Predicting the Pathway Involvement of Compounds Annotated in the Reactome Knowledgebase by Erik D. Huckvale, Hunter N. B. Moseley

“…<b>Conclusions</b>: These results indicate that the pathways in Reactome can also be effectively predicted, greatly increasing the number of human-defined pathways available for prediction.…”
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Testing mammary gland secretions to predict foaling in jennies by Tiziana Nervo, Alessia Bertero, Patrizia Ponzio, Eleonora Julita, Mariagrazia Lucia Poletto, Gian Guido Donato

“…In our work, neither electrolyte nor pH evaluations proved to be reliable to predict with an adequate level of confidence the impeding parturition.…”
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Machine Learning Models for the Prediction of Wind Loads on Containerships by Nastia Degiuli, Carlo Giorgio Grlj, Ivana Martić, Sandi Baressi Šegota, Nikola Anđelić, Darin Majnarić

“…In this study, machine learning models based on the multilayer perceptron and gradient-boosted tree methods were employed to predict wind load coefficients for containerships with various container configurations. …”
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Application of smart technologies for predicting soil erosion patterns by Rana Muhammad Adnan Ikram, Mo Wang, Hossein Moayedi, Atefeh Ahmadi Dehrashid, Shiva Gharibi, Jing-Cheng Han

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Predictive value of dendritic cell-related genes for prognosis and immunotherapy response in lung adenocarcinoma by Zihao Sun, Mengfei Hu, Xiaoning Huang, Minghan Song, Xiujing Chen, Jiaxin Bei, Yiguang Lin, Size Chen

“…Abstract Background Patients with lung adenocarcinoma (LUAD) receiving drug treatment often have an unpredictive response and there is a lack of effective methods to predict treatment outcome for patients. …”
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Predicting Employee Turnover Using Machine Learning Techniques by Adil Benabou, Fatima Touhami, My Abdelouahed Sabri

“…This study aims to identify the most effective machine learning model for predicting employee attrition, thereby providing organizations with a reliable tool to anticipate turnover and implement proactive retention strategies.Objective: This study aims to address the challenge of employee attrition by applying machine learning techniques to provide predictive insights that can improve retention strategies.Methods: Nine machine learning algorithms are applied to a dataset of 1,470 employee records. …”
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Cardiovascular reactivity and prediction of the high blood pressure in the community. by Mikhail Benet Rodríguez, Liliana J Espinosa Chang, Juan José Apollinaire Pennini, Milagros L León Regal, María Felicia Casanova González

“…For this reason, the search of method in the prediction of the high blood pressure is an appropriate way, and a very important objective to get. …”
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Optimizing hypertension prediction using ensemble learning approaches. by Isteaq Kabir Sifat, Md Kaderi Kibria

“…This study investigates how well ensemble learning techniques work to increase the accuracy of HTN prediction models. Utilizing a dataset of 612 participants from Ethiopia, which includes 27 features potentially associated with HTN risk, we aimed to enhance predictive performance over traditional single-model methods. …”
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Unsupervised Action Anticipation Through Action Cluster Prediction by Jiuxu Chen, Nupur Thakur, Sachin Chhabra, Baoxin Li

“…Apart from the action anticipation method, we propose an innovative evaluation scheme GreedyMapper, a unique many-to-one mapping scheme that provides a practical solution to the many-to-one mapping challenge, a task that existing mapping algorithms struggle to address. …”
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RACH Traffic Prediction in Massive Machine Type Communications by Hossein Mehri, Hani Mehrpouyan, Hao Chen

“…Comprehensive evaluations and simulations indicate that our proposed machine learning approach achieves a remarkable 52% higher accuracy in long-term predictions compared to traditional methods, without imposing additional processing load on the system.…”
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Predicting Critical Bicycle-Vehicle Conflicts at Signalized Intersections by Alireza Darzian Rostami, Anagha Katthe, Aryan Sohrabi, Arash Jahangiri

“…Intersection safety evaluation has primarily relied on historical crash data. …”
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Predicting functional recovery after acute ankle sprain. by Sean R O'Connor, Chris M Bleakley, Mark A Tully, Suzanne M McDonough

“…Multivariate stepwise linear regression analyses were undertaken to evaluate the association between the aforementioned variables and functional recovery.…”
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From predictive analytics to emotional recognition–The evolving landscape of cognitive computing in animal welfare by Suresh Neethirajan

“…The work extends to cognitive evaluations, such as mirror tests and bias assessments, revealing higher levels of self-awareness and cognitive abilities in farm animals than previously recognized. …”
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A subregional prediction model for radiation-induced hypothyroidism by Wenting Ren, Ziqi Pan, Kuo Men, Bin Liang, Qingfeng Xu, Junlin Yi, Jianrong Dai

“…Conclusions In this study, a subregional model for predicting RHT was established and the radiosensitivity of the relevant subregions was evaluated. …”
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Comparing AI/ML approaches and classical regression for predictive modeling using large population health databases: Applications to COVID-19 case prediction by Lise M. Bjerre, Cayden Peixoto, Rawan Alkurd, Robert Talarico, Rami Abielmona

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Dengue Severity Prediction in a Hyperendemic Region in Colombia by Jorge Emilio Salazar Flórez, Katerine Marín Velasquez, Luz Stella Giraldo Cardona, Ángela María Segura Cardona, Berta Nelly Restrepo Jaramillo, Margarita Arboleda

“…This study aimed to identify predictors of SD in a hyperendemic region of Colombia. Methods: A cross-sectional analysis was conducted using data from 2018 to 2022, encompassing 233 patients. …”
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Prediction of Risk Factors Associated with Postpartum Spontaneous Bleeding by Hadis Musavi, Fatemeh Kalaki-Jouybari, Amir Abbas Alviri, Abbas Khonakdar-Tarsi, Zinatosaadat Bouzari, Zahra Sadeghnezhad

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Prediction of induction motor faults using machine learning by Ademola Abdulkareem, Tochukwu Anyim, Olawale Popoola, John Abubakar, Agbetuyi Ayoade

“…This research study centers on the development of a versatile machine-learning model for predicting faults in induction motors within industrial environments. …”
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Prediction model for the selection of patients with glioma to proton therapy by Jesper Folsted Kallehauge, Siri Grondahl, Camilla Skinnerup Byskov, Morten Høyer, Slavka Lukacova

“…The aim of this retrospective study was to identify clinical parameters associated with referral to PT and build a prediction model. Patients and methods: The dataset consisted of patients with isocitrate dehydrogenase (IDH)-mutant grades 2 and 3 glioma and candidates for PT at the Aarhus University Hospital. …”
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Machine Learning-Based Prediction of No-Show Telemedicine Encounters by C. Mahony Reategui-Rivera, Wanting Cui, Stefan Escobar-Agreda, Leonardo Rojas-Mezarina, Joseph Finkelstein

“…Aim: This study aimed to evaluate the performance of machine learning (ML) models in predicting patient no-shows for telemedicine appointments within Peruvian health system and identify key predictors of nonattendance. …”
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