On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)

Survey and critique of techniques for extracting rules from trained artificial neural networks

Andrews, R., Diederich, J., & Tickle, A. B. (1995) · Knowledge-Based Systems · doi:10.1016/0950-7051(96)81920-4 · Cited by 1,225

A review of the explainability and safety of conversational agents for mental health to identify avenues for improvement

Sarkar, S., Gaur, M., Chen, L. K., Garg, M., & Srivastava, B. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1229805 · Cited by 38

Text Backdoor Detection Using an Interpretable RNN Abstract Model

Fan, M., Si, Z., Xie, X., Liu, Y., & Liu, T. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3103064 · Cited by 38

Relation-Based Counterfactual Explanations for Bayesian Network Classifiers

Albini, E., Rago, A., Baroni, P., & Toni, F. (2020) · Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/63 · Cited by 39

Data consistency in the English Hospital Episodes Statistics database

Hardy, F., Heyl, J., Tucker, K., Hopper, A., Marchã, M. J., Briggs, T. W. R., Yates, J., Day, J., Wheeler, A., Eve-Jones, S., et al. (2022) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2022-100633 · Cited by 38

Watch Out for Updates: Understanding the Effects of Model Explanation Updates in AI-Assisted Decision Making

Wang, X., & Yin, M. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581366 · Cited by 38

Combining explainable machine learning, demographic and multi-omic data to inform precision medicine strategies for inflammatory bowel disease

Gardiner, L.-J., Carrieri, A. P., Bingham, K., Macluskie, G., Bunton, D., McNeil, M., & Pyzer-Knapp, E. O. (2022) · PLOS ONE · doi:10.1371/journal.pone.0263248 · Cited by 36

Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images

Patkar, S., Chen, A., Basnet, A., Bixby, A., Rajendran, R., Chernet, R., Faso, S., Kumar, P. A., Desai, D., El-Zammar, O., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00765-w · Cited by 41

Global Aggregations of Local Explanations for Black Box models

van der Linden, I., Haned, H., & Kanoulas, E. (2019) · arXiv · doi:10.48550/arxiv.1907.03039 · Cited by 35

A Type-2 Fuzzy Logic Approach to Explainable AI for regulatory compliance, fair customer outcomes and market stability in the Global Financial Sector

Adams, J., & Hagras, H. (2020) · 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz48607.2020.9177542 · Cited by 35

Should learning analytics models include sensitive attributes? Explaining the why

Deho, O. B., Joksimovic, S., Li, J., Zhan, C., Liu, J., & Liu, L. (2023) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2022.3226474 · Cited by 35

Do Not Trust Additive Explanations

Gosiewska, A., & Biecek, P. (2019) · arXiv · doi:10.48550/arxiv.1903.11420 · Cited by 34

Explaining Multi-Criteria Decision Aiding Models with an Extended Shapley Value

Labreuche, C., & Fossier, S. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/46 · Cited by 35

Explaining Deep Learning Models for Spoofing and Deepfake Detection with Shapley Additive Explanations

Ge, W., Patino, J., Todisco, M., & Evans, N. (2022) · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp43922.2022.9747476 · Cited by 40

Shedding Light on Black Box Machine Learning Algorithms: Development of an Axiomatic Framework to Assess the Quality of Methods that Explain Individual Predictions

Honegger, M. (2018) · arXiv · doi:10.48550/arxiv.1808.05054 · Cited by 32

Medications and conditions associated with weight loss in patients prescribed semaglutide based on real‐world data

Powell, W., Song, X., Mohamed, Y., Walsh, D., Parks, E. J., McMahon, T. M., Khan, M., & Waitman, L. R. (2023) · Obesity · doi:10.1002/oby.23859 · Cited by 32

Defending against Backdoors in Federated Learning with Robust Learning Rate

Ozdayi, M. S., Kantarcioglu, M., & Gel, Y. R. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i10.17118 · Cited by 30

Towards mega-scale decarbonized industrial park (Mega-DIP): Generative AI-driven techno-economic and environmental assessment of renewable and sustainable energy utilization in petrochemical industry

Heo, S., Byun, J., Ifaei, P., Ko, J., Ha, B., Hwangbo, S., & Yoo, C. (2024) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.113933 · Cited by 31

Acoustic Features Analysis for Explainable Machine Learning-Based Audio Spoofing Detection

Bisogni, C., Loia, V., Nappi, M., & Pero, C. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104145 · Cited by 30

Machine Learning for Predicting the 3-Year Risk of Incident Diabetes in Chinese Adults

Wu, Y., Hu, H., Cai, J., Chen, R., Zuo, X., Cheng, H., & Yan, D. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.626331 · Cited by 28

A Clinical Decision Support System for Edge/Cloud ICU Readmission Model Based on Particle Swarm Optimization, Ensemble Machine Learning, and Explainable Artificial Intelligence

Alabdulhafith, M., Saleh, H., Elmannai, H., Ali, Z. H., El-Sappagh, S., Hu, J.-W., & El-Rashidy, N. (2023) · IEEE Access · doi:10.1109/access.2023.3312343 · Cited by 27

BERT-Based Natural Language Processing of Drug Labeling Documents: A Case Study for Classifying Drug-Induced Liver Injury Risk

Wu, Y., Liu, Z., Wu, L., Chen, M., & Tong, W. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.729834 · Cited by 26

Extendable and explainable deep learning for pan-cancer radiogenomics research

Liu, Q., & Hu, P. (2022) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.102111 · Cited by 27

Resource Reservation in Sliced Networks: An Explainable Artificial Intelligence (XAI) Approach

Barnard, P., Macaluso, I., Marchetti, N., & DaSilva, L. A. (2022) · ICC 2022 - IEEE International Conference on Communications · doi:10.1109/icc45855.2022.9838766 · Cited by 29

Exploring explainable AI features in the vocal biomarkers of lung disease

Chen, Z., Liang, N., Li, H., Zhang, H., Li, H., Yan, L., Hu, Z., Chen, Y., Zhang, Y., Wang, Y., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108844 · Cited by 26

Elucidation of genome-wide understudied proteins targeted by PROTAC-induced degradation using interpretable machine learning

Xie, L., & Xie, L. (2023) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010974 · Cited by 26

MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset

Meng, C., Trinh, L., Xu, N., & Liu, Y. (2021) · doi:10.21203/rs.3.rs-402058/v1 · Cited by 21

Predicting self-intercepted medication ordering errors using machine learning

King, C. R., Abraham, J., Fritz, B. A., Cui, Z., Galanter, W., Chen, Y., & Kannampallil, T. (2021) · PLOS ONE · doi:10.1371/journal.pone.0254358 · Cited by 22

A Hybrid Explainable AI Framework Applied to Global and Local Facial Expression Recognition

Deramgozin, M., Jovanovic, S., Rabah, H., & Ramzan, N. (2021) · 2021 IEEE International Conference on Imaging Systems and Techniques (IST) · doi:10.1109/ist50367.2021.9651357 · Cited by 22

The Pod People: Understanding Manipulation of Social Media Popularity via Reciprocity Abuse

Weerasinghe, J., Flanigan, B., Stein, A., McCoy, D., & Greenstadt, R. (2020) · Proceedings of The Web Conference 2020 · doi:10.1145/3366423.3380256 · Cited by 23

Ethical framework for responsible foundational models in medical imaging

Jha, D., Durak, G., Das, A., Sanjotra, J., Susladkar, O., Sarkar, S., Rauniyar, A., Kumar Tomar, N., Peng, L., Li, S., et al. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1544501 · Cited by 21

DeepThal: a deep learning-based framework for the large-scale prediction of the α+-thalassemia trait using red blood cell parameters

Phirom, K., Charoenkwan, P., Shoombuatong, W., Charoenkwan, P., Sirichotiyakul, S., & Tongsong, T. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11216305 · Cited by 20

Generative AI, Human Creativity, and Art

Zhou, E., & Lee, D. (2023) · doi:10.2139/ssrn.4594824 · Cited by 19

Forecasting acute kidney injury and resource utilization in ICU patients using longitudinal, multimodal models

Tan, Y., Dede, M., Mohanty, V., Dou, J., Hill, H., Bernstam, E., & Chen, K. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104648 · Cited by 19

Unlocking the Power of the Topic Content in News Headlines: BERTopic for Predicting Chinese Corporate Bond Defaults

Tang, W., Bu, H., Zuo, Y., & Wu, J. (2024) · Finance Research Letters · doi:10.1016/j.frl.2024.105062 · Cited by 19

The power of speech in the wild: discriminative power of daily voice diaries in understanding auditory verbal hallucinations using deep learning

Wang, W., Xu, W., Chander, A., Nepal, S., Buck, B., Pakhomov, S., Cohen, T., Ben-Zeev, D., & Campbell, A. (2023) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3610890 · Cited by 19

An explainable deepfake of speech detection method with spectrograms and waveforms

Yu, N., Chen, L., Leng, T., Chen, Z., & Yi, X. (2024) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2024.103720 · Cited by 18

Discretionary Information in ESG Investing: A Text Analysis of Mutual Fund Prospectuses

Andrikogiannopoulou, A., Krueger, P., Mitali, S. F., & Papakonstantinou, F. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4082263 · Cited by 18

Social Norms on Reddit: A Demographic Analysis

De Candia, S., De Francisci Morales, G., Monti, C., & Bonchi, F. (2022) · 14th ACM Web Science Conference 2022 · doi:10.1145/3501247.3531549 · Cited by 18

OnSIDES database: Extracting adverse drug events from drug labels using natural language processing models

Tanaka, Y., Chen, H. Y., Belloni, P., Gisladottir, U., Kefeli, J., Patterson, J., Srinivasan, A., Zietz, M., Sirdeshmukh, G., Berkowitz, J., et al. (2025) · Med · doi:10.1016/j.medj.2025.100642 · Cited by 17

Federated learning with multi-cohort real-world data for predicting the progression from mild cognitive impairment to Alzheimer's disease

Pan, J., Fan, Z., Smith, G. E., Guo, Y., Bian, J., & Xu, J. (2025) · Alzheimer's & Dementia · doi:10.1002/alz.70128 · Cited by 19

Early identification of bloodstream infection in hemodialysis patients by machine learning

Zhou, T., Ren, Z., Ma, Y., He, L., Liu, J., Tang, J., & Zhang, H. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e18263 · Cited by 17

Visual-based classification models for grading reclaimed structural timber for reuse: A theoretical, numerical and experimental investigation

Pasca, D. P., Aloisio, A., De Santis, Y., Burkart, H., & Øvrum, A. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2024.119218 · Cited by 15

AI-assisted prescreening of biomedical research proposals: ethical considerations and the pilot case of “la Caixa” Foundation

Carbonell Cortés, C., Parra-Rojas, C., Pérez-Lozano, A., Arcara, F., Vargas-Sánchez, S., Fernández-Montenegro, R., Casado-Marín, D., Rondelli, B., & López-Verdeguer, I. (2024) · Data & Policy · doi:10.1017/dap.2024.41 · Cited by 15

Is Genre Enough? A Theory of Genre Signaling as Generative AI Rhetoric

Omizo, R., & Hart-Davidson, B. (2024) · Rhetoric Society Quarterly · doi:10.1080/02773945.2024.2343615 · Cited by 16

AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI

Hiniduma, K., Byna, S., Bez, J. L., & Madduri, R. (2024) · Proceedings of the 36th International Conference on Scientific and Statistical Database Management · doi:10.1145/3676288.3676296 · Cited by 15

Prevent Medical Errors through Artificial Intelligence: A Review

Ahmad, S., & Wasim, S. (2023) · Saudi Journal of Medical and Pharmaceutical Sciences · doi:10.36348/sjmps.2023.v09i07.007 · Cited by 14

Distinguishing Fact from Fiction: A Benchmark Dataset for Identifying Machine-Generated Scientific Papers in the LLM Era.

Mosca, E., Abdalla, M. H. I., Basso, P., Musumeci, M., & Groh, G. (2023) · Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023) · doi:10.18653/v1/2023.trustnlp-1.17 · Cited by 14

Hybrid modeling for in silico optimization of a dynamic perfusion cell culture process

Agarwal, P., McCready, C., Ng, S. K., Ng, J. C., van de Laar, J., Pennings, M., & Zijlstra, G. (2025) · Biotechnology Progress · doi:10.1002/btpr.3503 · Cited by 14

LS-Tree: Model Interpretation When the Data Are Linguistic

Chen, J., & Jordan, M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5749 · Cited by 14

Interpretable Unsupervised Anomaly Detection For RAN Cell Trace Analysis

Chawla, A., Jacob, P., Feghhi, S., Rughwani, D., van der Meer, S., & Fallon, S. (2020) · 2020 16th International Conference on Network and Service Management (CNSM) · doi:10.23919/cnsm50824.2020.9269108 · Cited by 14

Rationale Discovery and Explainable AI

Steging, C., Renooij, S., & Verheij, B. (2021) · Frontiers in Artificial Intelligence and Applications · doi:10.3233/faia210341 · Cited by 14

Testing conditional independence in supervised learning algorithms

Watson, D. S., & Wright, M. N. (2021) · Machine Learning · doi:10.1007/s10994-021-06030-6 · Cited by 13

On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods

Amarasinghe, K., Rodolfa, K. T., Jesus, S., Chen, V., Balayan, V., Saleiro, P., Bizarro, P., Talwalkar, A., & Ghani, R. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i19.30082 · Cited by 13

Combining theoretical modelling and machine learning approaches: The case of teamwork effects on individual effort expenditure

Eisbach, S., Mai, O., & Hertel, G. (2024) · New Ideas in Psychology · doi:10.1016/j.newideapsych.2024.101077 · Cited by 12

Development of an explainable clinical decision support system for the prediction of patient quality of life in amyotrophic lateral sclerosis

Antoniadi, A. M., Galvin, M., Heverin, M., Hardiman, O., & Mooney, C. (2021) · Proceedings of the 36th Annual ACM Symposium on Applied Computing · doi:10.1145/3412841.3441940 · Cited by 12

Gauge fixing for sequence-function relationships

(2025) · doi:10.1371/journal.pcbi.1012818 · Cited by 11

Prediction of Oral Food Challenge Outcomes via Ensemble Learning

Zhang, J., Lee, D., Jungles, K., Shaltis, D., Najarian, K., Ravikumar, R., Sanders, G., & Gryak, J. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.101142 · Cited by 11

XAI-based Clinical Decision Support System: A Systematic Review

Kim, S., Kim, D., Kim, M., Ko, H., & Jeong, O. (2024) · doi:10.20944/preprints202406.0721.v1 · Cited by 72

Machine learning-based strategies for improving healthcare data quality: an evaluation of accuracy, completeness, and reusability

Jarmakovica, A. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1621514 · Cited by 10

Machine Learning Predictions of Recovery in Bilingual Poststroke Aphasia: Aligning Insights With Clinical Evidence

Marte, M. J., Carpenter, E., Scimeca, M., Russell-Meill, M., Peñaloza, C., Grasemann, U., Miikkulainen, R., & Kiran, S. (2025) · Stroke · doi:10.1161/strokeaha.124.047867 · Cited by 9

Fish-inspired tracking of underwater turbulent plumes

Gunnarson, P., & Dabiri, J. O. (2024) · Bioinspiration & Biomimetics · doi:10.1088/1748-3190/ad7181 · Cited by 11

A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making

Schoeffer, J. (2022) · CHI Conference on Human Factors in Computing Systems Extended Abstracts · doi:10.1145/3491101.3503811 · Cited by 9

Detecting and Understanding Textual Deepfakes in Online Reviews

Kowalczyk, P., Röder, M., Dürr, A., & Thiesse, F. (2022) · Proceedings of the Annual Hawaii International Conference on System Sciences · doi:10.24251/hicss.2022.184 · Cited by 9

Interpretable AI/ML for High-stakes Tasks with Human-in-the-loop: Critical Review and Future Trends

Kovalerchuk, B. (2024) · doi:10.21203/rs.3.rs-3989807/v1 · Cited by 9

Explainable vs. Interpretable Artificial Intelligence Frameworks in Oncology

Bertsimas, D., & Margonis, G. A. (2023) · Translational Cancer Research · doi:10.21037/tcr-22-2427 · Cited by 8

Conceptual Explanations of Neural Network Prediction for Time Series

Kusters, F., Schichtel, P., Ahmed, S., & Dengel, A. (2020) · 2020 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207341 · Cited by 8

Effect of endogenous and anthropogenic factors on the alkalinisation and salinisation of freshwater in United States by using explainable machine learning

Wimalagunarathna, N. D., Dharmarathne, G., Ekanayake, I. U., Rathanayake, U., Alwatugoda, J., & Meddage, D. P. P. (2024) · Case Studies in Chemical and Environmental Engineering · doi:10.1016/j.cscee.2024.100919 · Cited by 8

Deciphering Stereotypes in Pre-Trained Language Models

Ma, W., Scheible, H., Wang, B., Veeramachaneni, G., Chowdhary, P., Sun, A., Koulogeorge, A., Wang, L., Yang, D., & Vosoughi, S. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.697 · Cited by 8

Modeling health risks using neural network ensembles

Smith, B. M., Criminisi, A., Sorek, N., Harari, Y., Sood, N., & Heymsfield, S. B. (2024) · PLOS ONE · doi:10.1371/journal.pone.0308922 · Cited by 5

Artificial Intelligence-Assisted Emergency Department Vertical Patient Flow Optimization

Hodgson, N. R., Saghafian, S., Martini, W. A., Feizi, A., & Orfanoudaki, A. (2025) · Journal of Personalized Medicine · doi:10.3390/jpm15060219 · Cited by 6

MedRedQA for Medical Consumer Question Answering: Dataset, Tasks, and Neural Baselines

Nguyen, V., Karimi, S., Rybinski, M., & Xing, Z. (2023) · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.ijcnlp-main.42 · Cited by 5

Automated stratification of trauma injury severity across multiple body regions using multi-modal, multi-class machine learning models

Gao, J., Chen, G., O’Rourke, A. P., Caskey, J., Carey, K. A., Oguss, M., Stey, A., Dligach, D., Miller, T., Mayampurath, A., et al. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae071 · Cited by 5

Redundancy-aware unsupervised ranking based on game theory: Ranking pathways in collections of gene sets

Balestra, C., Maj, C., Müller, E., & Mayr, A. (2023) · PLOS ONE · doi:10.1371/journal.pone.0282699 · Cited by 4

Guiding discovery of protein sequence-structure-function modeling

(2024) · doi:10.1093/bioinformatics/btae002 · Cited by 4

Who does the model learn from?

Charpignon, M.-L., Celi, L. A., & Samuel, M. C. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00057-1 · Cited by 4

BDCN: Semantic Embedding Self-explanatory Breast Diagnostic Capsules Network

Chen, D., Zhong, K., & He, J. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-84186-7_28 · Cited by 3

Rethinking the effects of working memory training on executive functions in schizophrenia: A machine learning approach

Zhang, T., Su, M., Huo, X., & Zhao, X. (2025) · International Journal of Clinical and Health Psychology · doi:10.1016/j.ijchp.2025.100628 · Cited by 3

A large‐scale evolutionary and structural analysis of CLC channels and transporters

Mishra, A., Díaz Vázquez, G., & Robertson, J. L. (2025) · Protein Science · doi:10.1002/pro.70389 · Cited by 3

Development and Validation of Deep Learning‐ and Ensemble Learning‐Based Biological Ages in the NHANES Study

Huang, Y., Yang, X., Wang, Q., Abula, A., Dong, Y., & Li, W. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1532884 · Cited by 3

SimClone: Detecting Tabular Data Clones Using Value Similarity

Yang, X., Rajbahadur, G. K., Lin, D., Wang, S., & Jiang, Z. M. (. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3676961 · Cited by 3

EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: The MonuMAI cultural heritage use case

Díaz-Rodríguez, N., Lamas, A., Sanchez, J., Franchi, G., Donadello, I., Tabik, S., Filliat, D., Cruz, P., Montes, R., & Herrera, F. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.09.022 · Cited by 2

Multimodal Deep Learning Model for Prediction of Prognosis in Central Nervous System Inflammation

Choi, B. K., Choi, Y., Jang, S., Ha, W.-S., Cho, S., Chang, K., Sohn, B., Kim, K. M., & Park, Y. R. (2025) · Brain Communications · doi:10.1093/braincomms/fcaf179 · Cited by 1

Infrared spectrum analysis of organic molecules with neural networks using standard reference data sets in combination with real world data

Punjabi, D., Huang, Y.-C., Holzhauer, L., Tremouilhac, P., Friederich, P., Jung, N., & Bräse, S. (2024) · doi:10.26434/chemrxiv-2024-7twhh · Cited by 1

StackGlyEmbed: prediction of N-linked glycosylation sites using protein language models

Nafi, M. M. I., & Rahman, M. S. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbaf146 · Cited by 1