microRNA-497-5p-based screening identifies a novel synthetic lethal-type interaction via PKMYT1 and WEE1 in pleural mesothelioma by Nathanael Pruett, Sierra Wilferd, Anand Singh, Agnes Y. Choi, Shivani Dixit, Vivek Singh, Charlize Nguyen, Olivia Lin, David S. Schrump, Christopher L. Plaisier, Chuong D. Hoang

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Machine learning algorithms for risk factor selection with application to 60-day sepsis morbidity risk for a geriatric hip fracture cohort by Zhe Xu, Ruguo Zhang, Qiuhan Chen, Guoxuan Peng, Shanpeng Luo, Chen Liu, Ling Zeng, Jin Deng

“…The purpose of this study was to screen for risk factors for 60-day sepsis morbidity after hip fracture and to establish a predictive model using various machine learning algorithms. …”
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A voice-based algorithm can predict type 2 diabetes status in USA adults: Findings from the Colive Voice study. by Abir Elbéji, Mégane Pizzimenti, Gloria Aguayo, Aurélie Fischer, Hanin Ayadi, Franck Mauvais-Jarvis, Jean-Pierre Riveline, Vladimir Despotovic, Guy Fagherazzi

“…The pressing need to reduce undiagnosed type 2 diabetes (T2D) globally calls for innovative screening approaches. This study investigates the potential of using a voice-based algorithm to predict T2D status in adults, as the first step towards developing a non-invasive and scalable screening method. …”
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Integrating SEResNet101 and SE-VGG19 for advanced cervical lesion detection: a step forward in precision oncology by Yan Ye, Yuanyuan Chen, Jiajia Pan, Peipei Li, Feifei Ni, Haizhen He

“…Deep learning models hold the potential to enhance the accuracy of cervical cancer screening but require thorough evaluation to ascertain their practical utility. …”
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Preference-based expensive multi-objective optimization without using an ideal point by Peipei Zhao, Liping Wang, Qicang Qiu

“…The Gaussian process model is built on the objective functions. In the model-based optimization, the projection distance with upper confidence bound (UCB) is developed as the fitness of solutions for each subproblem. …”
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Development and Application of a Senolytic Predictor for Discovery of Novel Senolytic Compounds and Herbs by Jinjun Li, Kai Zhao, Guotai Yang, Haohao Lv, Renxin Zhang, Shuhan Li, Zhiyuan Chen, Min Xu, Naixue Yang, Shaoxing Dai

“…By applying MoLFormer-based oversampling and testing different algorithms, it was found that the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) models with MoLFormer embeddings exhibited the best performance, achieving Area Under the Curve (AUC) scores of 0.998 and 0.997, and F1 scores of 0.948 and 0.941, respectively. …”
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Machine learning aids in the discovery of efficient corrosion inhibitor molecules by Haiyan GONG, Lingwei MA, Dawei ZHANG

“…First, the current compound search space for corrosion inhibitor molecule screening models remains limited. Second, these models face challenges related to computational resources and time costs in practical applications. …”
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A Machine Learning Platform for Isoform-Specific Identification and Profiling of Human Carbonic Anhydrase Inhibitors by Lisa Piazza, Miriana Di Stefano, Clarissa Poles, Giulia Bononi, Giulio Poli, Gioele Renzi, Salvatore Galati, Antonio Giordano, Marco Macchia, Fabrizio Carta, Claudiu T. Supuran, Tiziano Tuccinardi

“…The best-performing models for each isoform were applied in a virtual screening campaign for ~2 million compounds. …”
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Deep learning-based analysis of 12-lead electrocardiograms in school-age children: a proof of concept study by Shuhei Toba, Yoshihide Mitani, Yusuke Sugitani, Yusuke Sugitani, Hiroyuki Ohashi, Hirofumi Sawada, Mami Takeoka, Naoki Tsuboya, Kazunobu Ohya, Noriko Yodoya, Takato Yamasaki, Yuki Nakayama, Hisato Ito, Masahiro Hirayama, Motoshi Takao

“…For detecting electrocardiograms with ST-T abnormality, complete right bundle branch block, QRS axis abnormality, left ventricular hypertrophy, incomplete right bundle branch block, WPW syndrome, supraventricular tachyarrhythmia, and Brugada-type electrocardiograms, the specificity of the deep learning-based model was higher than that of the conventional algorithm at the same sensitivity.ConclusionsThe present new deep learning-based method of screening for abnormal electrocardiograms in children showed at least a similar diagnostic performance compared to that of a conventional algorithm. …”
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Using Supervised Machine Learning Algorithms to Predict Bovine Leukemia Virus Seropositivity in Florida Beef Cattle: A 10‐Year Retrospective Study by Ameer A. Megahed, Y. Reddy Bommineni, Michael Short, João H. J. Bittar

“…The DT model showed comparable performance to RF (AUROC, 0.94; misclassification rate, 0.06). …”
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Global miniaturization of broadband antennas by prescreening and machine learning by Slawomir Koziel, Anna Pietrenko-Dabrowska, Ubaid Ullah

“…Our technique includes parameter space pre-screening and the iterative refinement of kriging surrogate models using the predicted merit function minimization as an infill criterion. …”
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Discrete Element Simulation Parameter Calibration of Wheat Straw Feed Using Response Surface Methodology and Particle Swarm Optimization–Backpropagation Hybrid Algorithm by Zhigao Hu, Hao Li, Xuming Shi, Lingzhuo Kong, Xiang Tian, Shiguan An, Bin Feng, Juan Ma

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Research on civil aircraft cockpit display interface availability considering multidimensional indicators clustering and reduction by CHEN Dengkai, XIAO Yao, XIAO Jianghao, ZHOU Yao, YANG Cong

“…Finally, the support vector machine(SVM) classification model was employed to verify performance and reliability of both algorithms. …”
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Recovering 3D Basin Basement Relief Using High-Precision Magnetic Data Through Particle Swarm Optimization and Back Propagation Algorithm by Shen Yan, Xinjun Zhang, Zhongda Shang, Kai Wang, Yixin Ma

“…Feature attributes were extracted, and the Gini importance was used to quantify feature factor contributions, screen out effective features, and improve algorithm efficiency. …”
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Applications of Artificial Intelligence in Drug Repurposing by Zhaoman Wan, Xinran Sun, Yi Li, Tianyao Chu, Xueyu Hao, Yang Cao, Peng Zhang

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Detection Method for Bolts with Mission Pins on Transmission Lines Based on DBSCAN-FPN by Zhenbing ZHAO, Shuai ZHANG, Wei JIANG, Peng WU

“…Aim at this problem, a detection method for bolts with missing pins is proposed based on the DBSCAN algorithm and FPN model. Firstly, the FPN model is used to locate the target area of the bolts with missing pins, and the areas with same morphological structure are clustered based on the DBSCAN clustering algorithm. …”
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Algorithm for assessing the total 10 years risk of death from cardiovascular diseases in women 25-64 years old in Tyumen (Tyumen risk scale) by G. S. Pushkarev, S. T. Matskeplishvili, V. A. Kuznetsov, E. V. Akimova

“…We used a multivariate Cox regression model to estimate hazard ratio (HR) and confidence interval (CI). …”
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Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River by Manqi Wang, Caili Zhou, Jiaqi Shi, Fei Lin, Yucheng Li, Yimin Hu, Xuesheng Zhang

“…Again, based on the screened features, a back-propagation neural network (BPNN) model optimized using a mixture of the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm was established as a means of estimating water quality parameter concentrations. …”
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Performance of artificial intelligence on cervical vertebral maturation assessment: a systematic review and meta-analysis by Termeh Sarrafan Sadeghi, Seyed AmirHossein Ourang, Fatemeh Sohrabniya, Soroush Sadr, Parnian Shobeiri, Saeed Reza Motamedian

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Grey wolf optimization technique with U-shaped and capsule networks-A novel framework for glaucoma diagnosis by Govindharaj I, Ramesh T, Poongodai A, Senthilkumar K. P, Udayasankaran P, Ravichandran S

“…The glaucoma classification depends on CapsNet as a deep learning model that provides exceptional image detection to ensure precise diagnosis. …”
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