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Machine-learning algorithms define pathogen-specific local immune fingerprints in peritoneal dialysis patients with bacterial infections

by Zhang, Jingjing, Friberg, Ida M., Kift-Morgan, Ann, Parekh, Gita, Morgan, Matt P., Liuzzi, Anna Rita, Lin, Chan-Yu, Donovan, Kieron L., Colmont, Chantal S., Morgan, Peter H., Davis, Paul, Weeks, Ian, Fraser, Donald J., Topley, Nicholas, Eberl, Matthias
Published in Kidney international (01.07.2017)

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Deep transfer learning algorithms applied to synthetic drawing images as a tool for supporting Alzheimer’s disease prediction

by Cilia, Nicole D., D’Alessandro, Tiziana, De Stefano, Claudio, Fontanella, Francesco
Published in Machine vision and applications (01.05.2022)

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Failure diagnosis system using a new nonlinear mapping augmentation approach for deep learning algorithm

by Kim, Dong-Yoon, Woo, Yeon-Jun, Kang, Keonwook, Yoon, Gil Ho
Published in Mechanical systems and signal processing (01.06.2022)

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Adaptive Fault-Tolerant Tracking Control for MIMO Discrete-Time Systems via Reinforcement Learning Algorithm With Less Learning Parameters

by Liu, Lei, Wang, Zhanshan, Zhang, Huaguang
Published in IEEE transactions on automation science and engineering (01.01.2017)

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Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in trauma-induced coagulopathy

by Chen, Qingsong, Li, Tao, Zhang, Tao, Zhou, Yue, Huang, Weifeng, Li, Hui, Shi, Li, Li, Jianxiao, Zhang, Qi, Ma, Man, Wang, Pan, Hu, Hui, Wei, Gongbin, Xiang, Jiangxia, Cheng, Yuan, Yang, Jun, Huang, Guangbin, Li, Yongming, Du, Dingyuan
Published in Scientific reports (08.07.2025)

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Series DC Arc Fault Detection Using Machine Learning Algorithms

by Dang, Hoang-Long, Kim, Jaechang, Kwak, Sangshin, Choi, Seungdeog
Published in IEEE access (2021)

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Lupus or not? SLE Risk Probability Index (SLERPI): a simple, clinician-friendly machine learning-based model to assist the diagnosis of systemic lupus erythematosus

by Adamichou, Christina, Genitsaridi, Irini, Nikolopoulos, Dionysis, Nikoloudaki, Myrto, Repa, Argyro, Bortoluzzi, Alessandra, Fanouriakis, Antonis, Sidiropoulos, Prodromos, Boumpas, Dimitrios T, Bertsias, George K
Published in Annals of the rheumatic diseases (01.06.2021)

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The practical utility of AI-assisted molecular profiling in the diagnosis and management of cancer of unknown primary: an updated review

by Lorkowski, Shuhui Wang, Dermawan, Josephine K., Rubin, Brian P.
Published in Virchows Archiv : an international journal of pathology (01.02.2024)

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Uncovering precision phenotype-biomarker associations in traumatic brain injury using topological data analysis

by Nielson, Jessica L., Cooper, Shelly R., Yue, John K., Sorani, Marco D., Inoue, Tomoo, Yuh, Esther L., Mukherjee, Pratik, Petrossian, Tanya C., Paquette, Jesse, Lum, Pek Y., Carlsson, Gunnar E., Vassar, Mary J., Lingsma, Hester F., Gordon, Wayne A., Valadka, Alex B., Okonkwo, David O., Manley, Geoffrey T., Ferguson, Adam R.
Published in PloS one (03.03.2017)

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Machine Learning Approaches to Prognostication in Traumatic Brain Injury

by Badjatia, Neeraj, Podell, Jamie, Felix, Ryan B., Chen, Lujie Karen, Dalton, Kenneth, Wang, Tina I., Yang, Shiming, Hu, Peter
Published in Current neurology and neuroscience reports (01.12.2025)

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Particle filtering‐based adaptive training of neural networks for real‐time structural damage diagnosis and prognosis

by Cadini, Francesco, Sbarufatti, Claudio, Corbetta, Matteo, Cancelliere, Francesco, Giglio, Marco
Published in Structural control and health monitoring (01.12.2019)

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Application of CNN-Based Machine Learning in the Study of Motor Fault Diagnosis

by Peng, Xiuyan, Wei, Lunpan, Gao, Wei
Published in Computational intelligence and neuroscience (05.09.2022)

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Can we identify individuals at risk to develop multiple myeloma? A machine learning‐based predictive model

by Mittelman, Moshe, Israel, Ariel, Oster, Howard S., Leshchinsky, Michael, Ben‐Shlomo, Yatir, Kepten, Eldad, Dolberg, Osnat Jarchowsky, Balicer, Ran, Shaham, Galit
Published in British journal of haematology (01.08.2025)

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Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques

by Pérez del Palomar, Amaya, Cegoñino, José, Montolío, Alberto, Orduna, Elvira, Vilades, Elisa, Sebastián, Berta, Pablo, Luis E., Garcia-Martin, Elena
Published in PloS one (06.05.2019)

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Deep learning in industrial machinery: A critical review of bearing fault classification methods

by Rehman, Attiq Ur, Jiao, Weidong, Jiang, Yonghua, Wei, Jianan, Sohaib, Muhammad, Sun, Jianfeng, E, Shiju, Rehman, Khalil Ur, Chi, Yongwei
Published in Applied soft computing (01.03.2025)

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In silico prediction of mitochondrial toxicity of chemicals using machine learning methods

by Zhao, Piaopiao, Peng, Yayuan, Xu, Xuan, Wang, Zhiyuan, Wu, Zengrui, Li, Weihua, Tang, Yun, Liu, Guixia
Published in Journal of applied toxicology (01.10.2021)

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Blood trace metals in a sporadic amyotrophic lateral sclerosis geographical cluster

by De Benedetti, Stefano, Lucchini, Giorgio, Del Bò, Cristian, Deon, Valeria, Marocchi, Alessandro, Penco, Silvana, Lunetta, Christian, Gianazza, Elisabetta, Bonomi, Francesco, Iametti, Stefania
Published in Biometals (01.06.2017)

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Clinical-functional correlation with brain volumetry in severe perinatal asphyxia: a case report

by Velasquez-Minoli, Juan Pablo, Cardona-Ramirez, Natalia, Garcia-Arias, Hernan Felipe, Restrepo-Restrepo, Feliza, Porras-Hurtado, Gloria Liliana
Published in Italian journal of pediatrics (09.04.2024)

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Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts

by Atabaki-Pasdar, Naeimeh, Ohlsson, Mattias, Viñuela, Ana, Frau, Francesca, Pomares-Millan, Hugo, Haid, Mark, Jones, Angus G., Thomas, E. Louise, Koivula, Robert W., Kurbasic, Azra, Mutie, Pascal M., Fitipaldi, Hugo, Fernandez, Juan, Dawed, Adem Y., Giordano, Giuseppe N., Forgie, Ian M., McDonald, Timothy J., Rutters, Femke, Cederberg, Henna, Chabanova, Elizaveta, Dale, Matilda, Masi, Federico De, Thomas, Cecilia Engel, Allin, Kristine H., Hansen, Tue H., Heggie, Alison, Hong, Mun-Gwan, Elders, Petra J. M., Kennedy, Gwen, Kokkola, Tarja, Pedersen, Helle Krogh, Mahajan, Anubha, McEvoy, Donna, Pattou, Francois, Raverdy, Violeta, Häussler, Ragna S., Sharma, Sapna, Thomsen, Henrik S., Vangipurapu, Jagadish, Vestergaard, Henrik, ‘t Hart, Leen M., Adamski, Jerzy, Musholt, Petra B., Brage, Soren, Brunak, Søren, Dermitzakis, Emmanouil, Frost, Gary, Hansen, Torben, Laakso, Markku, Pedersen, Oluf, Ridderstråle, Martin, Ruetten, Hartmut, Hattersley, Andrew T., Walker, Mark, Beulens, Joline W. J., Mari, Andrea, Schwenk, Jochen M., Gupta, Ramneek, McCarthy, Mark I., Pearson, Ewan R., Bell, Jimmy D., Pavo, Imre, Franks, Paul W.
Published in PLoS medicine (19.06.2020)

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Identification of key candidate genes for IgA nephropathy using machine learning and statistics based bioinformatics models

by Al Mehedi Hasan, Md, Maniruzzaman, Md, Shin, Jungpil
Published in Scientific reports (17.08.2022)

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