumar, S., Mathivanan, S., Telikicherla, D., Raju, R., Shafreen, R. B., Venugopal, A., et al. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn892 · Cited by 3,241 · see citing works
Byrd, A. L., Belkaid, Y., & Segre, J. A. (2018) · Nature Reviews Microbiology · doi:10.1038/nrmicro.2017.157 · Cited by 2,806 · see citing works
The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes
van Zundert, G. C. P., Rodrigues, J. P. G. L. M., Trellet, M., Schmitz, C., Kastritis, P. L., Karaca, E., Melquiond, A. S. J., van Dijk, M., de Vries, S. J., & Bonvin, A. M. J. J. (2016) · Journal of Molecular Biology · doi:10.1016/j.jmb.2015.09.014 · Cited by 2,785 · see citing works
The gut-brain axis: interactions between enteric microbiota, central and enteric nervous systems.
(2015) · PubMed Central · Cited by 2,540 · see citing works
Role of the gut microbiota in nutrition and health
Valdes, A. M., Walter, J., Segal, E., & Spector, T. D. (2018) · BMJ · doi:10.1136/bmj.k2179 · Cited by 2,375 · see citing works
The Database of Interacting Proteins: 2004 update
Salwinski, L. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh086 · Cited by 2,271 · see citing works
Varadi, M., Bertoni, D., Magana, P., Paramval, U., Pidruchna, I., Radhakrishnan, M., Tsenkov, M., Nair, S., Mirdita, M., Yeo, J., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad1011 · Cited by 2,052 · cited 1× · see citing works
“This similarity between the test and the validation outcomes suggests that the model e ff ectively captures the underlying pa tt erns within the data, minimizing over fi tt ing and validating its robustness in unseen scenarios.”
Oughtred, R., Rust, J., Chang, C., Breitkreutz, B., Stark, C., Willems, A., Boucher, L., Leung, G., Kolas, N., Zhang, F., et al. (2021) · Protein Science · doi:10.1002/pro.3978 · Cited by 1,858 · see citing works
ZDOCK server: interactive docking prediction of protein–protein complexes and symmetric multimers
Pierce, B. G., Wiehe, K. J., Hwang, H., Kim, B., Vreven, T., & Weng, Z. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu097 · Cited by 1,767 · see citing works
Microbiota–gut–brain axis and its therapeutic applications in neurodegenerative diseases
Loh, J. S., Mak, W. Q., Tan, L. K. S., Ng, C. X., Chan, H. H., Yeow, S. H., Foo, J. B., Ong, Y. S., How, C. W., & Khaw, K. Y. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01743-1 · Cited by 1,193 · see citing works
MINT, the molecular interaction database: 2012 update
Licata, L., Briganti, L., Peluso, D., Perfetto, L., Iannuccelli, M., Galeota, E., Sacco, F., Palma, A., Nardozza, A. P., Santonico, E., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr930 · Cited by 1,062 · see citing works
Gut microbiota and cardiovascular disease.
Witkowski, M., Weeks, T. L., & Hazen, S. L. (2020) · Circulation Research · doi:10.1161/circresaha.120.316242 · Cited by 1,051 · see citing works
Improved prediction of protein-protein interactions using AlphaFold2
Bryant, P., Pozzati, G., & Elofsson, A. (2021) · doi:10.1101/2021.09.15.460468 · Cited by 1,025 · see citing works
Performance and Its Limits in Rigid Body Protein-Protein Docking
Desta, I., Porter, K. A., Xia, B., Kozakov, D., & Vajda, S. (2020) · Structure · doi:10.1016/j.str.2020.06.006 · Cited by 697 · see citing works
The oral microbiome: diversity, biogeography and human health
Baker, J. L., Mark Welch, J. L., Kauffman, K. M., McLean, J. S., & He, X. (2024) · Nature Reviews Microbiology · doi:10.1038/s41579-023-00963-6 · Cited by 661 · see citing works
Emerging Roles for the Gut Microbiome in Autism Spectrum Disorder
Vuong, H. E., & Hsiao, E. Y. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2016.08.024 · Cited by 595 · see citing works
Targeting the gut and tumor microbiota in cancer
Park, E. M., Chelvanambi, M., Bhutiani, N., Kroemer, G., Zitvogel, L., & Wargo, J. A. (2022) · Nature Medicine · doi:10.1038/s41591-022-01779-2 · Cited by 496 · see citing works
Inferring Domain–Domain Interactions From Protein–Protein InteractionsPreprint
Deng, M., Mehta, S., Sun, F., & Chen, T. (2002) · Genome Research · doi:10.1145/565196.565211 · Cited by 475 · see citing works
Hypothesis testing, type I and type II errors
Ak, B., Chitnis, U., Jadhav, S. L., Bhawalkar, J., & Chaudhury, S. (2009) · Industrial Psychiatry Journal · doi:10.4103/0972-6748.62274 · Cited by 437 · see citing works
The IntAct database: efficient access to fine-grained molecular interaction data
del Toro, N., Shrivastava, A., Ragueneau, E., Meldal, B., Combe, C., Barrera, E., Perfetto, L., How, K., Ratan, P., Shirodkar, G., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1006 · Cited by 405 · see citing works
Prediction of protein–protein interactions using random decision forest framework
Chen, X., & Liu, M. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti721 · Cited by 386 · see citing works
Sequence-based prediction of protein protein interaction using a deep-learning algorithm
Sun, T., Zhou, B., Lai, L., & Pei, J. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1700-2 · Cited by 376 · see citing works
3did: a catalog of domain-based interactions of known three-dimensional structure
Mosca, R., Ceol, A. G. M., Stein, A., Olivella, R., & Aloy, P. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt887 · Cited by 307 · see citing works
SIFTS: Structure Integration with Function, Taxonomy and Sequences resource
Velankar, S. S., Dana, J. M., Jacobsen, J. O. B., van Ginkel, G., Gane, P. J., Luo, J., Oldfield, T. J., O’Donovan, C., Martin, M. J., & Kleywegt, G. J. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1258 · Cited by 277 · see citing works
HPIDB 2.0: a curated database for host–pathogen interactions
Ammari, M., Gresham, C. R., McCarthy, F., & Nanduri, B. (2016) · Database J. Biol. Databases Curation · doi:10.1093/database/baw103 · Cited by 270 · see citing works
Dana, J., Gutmanas, A., Tyagi, N., Qi, G., O'Donovan, C., Martin, M., & Velankar, S. (2018) · Nucleic Acids Res. · doi:10.1093/nar/gky1114 · Cited by 258 · see citing works
The Relationship Between Gut Microbiota and Inflammatory Diseases: The Role of Macrophages
Wang, J., Chen, W., & Wang, Y. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.01065 · Cited by 256 · see citing works
Introduction to host microbiome symbiosis in health and disease
Malard, F., Dore, J. M., Gaugler, B., & Mohty, M. (2020) · Mucosal Immunology · doi:10.1038/s41385-020-00365-4 · Cited by 211 · see citing works
The Dynamic Interplay between the Gut Microbiota and Autoimmune Diseases
Xu, H., Liu, M., Cao, J., Li, X., Fan, D., Xia, Y., Lu, X., Li, J.-T., Ju, D., & Zhao, H. (2019) · Journal of Immunology Research · doi:10.1155/2019/7546047 · Cited by 197 · see citing works
PHISTO: pathogen-host interaction search tool
Tekir, S. D., Çakır, T., Ardiç, E., Sayilirbas, A. S., Konuk, G., Konuk, M., Sariyer, H., Ugurlu, A., Karadeniz, I., Özgür, A., et al. (2013) · Bioinform. · doi:10.1093/bioinformatics/btt137 · Cited by 175 · see citing works
Discovery and significance of protein-protein interactions in health and disease
Greenblatt, J. F., Alberts, B., & Krogan, N. J. (2024) · Cell · doi:10.1016/j.cell.2024.10.038 · Cited by 171 · see citing works
HPIDB - a unified resource for host-pathogen interactions
Kumar, R., & Nanduri, B. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-s6-s16 · Cited by 160 · see citing works
The Gut Microbiome and Schizophrenia: The Current State of the Field and Clinical Applications
Szeligowski, T., Yun, A. L., Lennox, B., & Burnet, P. W. J. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00156 · Cited by 157 · see citing works
LightDock: a new multi-scale approach to protein–protein docking
Jiménez‐García, B., Roel‐Touris, J., Romero‐Durana, M., Vidal, M., Jiménez-González, D., & Fernández‐Recio, J. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx555 · Cited by 139 · see citing works
Dyer, M. D., Neff, C. D., DUFFORD, M. T. W., Rivera, C. G., Shattuck, D., Bassaganya-Riera, J., Murali, T. M., & Sobral, B. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012089 · Cited by 137 · see citing works
Modeling protein‐protein, protein‐peptide, and protein‐oligosaccharide complexes: CAPRI 7th edition
Lensink, M. F., Nadzirin, N., Velankar, S. S., & Wodak, S. J. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25870 · Cited by 136 · see citing works
Host-Microbe Protein Interactions during Bacterial Infection
Schweppe, D. K., Harding, C. L., Chavez, J. D., Wu, X., Ramage, E. R., Singh, P. K., Manoil, C., & Bruce, J. E. (2015) · Chemistry & Biology · doi:10.1016/j.chembiol.2015.09.015 · Cited by 135 · see citing works
Proteomes Are of Proteoforms: Embracing the Complexity
Carbonara, K., Andonovski, M., & Coorssen, J. R. (2021) · Proteomes · doi:10.3390/proteomes9030038 · Cited by 115 · see citing works
Das, S., & Chakrabarti, S. (2021) · Scientific Reports · doi:10.1038/s41598-020-80900-2 · Cited by 110 · see citing works
The Microbiome of the Nose—Friend or Foe?
Dimitri‐Pinheiro, S., Soares, R. A., & Barata, P. (2020) · Allergy & Rhinology · doi:10.1177/2152656720911605 · Cited by 101 · see citing works
The Proteins API: accessing key integrated protein and genome information
Nightingale, A., Antunes, R., Alpi, E., Bursteinas, B., Gonzales, L. J. D. C., Liu, W., Luo, J., Qi, G., Turner, E., & Martin, M. J. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx237 · Cited by 97 · see citing works
A domain-based approach to predict protein-protein interactions
Singhal, M., & Resat, H. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-199 · Cited by 92 · see citing works
Predicting domain-domain interactions using a parsimony approach
Guimarães, K. S., Jothi, R., Zotenko, E., & Przytycka, T. M. (2006) · Genome biology · doi:10.1186/gb-2006-7-11-r104 · Cited by 90 · see citing works
Global mapping of Salmonella enterica-host protein-protein interactions during infection
Walch, P., Selkrig, J., Knodler, L. A., Rettel, M., Stein, F., Fernandez, K. C., Viéitez, C., Potel, C. M., Scholzen, K., Geyer, M., et al. (2021) · Cell Host & Microbe · doi:10.1016/j.chom.2021.06.004 · Cited by 83 · see citing works
Yu, J., Vavruša, M., Andréani, J., Rey, J., Tufféry, P., & Guérois, R. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw340 · Cited by 80 · see citing works
Baranwal, M., Magner, A., Saldinger, J. C., Emre, E. S. T., Elvati, P., Kozarekar, S. S., VanEpps, J. S., Kotov, N. A., Violi, A., & Hero, A. O. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04910-9 · Cited by 79 · see citing works
Khlevner, J., Park, Y., & Margolis, K. G. (2018) · Gastroenterology Clinics of North America · doi:10.1016/j.gtc.2018.07.002 · Cited by 70 · see citing works
Hu, X., Feng, C., Zhou, Y., Harrison, A., & Chen, M. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab737 · Cited by 67 · see citing works
Huo, T., Liu, W., Guo, Y., Yang, C., Lin, J., & Rao, Z. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0535-y · Cited by 56 · see citing works
New advances in cross-linking mass spectrometry toward structural systems biology
Yu, C., & Huang, L. (2023) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2023.102357 · Cited by 53 · see citing works
Yang, F., Lei, Y., Zhou, M., Yao, Q., Han, Y., Wu, X. Y., Zhong, W., Zhu, C., Xu, W., Tao, R. N., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1173 · Cited by 49 · see citing works
ProtInteract: A deep learning framework for predicting protein–protein interactions
Soleymani, F., Paquet, E., Viktor, H. L., Michalowski, W. J., & Spinello, D. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.01.028 · Cited by 39 · see citing works
The Crosstalk Between Gut Microbiota and Obesity and Related Metabolic Disorders
Xu, W., Nie, Y., Yang, Z., & Lü, N. (2016) · Future Microbiology · doi:10.2217/fmb-2015-0024 · Cited by 34 · see citing works
Singh, N. L., Bhatia, V., Singh, S., & Bhatnagar, S. (2019) · Scientific Reports · doi:10.1038/s41598-019-40704-5 · Cited by 31 · see citing works
Apostolos, A. J., Chordia, M. D., Kolli, S. H., Dalesandro, B. E., Rutkowski, M. R., & Pires, M. M. (2022) · Cell chemical biology · doi:10.1016/j.chembiol.2022.11.010 · Cited by 28 · see citing works
Alborzi, S. Z., Nacer, A. A., Najjar, H., Ritchie, D. W., & Devignes, M. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008844 · Cited by 27 · see citing works
Photo-ANA enables profiling of host–bacteria protein interactions during infection
Li, X., Huang, S., & Li, X. (2023) · Nature Chemical Biology · doi:10.1038/s41589-022-01245-7 · Cited by 20 · see citing works
Sciuto, M. R., Warnken, U. H., Schnölzer, M., Valvo, C., Brunetto, L., Boe, A., Biffoni, M., Krammer, P. H., Maria, R. D., & Haas, T. L. (2017) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o116.065920 · Cited by 19 · see citing works
Acharya, D., & Dutta, T. K. (2021) · Scientific Reports · doi:10.1038/s41598-020-80549-x · Cited by 17 · see citing works
Structural insight into protein–protein interactions between intestinal microbiome and host
Post, S. E., & Brito, I. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102354 · Cited by 10 · see citing works
Global and temporal state of the human gut microbiome in health and diseasePreprint
Shoaie, S., Lee, S., Almeida, M., Bidkhori, G., Pons, N., Oñate, F. P., Chatelier, E. L., Begum, N., Proffitt, C., Rosário, D., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-339282/v1 · Cited by 9 · see citing works
Kim, M., Fèvre, C., Lavina, M., Disson, O., & Lecuit, M. (2023) · STAR Protocols · doi:10.1016/j.xpro.2023.102773 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [42]Wu L. Tian Y. Huang Y. Li S. Lin H. Chawla N.V. Li S.Z. Mape-ppi: Towards effective and efficient protein-protein interaction prediction via microenvironment-aware protein embedding arXiv 2024 2402.14391
Cited by
Sorted by citations
Current Concepts in Probiotic Safety and Efficacy
Churin, A. A., Sokolyanskaya, L. O., Lukina, A. P., & Karnachuk, O. V. (2026) · Nutrients · doi:10.3390/nu18040696 · Cited by 31 · see citing works
“Host–microbe interaction modeling, using systems biology, metabolic network reconstruction, and in silico simulations, provides insight into potential functional impacts and strain-specific mechanisms, supporting hypothesis generation before clinical validation [158].”
Jo, D.-M., Ko, S.-C., Kim, K. W., Yang, D., Kim, J.-Y., Oh, G.-W., Choi, G., Lee, D.-S., Tabassum, N., Kim, Y.-M., et al. (2025) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2025.105309 · Cited by 20 · see citing works
Shukla, A. K., Jha, R. K., Choudhary, P., Panwar, S., Maithani, D., & Kumar, A. (2025) · In Silico Research in Biomedicine · doi:10.1016/j.insi.2025.100055 · Cited by 6 · see citing works
Modulating the human gut microbiome-host system: a new drug discovery paradigm
Colmenarejo, G. (2026) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2026.1898583
- Authors
- Kiouri, D. P., Batsis, G. C., & Chasapis, C. T.
- Affiliations
- National and Kapodistrian University of Athens (GR)
- National Hellenic Research Foundation (GR)
- Corresponding author
- Chasapis, Christos T.
- Venue
- Proteomes
- Publisher
- MDPI AG
- Published
- 2025-02-17
- Volume
- 13
- Issue
- 1
- Pages
- 10
- ISSN
- 2227-7382 (ISSN-L)
- PMC
- PMC11843979
- OpenAlex
- W4407642374
- Cited by
- 3
- References
- 68
- Citations by year
- 2026: 1 · 2025: 2
- FWCI
- 0.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Proteomes (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- National Hellenic Research Foundation Helios Repository (National Hellenic Research Foundation) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Research Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- protein-protein interaction prediction, protein structure, variational autoencoder, cross-attention fusion, class imbalance, focal loss, proteoforms, gut microbiota
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
