Deep Learning in Biomedical Data Science

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318

You Only Look Once: Unified, Real-Time Object Detection

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570

A logical calculus of the ideas immanent in nervous activity

McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555

Dermatologist-level classification of skin cancer with deep neural networks

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362

Approximation by superpositions of a sigmoidal function

Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883

The perceptron: A probabilistic model for information storage and organization in the brain.

Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280

Untitled work

(2012) · Cited by 9,062

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697

Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning

Shao, S., McAleer, S., Yan, R., & Baldi, P. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2864759 · Cited by 1,690

Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy

Urban, G., Tripathi, P. V., Alkayali, T., Mittal, M., Jalali, F., Karnes, W. E., & Baldi, P. (2018) · Gastroenterology · doi:10.1053/j.gastro.2018.06.037 · Cited by 763

Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review

Antoniadi, A. M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B. A., & Mooney, C. (2021) · Applied Sciences · doi:10.3390/app11115088 · Cited by 629

Deep learning in biomedicine

Wainberg, M., Merico, D., Delong, A., & Frey, B. J. (2018) · Nature Biotechnology · doi:10.1038/nbt.4233 · Cited by 596

Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning

Talukder, M. A., Islam, M. M., Uddin, M. A., Akhter, A., Hasan, K. F., & Moni, M. A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117695 · Cited by 330

A Review of Challenges and Opportunities in Machine Learning for Health

Ghassemi, M., Naumann, T., Schulam, P., Beam, A. L., Chen, I. Y., & Ranganath, R. (2018) · PubMed · doi:10.48550/arxiv.1806.00388 · Cited by 368

Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence

Alexandrov, T. (2020) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-011420-031537 · Cited by 308

Deep Learning: The Good, the Bad, and the Ugly

Serre, T. (2019) · Annual Review of Vision Science · doi:10.1146/annurev-vision-091718-014951 · Cited by 266

Machine learning for microbiologists

Asnicar, F., Thomas, A. M., Passerini, A., Waldron, L., & Segata, N. (2023) · Nature Reviews Microbiology · doi:10.1038/s41579-023-00984-1 · Cited by 284

Solvent Accessibility of Residues Undergoing Pathogenic Variations in Humans: From Protein Structures to Protein Sequences

Savojardo, C., Manfredi, M., Martelli, P. L., & Casadio, R. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.626363 · Cited by 166

Deep Learning Analysis of Vibrational Spectra of Bacterial Lysate for Rapid Antimicrobial Susceptibility Testing

Thrift, W. J., Ronaghi, S., Samad, M., Wei, H. L., Nguyen, D. G., Cabuslay, A. S., Groome, C. E., Santiago, P. J., Baldi, P., Hochbaum, A. I., et al. (2020) · ACS Nano · doi:10.1021/acsnano.0c05693 · Cited by 151

Light neural network with fewer parameters based on CNN for fault diagnosis of rotating machinery

Jin, T., Yan, C., Chen, C., Yang, Z., Tian, H., & Wang, S. (2021) · Measurement · doi:10.1016/j.measurement.2021.109639 · Cited by 123

Deep Learning for Biospectroscopy and Biospectral Imaging: State-of-the-Art and Perspectives

He, H., Yan, S., Lyu, D., Xu, M., Ye, R., Zheng, P., Lu, X., Wang, L., & Ren, B. W. (2021) · Analytical Chemistry · doi:10.1021/acs.analchem.0c04671 · Cited by 114

All-optical graph representation learning using integrated diffractive photonic computing units

Yan, T., Yang, R., Zheng, Z., Lin, X., Xiong, H., & Dai, Q. (2022) · Science Advances · doi:10.1126/sciadv.abn7630 · Cited by 109

The capacity of feedforward neural networks

Baldi, P., & Vershynin, R. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.04.009 · Cited by 83

Osseosurface electronics—thin, wireless, battery-free and multimodal musculoskeletal biointerfaces

Cai, L., Burton, A., Gonzales, D. A., Kasper, K. A., Azami, A., Peralta, R., Johnson, M., Bakall, J. A., Villalobos, E. B., Ross, E. C., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27003-2 · Cited by 73

Deep learning for mining protein data

Shi, Q., Chen, W., Huang, S., Wang, Y., & Xue, Z. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz156 · Cited by 68

Deep Learning in Diverse Intelligent Sensor Based Systems

Zhu, Y., Wang, M., Yin, X., Zhang, J., Meijering, E. H. W., & Hu, J. (2022) · Sensors · doi:10.3390/s23010062 · Cited by 73

iResponse: An AI and IoT-Enabled Framework for Autonomous COVID-19 Pandemic Management

Alam, F., Almaghthawi, A., Katib, I. A., Albeshri, A. A., & Mehmood, R. (2021) · Sustainability · doi:10.3390/su13073797 · Cited by 68

Classifying shoulder implants in X-ray images using deep learning

Urban, G., Porhemmat, S., Stark, M., Feeley, B. T., Okada, K., & Baldi, P. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.04.005 · Cited by 63

Development and validation of an interpretable neural network for prediction of postoperative in-hospital mortality

Lee, C. K., Samad, M., Hofer, I. S., Cannesson, M. P., & Baldi, P. (2021) · npj Digital Medicine · doi:10.1038/s41746-020-00377-1 · Cited by 64

iCYP-MFE: Identifying Human Cytochrome P450 Inhibitors Using Multitask Learning and Molecular Fingerprint-Embedded Encoding

Nguyen‐Vo, T., Trinh, Q. H., Nguyen, L., Nguyen-Hoang, P.-U., Nguyen, T.-N., Nguyen, D. T., Nguyen, B. P., & Le, L. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00628 · Cited by 50

Decoding the Human Face: Progress and Challenges in Understanding the Genetics of Craniofacial Morphology

Naqvi, S., Hoskens, H., Wilke, F., Weinberg, S. M., Shaffer, J. R., Walsh, S., Shriver, M. D., Wysocka, J., & Claes, P. D. (2022) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-120121-102607 · Cited by 53

Deep learning algorithms for the early detection of breast cancer: A comparative study with traditional machine learning

Martinez, R. M. G., & van Dongen, D.-M. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101317 · Cited by 53

TAAWUN: a Decision Fusion and Feature Specific Road Detection Approach for Connected Autonomous Vehicles

Alam, F., Mehmood, R., Katib, I. A., Altowaijri, S. M., & Albeshri, A. (2019) · Journal on spesial topics in mobile networks and applications · doi:10.1007/s11036-019-01319-2 · Cited by 40

Preparing Next-Generation Scientists for Biomedical Big Data: Artificial Intelligence Approaches

Moore, J. H., Boland, M. R., Cámara, P. G., Chervitz, H., Gonzalez‐Hernandez, G., Himes, B. E., Kim, D., Mowery, D. L., Ritchie, M. D., Shen, L., et al. (2019) · Personalized Medicine · doi:10.2217/pme-2018-0145 · Cited by 40

Microbiology in the era of artificial intelligence: transforming medical and pharmaceutical microbiology

Tsitou, V.-M., Rallis, D., Cekova, M., & Yanev, N. (2024) · Biotechnology & Biotechnological Equipment · doi:10.1080/13102818.2024.2349587 · Cited by 49

Synergy Effect between Convolutional Neural Networks and the Multiplicity of SMILES for Improvement of Molecular PredictionPreprint

Kimber, T. B., Engelke, S., Tetko, I. V., Bruno, E. N., & Godin, G. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.04439 · Cited by 35

AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation

Joshi, S. D., Urteaga, I., van Amsterdam, W. A. C., Hripcsak, G. M., Elias, P. A., Recht, B., Elhadad, N., Fackler, J. C., Sendak, M., Wiens, J., et al. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae301 · Cited by 44

Predicting in vitro human mesenchymal stromal cell expansion based on individual donor characteristics using machine learning

Mehrian, M., Lambrechts, T., Maréchal, M., Luyten, F. P., Papantoniou, I., & Geris, L. (2020) · Cytotherapy · doi:10.1016/j.jcyt.2019.12.006 · Cited by 33

Exploiting complex medical data with interpretable deep learning for adverse drug event prediction

Rebane, J. T., Samsten, I., & Papapetrou, P. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101942 · Cited by 29

Combining Deep Learning With Optical Coherence Tomography Imaging to Determine Scalp Hair and Follicle Counts

Urban, G., Feil, N., Csuka, E. A., Hashemi, K., Ekelem, C., Choi, F. D., Mesinkovska, N. A., & Baldi, P. (2020) · Lasers in Surgery and Medicine · doi:10.1002/lsm.23324 · Cited by 28

Reduction of Platelet Outdating and Shortage by Forecasting Demand With Statistical Learning and Deep Neural Networks: Modeling Study

Schilling, M., Rickmann, L., Hutschenreuter, G., & Spreckelsen, C. (2021) · JMIR Medical Informatics · doi:10.2196/29978 · Cited by 28

Abnormal Health Monitoring and Assessment of a Three‐Phase Induction Motor Using a Supervised CNN‐RNN‐Based Machine Learning Algorithm

Saxena, A., Kumar, R., Rawat, A. K., Majid, M., Singh, J., Devakirubakaran, S., & Singh, G. K. (2023) · Mathematical Problems in Engineering · doi:10.1155/2023/1264345 · Cited by 26

Gap Junction Channels of Innexins and Connexins: Relations and Computational Perspectives

Sánchez, A., Castro, C., Flores, D., Gutiérrez-López, E., & Baldi, P. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20102476 · Cited by 26

Deep Learning–Assisted Multiphoton Microscopy to Reduce Light Exposure and Expedite Imaging in Tissues With High and Low Light Sensitivity

McAleer, S., Fast, A., Xue, Y., Seiler, M. J., Tang, W. C., Balu, M., Baldi, P., & Browne, A. (2021) · Translational Vision Science & Technology · doi:10.1167/tvst.10.12.30 · Cited by 27

Protein–Protein Interaction Methods and Protein Phase Separation

Savojardo, C., Martelli, P. L., & Casadio, R. (2020) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-011720-104428 · Cited by 26

Bearing Fault Diagnosis Method Based on Convolutional Neural Network and Knowledge Graph

Li, Z., Li, Y., Sun, Q., & Qi, B. (2022) · Entropy · doi:10.3390/e24111589 · Cited by 22

Toward next-generation endoscopes integrating biomimetic video systems, nonlinear optical microscopy, and deep learning

Stanciu, S. G., König, K., Song, Y. M., Wolf, L., Charitidis, C. A., Bianchini, P., & Goetz, M. (2023) · Biophysics Reviews · doi:10.1063/5.0133027 · Cited by 23

A Bayesian Possibilistic C-Means clustering approach for cervical cancer screening

Li, F., Wang, S., & Liu, G. (2019) · Information Sciences · doi:10.1016/j.ins.2019.05.089 · Cited by 20

Revisiting the Use of Quantum Chemical Calculations in LogPoctanol-water Prediction

Roy, D., & Patel, C. (2023) · Molecules · doi:10.3390/molecules28020801 · Cited by 20

Multi-scale adaptive-routing capsule contrastive network-based intelligent fault diagnosis method for rotating machinery under noisy environment and labels

Xiong, Y., Liu, Z., Tan, J., & Hao, L. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102712 · Cited by 20

Disease classification for whole-blood DNA methylation: Meta-analysis, missing values imputation, and XAI

Kalyakulina, A., Yusipov, I., Bacalini, M. G., Franceschi, C., Vedunova, M. V., & Ivanchenko, M. (2022) · GigaScience · doi:10.1093/gigascience/giac097 · Cited by 19

Ultra‐Compact and NonVolatile Nanophotonic Neural Networks

Yuan, H., Wang, Z., Peng, Z., Wu, J., & Yang, J. (2023) · Advanced Optical Materials · doi:10.1002/adom.202300215 · Cited by 19

Physics-informed neural entangled-ladder network for inhalation impedance of the respiratory system

Kumar, A., Jain, S., Jain, S., Ritam, M., Xia, Y., & Chandra, R. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107421 · Cited by 22

Biomedical relation extraction method based on ensemble learning and attention mechanism

Jia, Y., Wang, H., Yuan, Z., Zhu, L., & Xiang, Z. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05951-y · Cited by 17

Polynomial Threshold Functions, Hyperplane Arrangements, and Random Tensors

Baldi, P., & Vershynin, R. (2019) · SIAM Journal on Mathematics of Data Science · doi:10.1137/19m1257792 · Cited by 22

Advances in Deep Learning Assisted Drug Discovery Methods: A Self-review

Zhang, H., & Saravanan, K. M. (2024) · Current Bioinformatics · doi:10.2174/0115748936285690240101041704 · Cited by 16

片上集成光学神经网络综述（特邀）

Tingzhao, 符. F., Run, 孙. S., Yuyao, 黄. H., Jianfa, 张. Z., Sigang, 杨. Y., Zhihong, 朱. Z., & Hongwei, 陈. C. (2024) · Chinese Journal of Lasers · doi:10.3788/cjl231227 · Cited by 17

Static and Motion Facial Analysis for Craniofacial Assessment and Diagnosing Diseases

Matthews, H. S., de Jong, G., Maal, T., & Claes, P. D. (2022) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-122120-111413 · Cited by 15

Predictive Models for Health Deterioration: Understanding Disease Pathways for Personalized Medicine

Eskofier, B. M., & Klucken, J. (2023) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-110220-030247 · Cited by 15

Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

He, Q., & Wang, J. (2020) · Processes · doi:10.3390/pr8080951 · Cited by 17

Data science in healthcare: techniques, challenges and opportunities

Devi, P. P., & Bansal, K. L. (2024) · Health and Technology · doi:10.1007/s12553-024-00861-8 · Cited by 14

Reliable Thyroid Carcinoma Detection with Real-Time Intelligent Analysis of Ultrasound Images

Fang, H., Gong, L., Xu, Y. X., Zhuo, Y., Kong, W., Peng, C., & Yuan, J. (2020) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2020.11.024 · Cited by 13

An Investigation of Interpretable Deep Learning for Adverse Drug Event Prediction

Rebane, J. T., Karlsson, I., & Papapetrou, P. (2019) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2019.00075 · Cited by 13

A Robust MZI-Based Optical Neural Network Using QR Decomposition

Lin, J., Yang, K., Fu, Q., Wang, P., Dai, S., Chen, W., Kong, D., Li, J., Dai, T., & Yang, J. (2024) · Journal of Lightwave Technology · doi:10.1109/jlt.2024.3476113 · Cited by 13

Trends of Multimodal Neural Engineering Study: A Bibliometric Review

Wang, J., Rui, C., & Liao, P. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09557-y · Cited by 11

Identifying influential neighbors in social networks and venue affiliations among young MSM: a data science approach to predict HIV infection

Xiang, Y., Fujimoto, K., Li, F., Wang, Q., Vecchio, N. D., Schneider, J. A., Zhi, D., & Tao, C. (2020) · AIDS · doi:10.1097/qad.0000000000002784 · Cited by 13

Generalization vs. Specificity: In Which Cases Should a Clinic Train its Own Segmentation Models?

Schreier, J., Attanasi, F., & Laaksonen, H. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.00675 · Cited by 11

A Novel Repetition Frequency-Based DNA Encoding Scheme to Predict Human and Mouse DNA Enhancers with Deep Learning

Alakuş, T. B. (2023) · Biomimetics · doi:10.3390/biomimetics8020218 · Cited by 10

End-to-end sensor fusion and classification of atrial fibrillation using deep neural networks and smartphone mechanocardiography

Mehrang, S., Tadi, M. J., Knuutila, T., Jaakkola, J., Jaakkola, S., Kiviniemi, T., Vasankari, T., Airaksinen, J. K. E., Koivisto, T., & Pänkäälä, M. (2022) · Physiological Measurement · doi:10.1088/1361-6579/ac66ba · Cited by 10

Machine Learning Descriptors for Mapping Structure‐Property‐Performance Relationships of Perovskite Solar Cells

Su, Y., Wang, K., Guan, X., Wu, Y., Zhang, H., Xie, F., & Chu, J. (2025) · Advanced Energy Materials · doi:10.1002/aenm.202505294 · Cited by 10

X-ışını görüntülerinden omuz implantlarının tespiti ve sınıflandırılması: YOLO ve önceden eğitilmiş evrişimsel sinir ağı tabanlı bir yaklaşım

Karacı, A. (2021) · Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi · doi:10.17341/gazimmfd.888202 · Cited by 8

Attention-based Convolutional Neural Networks for Diesel Fuel System Fault Diagnosis

Xie, Y., Niu, T., Shao, S., Zhao, Y., & Cheng, Y. (2020) · International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) · doi:10.1109/icsmd50554.2020.9261700 · Cited by 8

Deep Learning in the Natural Sciences: Applications to Physics

Sadowski, P., & Baldi, P. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99492-5_12 · Cited by 8

Prediction of biomechanical properties of ex vivo human femoral cortical bone using Raman spectroscopy and machine learning algorithms

Ünal, M., Ünlü, R., Uppuganti, S., & Nyman, J. S. (2025) · Bone Reports · doi:10.1016/j.bonr.2025.101870 · Cited by 7

Convolution Neural Networks for Disease Prediction: Applications and Challenges

Mushtaq, S., & Singh, O. (2024) · Scalable Computing Practice and Experience · doi:10.12694/scpe.v25i1.2097 · Cited by 7

Network Biology and Medicine to Rescue: Applications for Retinal Disease Mechanisms and Therapy

Mondal, A. K., & Swaroop, A. (2023) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-27681-1_25 · Cited by 7

Introducing Attribute Association Graphs to Facilitate Medical Data Exploration: Development and Evaluation Using Epidemiological Study Data

Bellmann, L., Wiederhold, A. J., Trübe, L., Twerenbold, R., Ückert, F., & Gottfried, K. (2024) · JMIR Medical Informatics · doi:10.2196/49865 · Cited by 6

A Hybrid Levenberg–Marquardt Algorithm on a Recursive Neural Network for Scoring Protein Models

Faraggi, E., Jernigan, R. L., & Kloczkowski, A. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0826-5_15 · Cited by 6

Application of Machine Learning Techniques in Drug-target Interactions Prediction

Zhang, S., Wang, J., Lin, Z., & Liang, Y. (2020) · Current Pharmaceutical Design · doi:10.2174/1381612826666201125105730 · Cited by 6

Resilient Biomedical Systems Design Under Noise Using Logic-Based Machine Learning

Rahman, T., Shafik, R., Granmo, O., & Yakovlev, A. (2022) · Frontiers in Control Engineering · doi:10.3389/fcteg.2021.778118 · Cited by 6

Prediction of viral-host interactions of COVID-19 by computational methods

Alakuş, T. B., & Türkoğlu, İ. (2022) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2022.104622 · Cited by 5

Evaluation of Current Trends in Biomedical Applications Using Soft Computing

Kumar, S., & Veer, K. (2023) · Current Bioinformatics · doi:10.2174/1574893618666230706112826 · Cited by 5

Leveraging interpolation models and error bounds for verifiable scientific machine learning

Chang, T. H., Gillette, A., & Maulik, R. (2025) · Journal of Computational Physics · doi:10.1016/j.jcp.2025.113726 · Cited by 6

Will Artificial Intelligence Provide Answers to Current Gaps and Needs in Chronic Heart Failure?

Boccuto, F., Rosa, S. D., Torella, D., Veltri, P., & Guzzi, P. H. (2023) · Applied Sciences · doi:10.3390/app13137663 · Cited by 5

Low-threshold dual-polarization electro-optic nonlinear activation functions

Jin, T., Lin, J., Wang, P., Fu, Q., Sun, Y., Zou, Y., Dai, S., Chen, W., Li, J., Dai, T., et al. (2025) · APL Photonics · doi:10.1063/5.0230013 · Cited by 4

From data to cures: Leveraging machine learning, deep learning and pharmacore modelling for targeted therapies

Sharma, K., Srivastava, V., & Singh, R. K. (2025) · AIP conference proceedings · doi:10.1063/5.0247859 · Cited by 4

Deep learning

Cannataro, M., Guzzi, P. H., Agapito, G., Zucco, C., & Milano, M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-822952-1.00014-0 · Cited by 4

Pathology Report Interpretation and Disease Diagnosis Using Fuzzy Logic Embedded in an Artificial Intelligence Framework: A New Paradigm for Digital Technologies

Nambison, N. K. M., Singh, D. P., Mehar, R., Nambison, S. N., Sharma, H., Sharma, D., & Nambison, E. (2024) · Cureus · doi:10.7759/cureus.71148 · Cited by 3

A Chronology of Health Care Marketing Research

Iacobucci, D. (2019) · Foundations and Trends® in Marketing · doi:10.1561/1700000060 · Cited by 4

Deep Learning Analysis: A Review

Kumar, P., Ravichandran, S., & Satyala, N. (2018) · Asian Journal of Computer Science and Technology · doi:10.51983/ajcst-2018.7.s1.1811 · Cited by 3

Educational Data Science: An “Umbrella Term” or an Emergent Domain?

Peña‐Ayala, A. (2023) · Big data management · doi:10.1007/978-981-99-0026-8_3 · Cited by 3

Deep machine learning-assisted multiphoton microscopy to reduce light exposure and expedite imagingPreprint

McAleer, S., Fast, A., Xue, Y., Seiler, M. J., Tang, W. C., Balu, M., Baldi, P., & Browne, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.06408 · Cited by 3

Physics-constrained deep learning approach for solving forward and inverse thermo-mechanical coupling problems

Peng, L., Li, Q., Cui, K., Qin, X., Qing, Y., Guo, Z., & Qin, W. (2026) · International Communications in Heat and Mass Transfer · doi:10.1016/j.icheatmasstransfer.2026.110696 · Cited by 2

Machine learning techniques in biomedical data analysis: A scoping review of biostatistical perspectives

Usha, A., George, N., Janagi, K., & Verma, P. (2026) · Clinical Epidemiology and Global Health · doi:10.1016/j.cegh.2026.102362 · Cited by 2

Data Science Process for Smart Cities

Estrada, E., & Vargas, M. P. M. (2020) · Advances in human and social aspects of technology book series · doi:10.4018/978-1-7998-7552-9.ch016 · Cited by 2

STRCacheML: A Machine Learning-Assisted Content Caching Policy for Streaming Services

Mahara, A., Fuentes, J., Poellabauer, C., & Rishe, N. D. (2024) · International Conference on Artificial Intelligence and Big Data (AIBD) · doi:10.5121/csit.2024.140409 · Cited by 2

Research on Gearbox Fault Diagnosis Method Based on ECA Improved VGG16

Gong, H., Nie, X., & Xie, G. (2024) · Chinese Control Conference (CCC) · doi:10.23919/ccc63176.2024.10662349 · Cited by 2

Harnessing artificial intelligence in identifying and isolation of marine peptides

Kumari, P., Gaur, B., & Mishra, V. (2024) · Methods in microbiology · doi:10.1016/bs.mim.2024.10.002 · Cited by 2

On the B-differential of the componentwise minimum of two affine vector functions

Dussault, J., Gilbert, J. C., & Plaquevent-Jourdain, B. (2025) · Mathematical Programming Computation · doi:10.1007/s12532-024-00266-8 · Cited by 2

“Deep learning” for healthcare: Opportunities, threats, and challenges

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