Machine learning−derived multivariable predictors of postcardiotomy cardiogenic shock in high-risk cardiac surgery patientsCentral MessagePerspective by Edward G. Soltesz, MD, MPH, Randi J. Parks, PhD, Elise M. Jortberg, MS, Eugene H. Blackstone, MD

“…In total, 68 preoperative clinical variables were considered in machine-learning algorithms trained and optimized using scikit-learn software. …”
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ShinyGS—a graphical toolkit with a serial of genetic and machine learning models for genomic selection: application, benchmarking, and recommendations by Le Yu, Le Yu, Yifei Dai, Mingjia Zhu, Linjie Guo, Yan Ji, Huan Si, Lirui Cheng, Tao Zhao, Yanjun Zan

“…This toolkit incorporates 16 methods, including linear models from maximum likelihood and Bayesian framework (BA, BB, BC, BL, and BRR), machine learning models, and a data visualization function. …”
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Recent Advancements in Neuroimaging‐Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e‐Health: A Systematic Review by Zia‐Ur‐Rehman, Mohd Khalid Awang, Ghulam Ali, Muhammad Faheem

“…ABSTRACT Purpose Alzheimer's disease (AD) is a severe neurological disease that significantly impairs brain function. Timely identification of AD is essential for appropriate treatment and care. …”
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Gully-ERFNet: a novel lightweight deep learning model for extracting erosion gullies in the black soil region of Northeast China by Qingyao Li, Jiuchun Yang, Jiaqi Wang, Zhi Li, Jianwei Fan, Liwei Ke, Xue Wang

“…We employed the Adaptive Correction Loss function to reduce the impact of incorrect labels on model performance. …”
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Auxiliary identification of depression patients using interpretable machine learning models based on heart rate variability: a retrospective study by Min Yang, Huiqin Zhang, Minglan Yu, Yunxuan Xu, Bo Xiang, Xiaopeng Yao

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A Label Correction Learning Framework for Gully Erosion Extraction Using High-Resolution Remote Sensing Images and Noisy Labels by Chunhui Zhao, Yi Shen, Nan Su, Yiming Yan, Shou Feng, Wei Xiang, Yong Liu, Tianhao Zhao

“…To achieve this, we introduce three key components in the framework, including an adaptive correction loss function, a multitree refinement module, and a noise correction module. …”
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Application of machine learning algorithms for predicting the life-long physiological effects of zinc oxide Micro/Nano particles on Carum copticum by Maryam Mazaheri-Tirani, Soleyman Dayani, Majid Iranpour Mobarakeh

“…The model obtained from SVR with the radial basis function (RBF) kernel was selected as a comprehensive model for predicting and determining the efficacy of the results.…”
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GAB-YOLO: a lightweight deep learning model for real-time detection of abnormal behaviors in juvenile greater amberjack fish by Mingxin Liu, Mingxin Liu, Chun Zhang, Cong Lin, Cong Lin

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Mitigating malicious denial of wallet attack using attribute reduction with deep learning approach for serverless computing on next generation applications by Amal K. Alkhalifa, Mohammed Aljebreen, Rakan Alanazi, Nazir Ahmad, Sultan Alahmari, Othman Alrusaini, Ali Alqazzaz, Hassan Alkhiri

“…The deep learning (DL) model, a part of the machine learning (ML) technique, has developed as an effectual device in cybersecurity, permitting more effectual recognition of anomalous behaviour and classifying patterns indicative of threats. …”
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Auto-Segmentation via deep-learning approaches for the assessment of flap volume after reconstructive surgery or radiotherapy in head and neck cancer by Juliette Thariat, Zacharia Mesbah, Youssef Chahir, Arnaud Beddok, Alice Blache, Jean Bourhis, Abir Fatallah, Mathieu Hatt, Romain Modzelewski

“…Abstract Reconstructive flap surgery aims to restore the substance and function losses associated with tumor resection. Automatic flap segmentation could allow quantification of flap volume and correlations with functional outcomes after surgery or post-operative RT (poRT). …”
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Protein allosteric site identification using machine learning and per amino acid residue reported internal protein nanoenvironment descriptors by Folorunsho Bright Omage, José Augusto Salim, Ivan Mazoni, Inácio Henrique Yano, Luiz Borro, Jorge Enrique Hernández Gonzalez, Fabio Rogerio de Moraes, Poliana Fernanda Giachetto, Ljubica Tasic, Raghuvir Krishnaswamy Arni, Goran Neshich

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Multi-Omics Analysis Combined with Machine Learning Identified FABP4 in Smooth Muscle Cells as a Pathogenic Factor in Atherosclerosis by Wang Y, Shang T, Cai R, Wang C

“…Keywords: atherosclerosis, smooth muscle cell, machine learning, molecular docking…”
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Brain tau PET-based identification and characterization of subpopulations in patients with Alzheimer’s disease using deep learning-derived saliency maps by Yanxiao Li, Xiuying Wang, Qi Ge, Manuel B Graeber, Shaozhen Yan, Jian Li, Shuyu Li, Wenjian Gu, Shuo Hu, Tammie L. S. Benzinger, Jie Lu, Yun Zhou

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A machine learning-based approach for constructing a 3D apparent geological model using multi-resistivity data by Jordi Mahardika Puntu, Ping-Yu Chang, Haiyina Hasbia Amania, Ding-Jiun Lin, M. Syahdan Akbar Suryantara, Jui-Pin Tsai, Hwa-Lung Yu, Liang-Cheng Chang, Jun-Ru Zeng, Lingerew Nebere Kassie

“…Abstract This study presents a comprehensive approach for constructing a 3D Apparent Geological Model (AGM) by integrating multi-resistivity data using statistical methods, supervised machine learning (SML), and Python-based modeling techniques. …”
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Enhancing Urban Heat Island Analysis Through Multisensor Data Fusion and GRU-Based Deep Learning Approaches for Climate Modeling by Ning Tang, Muhammad Farhan, Pir Mohammad, M. Abdullah-Al-Wadud, Saddam Hussain, Umair Hamza, Rana Muhammad Zulqarnain, Nazih Yacer Rebouh

“…The adopted approach emphasizes vegetation's crucial function in reducing UHI's effects and offers valuable insights for urban planning and measures to mitigate climate change.…”
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Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and in vitro experiments by Yonghua Pang, Jiahui Liang, Jiahui Liang, Yakai Deng, Weinan Chen, Yunyan Shen, Jing Li, Xin Wang, Zhiyao Ren

“…Additionally, its functional role suggests potential as a therapeutic target.…”
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Intra-BLA alteration of interneurons’ modulation of activity in rats, reveals a dissociation between effects on anxiety symptoms and extinction learning by Rinki Saha, Lisa-Sophie Wüstner, Darpan Chakraborty, Rachel Anunu, Silvia Mandel, Joyeeta Dutta Hazra, Martin Kriebel, Hansjuergen Volkmer, Hanoch Kaphzan, Gal Richter-Levin

“…While intra-BLA NF-KD resulted in impaired extinction learning, without increased symptoms of anxiety, intra-BLA reduction of EphA7 expression resulted in increased symptoms of anxiety, as measured in the elevated plus maze, but without affecting fear conditioning or extinction learning. …”
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Anti-factor Xa during unfractionated heparin therapy in critically ill patients: Development of prediction models using machine learning by Boris Delange, Guillaume Bouzillé, Pauline Guillot, Anaëlle Bichon, Océane Bernard de Lajartre, Isabelle Gouin, Yoann Launey, Alexandre Mansour, Mathieu Lesouhaitier, Jean-Marc Tadié, Arnaud Gacouin, Marc Cuggia, Adel Maamar

“…Models incorporated UFH rate, bolus, prior anti-Xa, kidney function, inflammation, volemic state, extracorporeal membrane oxygenation, and bilirubinemia. …”
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Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data by Nur Hani Zainal, Hui Han Tan, Ryan Yee Shiun Hong, Michelle Gayle Newman

“…These ML models included random forest and support vector machines (radial basis function kernel) and 10-fold nested cross-validation that separated model training, minimal tuning in inner folds, and model testing in outer folds. …”
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Exergy and energy-based sustainability evaluation of diesel-biodiesel-ethanol blends with emission forecasting using advanced machine learning models by Harish Venu, V. Dhana Raju, Jayashri N. Nair, Sameer Algburi, Ali E. Anqi, Ali A. Rajhi, Mohammed Kareemullah

“…The increasing influence of machine learning in engine emission prediction is on rising trend. …”
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