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Cited by in CrossRef
For: Charilaou P, Battat R. Machine learning models and over-fitting considerations. World J Gastroenterol 2022; 28(5): 605-607 [PMID: 35316964 DOI: 10.3748/wjg.v28.i5.605]
URL: https://www.wjgnet.com/1007-9327/full/v28/i5/605.htm
Number Citing Articles
1
Yingwen Wu, Yangjian Ji. Identifying firm-specific technology opportunities from the perspective of competitors by using association rule miningJournal of Informetrics 2023; 17(2): 101398 doi: 10.1016/j.joi.2023.101398
2
Ruth Salim, Simon Husby, Christian Winther Eskelund, David W. Scott, Harald Holte, Arne Kolstad, Riikka Räty, Sara Ek, Mats Jerkeman, Christian Geisler, Lasse Sommer Kristensen, Mette Dahl, Kirsten Grønbæk. Exploring new prognostic biomarkers in Mantle Cell Lymphoma: a comparison of the circSCORE and the MCL35 scoreLeukemia & Lymphoma 2023; 64(8): 1414 doi: 10.1080/10428194.2023.2216819
3
Jan-Mou Lee, Yi-Ping Hung, Kai-Yuan Chou, Cheng-Yun Lee, Shian-Ren Lin, Ya-Han Tsai, Wan-Yu Lai, Yu-Yun Shao, Chiun Hsu, Chih-Hung Hsu, Yee Chao. Artificial intelligence-based immunoprofiling serves as a potentially predictive biomarker of nivolumab treatment for advanced hepatocellular carcinomaFrontiers in Medicine 2022; 9 doi: 10.3389/fmed.2022.1008855
4
Milad Hosseinpour, Mohammad Javad Shojaei, Mohsen Salimi, Majid Amidpour. Machine learning in absorption-based post-combustion carbon capture systems: A state-of-the-art reviewFuel 2023; 353: 129265 doi: 10.1016/j.fuel.2023.129265
5
Wentao Zhang, Wenguang Huang, Jie Tan, Dawei Huang, Jun Ma, Bingdang Wu. Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectivesChemosphere 2023; 311: 137044 doi: 10.1016/j.chemosphere.2022.137044
6
Pradeep Kumar Hanumegowda, Sakthivel Gnanasekaran. Prediction of Work-Related Risk Factors among Bus Drivers Using Machine LearningInternational Journal of Environmental Research and Public Health 2022; 19(22): 15179 doi: 10.3390/ijerph192215179
7
Alvaro Ras-Carmona, Alexander A. Lehmann, Paul V. Lehmann, Pedro A. Reche. Prediction of B cell epitopes in proteins using a novel sequence similarity-based methodScientific Reports 2022; 12(1) doi: 10.1038/s41598-022-18021-1
8
Abdelhady Omar, Atefeh Delnaz, Mazdak Nik-Bakht. Comparative analysis of machine learning techniques for predicting water main failures in the City of KitchenerJournal of Infrastructure Intelligence and Resilience 2023; 2(3): 100044 doi: 10.1016/j.iintel.2023.100044
9
Arihant Singh, Vivek R Velagala, Tanishq Kumar, Rajoshee R Dutta, Tushar Sontakke. The Application of Deep Learning to Electroencephalograms, Magnetic Resonance Imaging, and Implants for the Detection of Epileptic Seizures: A Narrative ReviewCureus 2023;  doi: 10.7759/cureus.42460
10
Matthew I. Miller, Ludy C. Shih, Vijaya B. Kolachalama. Machine Learning in Clinical Trials: A Primer with Applications to NeurologyNeurotherapeutics 2023; 20(4): 1066 doi: 10.1007/s13311-023-01384-2
11
Zaid Alhulaybi, Muhammad Martuza, Sayeed Rushd. Modeling the Mechanical Properties of a Polymer-Based Mixed-Matrix Membrane Using Deep Learning Neural NetworksChemEngineering 2023; 7(5): 80 doi: 10.3390/chemengineering7050080
12
Ibrahem Albalkhi, Aashim Bhatia, Nico Lösch, Robert Goetti, Kshitij Mankad. Current state of radiomics in pediatric neuro-oncology practice: a systematic reviewPediatric Radiology 2023; 53(10): 2079 doi: 10.1007/s00247-023-05679-6
13
Miroslav Stojadinovic, Bogdan Milicevic, Slobodan Jankovic. Enhanced PSA Density Prediction Accuracy When Based on Machine LearningJournal of Medical and Biological Engineering 2023; 43(3): 249 doi: 10.1007/s40846-023-00793-0
14
Laily Azyan Ramlan, Wan Mimi Diyana Wan Zaki, Haliza Abdul Mutalib, Aini Hussain, Aouache Mustapha. Cataract Detection using Pupil Patch Classification and Ruled-based System in Anterior Segment Photographed Images2023 IEEE 13th Symposium on Computer Applications & Industrial Electronics (ISCAIE) 2023; : 124 doi: 10.1109/ISCAIE57739.2023.10165004
15
Yao Yao, Chuanliang Jia, Haicheng Zhang, Yakui Mou, Cai Wang, Xiao Han, Pengyi Yu, Ning Mao, Xicheng Song. Applying a nomogram based on preoperative CT to predict early recurrence of laryngeal squamous cell carcinoma after surgeryJournal of X-Ray Science and Technology 2023; 31(3): 435 doi: 10.3233/XST-221320