RAPPPID: towards generalizable protein interaction prediction with AWD-LSTM twin networks
Open accessPublished version of a preprint
Abstract
Motivation: Computational methods for the prediction of protein–protein interactions (PPIs), while important tools for researchers, are plagued by challenges in generalizing to unseen proteins. Datasets used for modelling protein–protein predictions are particularly predisposed to information leakage and sampling biases. Results: In this study, we introduce RAPPPID, a method for the Regularized Automatic Prediction of Protein–Protein Interactions using Deep Learning. RAPPPID is a twin Averaged Weight-Dropped Long Short-Term memory network which employs multiple regularization methods during training time to learn generalized weights. Testing on stringent interaction datasets composed of proteins not seen during training, RAPPPID outperforms state-of-the-art methods. Further experiments show that RAPPPID’s performance holds regardless of the particular proteins in the testing set and its performance is higher for experimentally supported edges. This study serves to demonstrate that appropriate regularization is an important component of overcoming the challenges of creating models for PPI prediction that generalize to unseen proteins. Additionally, as part of this study, we provide datasets corresponding to several data splits of various strictness, in order to facilitate assessment of PPI reconstruction methods by others in the future. Availability and implementation: Code and datasets are freely available at https://github.com/jszym/rapppid and Zenodo.org. Supplementary information: Supplementary data are available at Bioinformatics online.
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.
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
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669 · see citing works
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · 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
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077 · see citing works
(2014) · Cited by 33,419 · see citing works
Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1131 · Cited by 17,749 · see citing works
Principles that Govern the Folding of Protein Chains
Anfinsen, C. B. (1973) · Science · doi:10.1126/science.181.4096.223 · Cited by 7,096 · see citing works
Amino acid substitution matrices from protein blocks.
Henikoff, S., & Henikoff, J. G. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.22.10915 · Cited by 6,218 · see citing works
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061 · see citing works
Kudo, T., & Richardson, J. (2018) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-2012 · Cited by 4,454 · see citing works
Protein complex prediction with AlphaFold-Multimer
Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., et al. (2021) · doi:10.1101/2021.10.04.463034 · Cited by 3,808 · see citing works
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016239118 · Cited by 3,286 · see citing works
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2019) · doi:10.1101/622803 · Cited by 3,280 · see citing works
Recurrent Neural Network RegularizationPreprint
Zaremba, W., Sutskever, I., & Vinyals, O. (2014) · arXiv.org · doi:10.48550/arxiv.1409.2329 · Cited by 3,086 · see citing works
The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions
Hochreiter, S. (1998) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488598000094 · Cited by 2,939 · see citing works
A Critical Review of Recurrent Neural Networks for Sequence LearningPreprint
Lipton, Z. C., Berkowitz, J. A., & Elkan, C. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.00019 · Cited by 2,711 · see citing works
Roux, K. J., Kim, D. I., Raida, M., & Burke, B. (2012) · Journal of Cell Biology · doi:10.1083/jcb.201112098 · Cited by 2,416 · see citing works
The reactome pathway knowledgebase 2022
Gillespie, M. E., Jassal, B., Stephan, R., Orlic-Milacic, M., Rothfels, K., Senff‐Ribeiro, A., Griss, J., Sevilla, C., Matthews, L. R., Gong, C., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1028 · Cited by 2,355 · 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
The reactome pathway knowledgebase
Jassal, B., Matthews, L. R., Viteri, G., Gong, C., Lorente, P., Fabregat, A., Sidiropoulos, K., Cook, J., Gillespie, M. E., Haw, R., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1031 · Cited by 2,111 · see citing works
Signature Verification Using A "Siamese" Time Delay Neural Network
BROMLEY, J., BENTZ, J. W., BOTTOU, L., GUYON, I., LECUN, Y., MOORE, C., SÄCKINGER, E., & SHAH, R. (1993) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001493000339 · Cited by 2,077 · see citing works
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
The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases
Orchard, S. E., Ammari, M. G., Aranda, B., Breuza, L., Briganti, L., Broackes-Carter, F. C., Campbell, N. H., Chavali, G. B., Chen, C. C. L., del‐Toro, N., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1115 · Cited by 1,835 · see citing works
Architecture of the human interactome defines protein communities and disease networks
Huttlin, E. L., Bruckner, R. J., Paulo, J. A., Cannon, J. R., Ting, L., Baltier, K., Colby, G., Gebreab, F., Gygi, M. P., Parzen, H., et al. (2017) · Nature · doi:10.1038/nature22366 · Cited by 1,437 · see citing works
A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data
Jansen, R., Yu, H., Greenbaum, D. S., Kluger, Y., Krogan, N. J., Chung, S., Emili, A., Snyder, M. P., Greenblatt, J. F., & Gerstein, M. (2003) · Science · doi:10.1126/science.1087361 · Cited by 1,254 · see citing works
Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates
Kudo, T. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1007 · Cited by 1,021 · see citing works
Japanese and Korean voice search
Schuster, M., & Nakajima, K. (2012) · 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2012.6289079 · Cited by 862 · see citing works
Averaging Weights Leads to Wider Optima and Better GeneralizationPreprint
Izmailov, P., Podoprikhin, D. A., Garipov, T., Vetrov, D., & Wilson, A. G. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.05407 · Cited by 818 · see citing works
BioLiP: a semi-manually curated database for biologically relevant ligand–protein interactions
Yang, J., Roy, A., & Zhang, Y. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks966 · Cited by 749 · see citing works
Regularizing and Optimizing LSTM Language Models
Merity, S., Keskar, N. S., & Socher, R. (2017) · arXiv · doi:10.48550/arxiv.1708.02182 · Cited by 697 · 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
Mish: A Self Regularized Non-Monotonic Activation Function
Misra, D. (2020) · British Machine Vision Conference (BMVC) · doi:10.5244/c.34.191 · Cited by 683 · see citing works
Kernel methods for predicting protein–protein interactions
Ben‐Hur, A., & Noble, W. S. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti1016 · Cited by 561 · see citing works
Mish: A Self Regularized Non-Monotonic Activation Function
Misra, D. (2019) · arXiv · doi:10.48550/arxiv.1908.08681 · Cited by 541 · see citing works
Protein interaction data curation: the International Molecular Exchange (IMEx) consortium
Orchard, S. E., Kerrien, S., Abbani, S., Aranda, B., Bhate, J., Bidwell, S. L., Bridge, A. J., Briganti, L., Brinkman, F. S. L., Cesareni, G., et al. (2012) · Nature Methods · doi:10.1038/nmeth.1931 · Cited by 538 · see citing works
Overcoming the challenges of membrane protein crystallography
Carpenter, E. P., Beis, K., Cameron, A. D., & Iwata, S. (2008) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2008.07.001 · Cited by 527 · see citing works
HIPPIE v2.0: enhancing meaningfulness and reliability of protein–protein interaction networks
Alanis‐Lobato, G., Andrade-Navarro, M. A., & Schaefer, M. H. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw985 · Cited by 520 · see citing works
THPdb: Database of FDA-approved peptide and protein therapeutics
Usmani, S. S., Bedi, G., Samuel, J. S., Singh, S., Kalra, S., Kumar, P., Ahuja, A. A., Sharma, D. M., Gautam, A., & Raghava, G. P. S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0181748 · Cited by 488 · see citing works
ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised LearningPreprint
Elnaggar, A., Heinzinger, M., Dallago, C., Rehawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.12.199554 · Cited by 409 · see citing works
Multifaceted protein–protein interaction prediction based on Siamese residual RCNN
Chen, M., Ju, C. J. ‐., Zhou, G., Chen, X., Zhang, T., Chang, K.-W., Zaniolo, C., & Wang, W. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz328 · Cited by 375 · see citing works
High-Performance Large-Scale Image Recognition Without NormalizationPreprint
Brock, A., De, S., Smith, S. L., & Simonyan, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.06171 · Cited by 356 · see citing works
Peptide therapeutics: Targeting the undruggable space
Tsomaia, N. (2015) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2015.01.014 · Cited by 348 · see citing works
Transformer protein language models are unsupervised structure learners
Rao, R., Meier, J., Sercu, T., Ovchinnikov, S., & Rives, A. (2020) · doi:10.1101/2020.12.15.422761 · Cited by 341 · see citing works
Fundamentals of protein interaction network mapping
Snider, J. D., Kotlyar, M., Saraon, P., Yao, Z., Jurišica, I., & Štagljar, I. (2015) · Molecular Systems Biology · doi:10.15252/msb.20156351 · Cited by 301 · see citing works
Protein interaction networks revealed by proteome coevolution
Cong, Q., Anishchenko, I., Ovchinnikov, S., & Baker, D. (2019) · Science · doi:10.1126/science.aaw6718 · Cited by 241 · see citing works
Choosing negative examples for the prediction of protein-protein interactions
Ben‐Hur, A., & Noble, W. S. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-s1-s2 · Cited by 240 · see citing works
Flaws in evaluation schemes for pair-input computational predictions
Park, Y., & Marcotte, E. M. (2012) · Nature Methods · doi:10.1038/nmeth.2259 · Cited by 213 · see citing works
Gradient Centralization: A New Optimization Technique for Deep Neural Networks
Yong, H., Huang, J., Hua, X., & Lei Zhang, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58452-8_37 · Cited by 212 · see citing works
iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence
Turner, B., Razick, S., Turinsky, A. L., Vlasblom, J., Crowdy, E., Cho, E., Morrison, K., Donaldson, I. M., & Wodak, S. J. (2010) · Database · doi:10.1093/database/baq023 · Cited by 207 · see citing works
Blohm, P., Frishman, G., Smialowski, P., Goebels, F., Wachinger, B., Ruepp, A., & Frishman, D. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1079 · Cited by 192 · see citing works
Predicting protein-protein interactions via multivariate mutual information of protein sequences
Ding, Y., Tang, J., & Guo, F. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1253-9 · Cited by 143 · see citing works
Boekhout, A. H., Beijnen, J. H., & Schellens, J. H. M. (2011) · The Oncologist · doi:10.1634/theoncologist.2010-0035 · Cited by 127 · see citing works
Novel Protein Therapeutics for Systolic Heart Failure
Chen, H. H., Glockner, J. F., Schirger, J. A., Cataliotti, A., Redfield, M. M., & Burnett, J. C. J. (2012) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2012.07.056 · Cited by 95 · see citing works
The yeast two-hybrid assay: still finding connections after 25 years
Vidal, M., & Fields, S. (2014) · Nature Methods · doi:10.1038/nmeth.3182 · Cited by 78 · see citing works
SPRINT: ultrafast protein-protein interaction prediction of the entire human interactome
Li, Y., & Ilie, L. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1871-x · Cited by 76 · see citing works
Bias tradeoffs in the creation and analysis of protein–protein interaction networks
Gillis, J. A., Ballouz, S., & Pavlidis, P. (2014) · Journal of Proteomics · doi:10.1016/j.jprot.2014.01.020 · Cited by 68 · see citing works
A closer look at cross-validation for assessing the accuracy of gene regulatory networks and models
Tabe-Bordbar, S., Emad, A., Zhao, S. D., & Sinha, S. (2018) · Scientific Reports · doi:10.1038/s41598-018-24937-4 · Cited by 63 · see citing works
Self-citationPIPE4: Fast PPI Predictor for Comprehensive Inter- and Cross-Species Interactomes
Dick, K., Samanfar, B., Barnes, B., Cober, E. R., Mimee, B., Tan, L. H., Molnar, S. J., Biggar, K. K., Golshani, A., Dehne, F., et al. (2020) · Scientific Reports · doi:10.1038/s41598-019-56895-w · Cited by 35 · see citing works
Pertuzumab: A New Targeted Therapy for HER 2‐Positive Metastatic Breast Cancer
Malenfant, S. J., Eckmann, K. R., & Barnett, C. M. (2013) · Pharmacotherapy The Journal of Human Pharmacology and Drug Therapy · doi:10.1002/phar.1338 · Cited by 23 · see citing works
Comparing two deep learning sequence-based models for protein-protein interaction predictionPreprint
Richoux, F., Servantie, C., Borès, C., & Télétchéa, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.06268 · Cited by 23 · see citing works
Long-term delivery of protein and peptide therapeutics for cancer therapies
Sikder, S., Gote, V., Alshamrani, M., Sicotte, J., & Pal, D. (2019) · Expert Opinion on Drug Delivery · doi:10.1080/17425247.2019.1662785 · Cited by 21 · see citing works
Browne, F., Wang, H., Zheng, H., & Azuaje, F. J. (2007) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2007.4375748 · Cited by 20 · see citing works
Ranger21: a synergistic deep learning optimizerPreprint
Wright, L., & Demeure, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.13731 · Cited by 4 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805
Flagged first, then by citations
3 of the 70 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review
Lee, M. (2023) · Molecules · doi:10.3390/molecules28135169 · Cited by 93 · see citing works
Predicting protein-protein interactions in the human proteome
Zhang, J., Humphreys, I. R., Pei, J., Kim, J., Choi, C., Yuan, R., Durham, J., Liu, S., Choi, H.-J., Baek, M., et al. (2025) · Science · doi:10.1126/science.adt1630 · Cited by 87 · see citing works
Democratizing protein language models with parameter-efficient fine-tuning
Sledzieski, S., Kshirsagar, M., Baek, M., Dodhia, R., Lavista Ferres, J., & Berger, B. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2405840121 · Cited by 86 · see citing works
Linguistically inspired roadmap for building biologically reliable protein language models
Vu, M. H., Akbar, R., Robert, P. A., Świątczak, B., Sandve, G. K., Greiff, V., & Haug, D. T. T. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00637-1 · Cited by 57 · see citing works
Li, S., Wu, S., Wang, L., Li, F., Jiang, H., & Bai, F. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102344 · Cited by 42 · see citing works
Recent advances in deep learning for protein-protein interaction: a review
Cui, J., Yang, S., Yi, L., Xi, Q., Yang, D., & Zuo, Y. (2025) · BioData Mining · doi:10.1186/s13040-025-00457-6 · Cited by 34 · see citing works
Subgraph-Aware Graph Kernel Neural Network for Link Prediction in Biological Networks
Li, M., Wang, Z., Liu, L., Liu, X., & Zhang, W. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3390092 · Cited by 23 · see citing works
Zhou, K., Lei, C., Zheng, J., Huang, Y., & Zhang, Z. (2023) · Plant Methods · doi:10.1186/s13007-023-01119-6 · Cited by 21 · see citing works
Pitfalls of machine learning models for protein–protein interaction networks
Lannelongue, L., & Inouye, M. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae012 · Cited by 16 · see citing works
High-accuracy protein complex structure modeling based on sequence-derived structure complementarity
Hou, M., Xia, Y., Wang, P., Lv, Z., Hou, D., Zhou, X., Zeng, J., & Zhang, G. (2025) · Nature Communications · doi:10.1038/s41467-025-65090-7 · Cited by 13 · see citing works
Protein–Protein Interaction Prediction Model Based on ProtBert-BiGRU-Attention
Gao, Q., Zhang, C., Li, M., & Yu, T. (2024) · Journal of Computational Biology · doi:10.1089/cmb.2023.0297 · Cited by 12 · see citing works
Luo, Y., & Cai, J. (2025) · Current Proteomics · doi:10.2174/0115701646374018250227075247 · Cited by 13 · see citing works
Growing ecosystem of deep learning methods for modeling protein–protein interactions
Rogers, J. R., Nikolényi, G., & AlQuraishi, M. (2023) · Protein Engineering Design and Selection · doi:10.1093/protein/gzad023 · Cited by 9 · see citing works
Szymborski, J., & Emad, A. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae405 · Cited by 8 · see citing works
Self-citationA flaw in using pretrained protein language models in protein–protein interaction inference models
Szymborski, J., & Emad, A. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-025-01176-7 · Cited by 8 · see citing works
Self-citationSPIDER: constructing cell-type-specific protein–protein interaction networks
Kupershmidt, Y., Kasif, S., & Sharan, R. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae130 · Cited by 7 · see citing works
Chatterjee, A., Ravandi, B., Haddadi, P., Philip, N. H., Abdelmessih, M., Mowrey, W. R., Ricchiuto, P., Liang, Y., Ding, W., Mobarec, J. C., et al. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf148 · Cited by 7 · see citing works
Deep learning methods for proteome-scale interaction prediction
Yoon, M. S., Bae, B., Kim, K. H., Park, H., & Baek, M. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102981 · Cited by 5 · see citing works
Research on Data Cleaning Algorithm Based on Multi Type Construction Waste
Wang, P., Liu, Y., Sun, Q., Bai, Y., & Li, C. (2022) · Sustainability · doi:10.3390/su141912286 · Cited by 4 · see citing works
Pitfalls of machine learning models for protein-protein interactionsPreprint
Lannelongue, L., & Inouye, M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.07.479382 · Cited by 4 · see citing works
LPBERT: A Protein–Protein Interaction Prediction Method Based on a Pre-Trained Language Model
Hu, A., Kuang, L., & Yang, D. (2025) · Applied Sciences · doi:10.3390/app15063283 · Cited by 3 · see citing works
Sarkar, D., & Sarkar, C. (2025) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2025.141102 · Cited by 2 · see citing works
Liu, X., Qu, H., Huang, C., Meng, L., Chen, Q., & Wang, Q. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e25992 · Cited by 2 · see citing works
Memory-efficient, accelerated protein interaction inference with blocked, multi-GPU D-SCRIPT
Schäffer, D. E., Sledzieski, S., Cowen, L., & Berger, B. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf564 · Cited by 1 · see citing works
Alam, A., Fernández-Cabezudo, M. J., Hamoudi, R., & al-Ramadi, B. K. (2026) · npj Systems Biology and Applications · doi:10.1038/s41540-026-00698-z · Cited by 3 · see citing works
Decoding the Functional Interactome of Non-model Organisms with PHILHARMONIC
Sledzieski, S., Versavel, C., Singh, R., Ocitti, F., Devkota, K., Kumar, L., Shpilker, P., Roger, L. M., Yang, J., Lewinski, N. A., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-90252-9_19
Emad, A., & Hostallero, D. E. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4566-6_15
Self-citationAsh, J., Francino-Urdaniz, I. M., Kells, S. P., Davis, C. N., Whitehead, T. A., & Khare, S. D. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.04.686550
Zhou, K., Lei, C., Zheng, J., Huang, Y., & Zhang, Z. (2023) · Figshare · doi:10.6084/m9.figshare.24770258
PPIGAN: Prediction of Protein–Protein Interactions Using Generative Adversarial Networks
Zhang, X., Xue, S., Geng, J., Wen, X., Lai, L., Huang, L., & Yu, J. (2026) · Journal of Computational Biology · doi:10.1177/15578666261453608
GATSBI: improving context-aware protein embeddings through biologically motivated data splits
Nayar, G., & Altman, R. B. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag233
Auditing Protein-Protein Interaction Signals with Sparse Autoencoder FingerprintsPreprint
Zhu, W., Wang, S., Liu, X., Xue, Y., Shen, H.-B., Chen, B., & Pan, X. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.27.754758
Constructed Negatives Shape the Evaluation of Sequence-Based Protein–Protein Interaction ModelsPreprint
Durand, E., Grudinin, S., Durand, B., & Laine, É. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.08.757593
Vijayanand, M., & Patil, K. K. (2026) · International Journal of Peptide Research and Therapeutics · doi:10.1007/s10989-026-10870-y
- Authors
- Szymborski, J., & Emad, A.
- Affiliations
- Mila - Quebec Artificial Intelligence Institute (CA)
- Corresponding author
- Emad, Amin
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-06-30
- Volume
- 38
- Issue
- 16
- Pages
- 3958-3967
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W4283716699
- Cited by
- 34
- References
- 70
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 7 · 2023: 5 · 2022: 3
- FWCI
- 2.21
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Bioinformatics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-04460)
- McGill Initiative in Computational Medicine
- 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, Siamese network, regularization, information leakage, sampling bias, deep learning
- MeSH terms
- Cell Communication, Proteins, Neural Networks, Computer
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
