Developing a Behavioral Phenotyping Layer for Artificial Intelligence–Driven Predictive Analytics in a Digital Resiliency Course: Protocol for a Randomized Controlled Trial by Trevor van Mierlo, Rachel Fournier, Siu Kit Yeung, Sofiia Lahutina

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Stroke prediction in elderly patients with atrial fibrillation using machine learning combined clinical and left atrial appendage imaging phenotypic features by Hao Huang, Yan Xiong, Yuan Yao, Jie Zeng

“…Abstract Background Atrial fibrillation (AF) is one of the primary etiologies for ischemic stroke, and it is of paramount importance to delineate the risk phenotypes among elderly AF patients and to investigate more efficacious models for predicting stroke risk. …”
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Reflectance spectroscopy predicts leaf functional traits across wine grape cultivars by Boya Cui, Rachel Mariani, Kimberley A. Cathline, Gavin Robertson, Adam R. Martin

“…Reflectance spectroscopy can therefore rapidly characterize wine grape phenotypes and, in doing so, inform predictions of how vines, clones and cultivars will respond to environmental change. …”
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Leveraging ultrasonic-derived phenotypes and estimated breeding value to improve abdominal fat weight prediction in chickens throughout the egg laying period by Penghao Li, Zhengda Li, Fan Ying, Dan Zhu, Dawei Liu, Xianyi Song, Jie Wen, Guiping Zhao, Bingxing An

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Brain-phenotype predictions of language and executive function can survive across diverse real-world data: Dataset shifts in developmental populations by Brendan D. Adkinson, Matthew Rosenblatt, Javid Dadashkarimi, Link Tejavibulya, Rongtao Jiang, Stephanie Noble, Dustin Scheinost

“…Predictive modeling potentially increases the reproducibility and generalizability of neuroimaging brain-phenotype associations. …”
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Open-Source High-Throughput Phenotyping for Blueberry Yield and Maturity Prediction Across Environments: Neural Network Model and Labeled Dataset for Breeders by Jing Zhang, Jerome Maleski, Hudson Ashrafi, Jessica A. Spencer, Ye Chu

“…In this study, we developed and shared a high-throughput phenotyping method using neural networks to predict blueberry time to maturity and to provide a proxy for yield, overcoming the labor constraints of obtaining high-frequency data. …”
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Implementation of microbiological monitoring for predicting the multidrug-resistant phenotype of respiratory isolates based on risk factor analysis in patients with community-acquired pneumonia by O. P. Bobrova, S. K. Zyryanov, V. V. Chernaya, M. V. Statsenko, O. A. Parygina

“…The obtained results demonstrate the importance of a comprehensive analysis of clinical and microbiological factors for predicting the polyresistance phenotype in a specific medical organization and optimizing the empirical choice of antimicrobial drugs.…”
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Identifying and Validating Prognostic Hyper-Inflammatory and Hypo-Inflammatory COVID-19 Clinical Phenotypes Using Machine Learning Methods by Ji X, Guo Y, Tang L, Gao C

“…These phenotypes can be accurately recognized using machine learning models, with the AdaBoost model being optimal for predicting in-hospital mortality. …”
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Real-time monitoring to predict depressive symptoms: study protocol by Yu-Rim Lee, Jong-Sun Lee

“…The present study aims to identify the digital phenotype that significantly predicts depressive symptoms.Methods and analysisThe study will recruit a total of 150 participants in their 20s who have experienced depression for the past two weeks in Korea. …”
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Predicting adaptation and evolution of plasticity from temporal environmental change by Cristóbal Gallegos, Luis‐Miguel Chevin, Kathryn A. Hodgins, Keyne Monro

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Phenotyping of risk factors and prediction of inhospital mortality in patients with coronary artery disease after coronary artery bypass grafting based on explainable artificial intelligence methods by B. I. Geltser, K. I. Shakhgeldyan, V. Yu. Rublev, I. G. Domzhalov, M. M. Tsivanyuk, O. I. Shekunova

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Sequence Modeling of Passive Sensing Data for Treatment Response Prediction in Major Depressive Disorder by Bochao Zou, Xiaolong Zhang, Le Xiao, Ran Bai, Xin Li, Hui Liang, Huimin Ma, Gang Wang

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The use of biochemical blood markers for early prediction of the intensity of live weight gain of calves by S. V. Nikolaev

“…Thus, the use of these coefficients to predict the rate of live weight gain in calves can increase the accuracy of the selection of animals with the desired phenotype in early postnatal ontogenesis.…”
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Psychotic relapse prediction via biomarker monitoring: a systematic review by Alexandros Smyrnis, Christos Theleritis, Christos Theleritis, Panagiotis Ferentinos, Nikolaos Smyrnis, Nikolaos Smyrnis

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Development of a FER0.4K SNP array for genomic predication of Fusarium ear rot resistance in maize by Zhao Wang, Haoqiang Zhang, Wenchao Ye, Yuchen Han, Huan Li, Zijian Zhou, Chunhui Li, Xuecai Zhang, Jianan Zhang, Jiafa Chen, Jianyu Wu

Subjects: “…Phenotype prediction…”
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Near-Infrared Spectroscopy and Wet Chemistry Dataset for Forage Nutritional Quality Assessment in Urochloa humidicolaHarvard dataverse by Rodrigo Andrés-Camelo, Johanna Mazabel, Paula Espitia-Buitrago, Rosa N. Jauregui, Juan Andrés Cardoso

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Persistent systemic inflammation is associated with poor clinical outcomes in COPD: a novel phenotype. by Alvar Agustí, Lisa D Edwards, Stephen I Rennard, William MacNee, Ruth Tal-Singer, Bruce E Miller, Jørgen Vestbo, David A Lomas, Peter M A Calverley, Emiel Wouters, Courtney Crim, Julie C Yates, Edwin K Silverman, Harvey O Coxson, Per Bakke, Ruth J Mayer, Bartolome Celli, Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) Investigators

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Phenotypic Diversity of Marfan Syndrome by Zixiang Xu, MD, Zhenghong Li, MD, Long Xiang, MD, Tao Chen, MD, Wang Lu, MD, Chang’e Guo, MD, Jinmiao Chen, MD, PhD, Bi’ang Kang, MD, PhD, Zesi Liu, MD, Daokang Xiang, MD, PhD, Liqun Sun, MD, PhD, Kui Hu, MD, PhD

“…Although genetic testing is invaluable for diagnosing MFS, its capacity to forecast manifestations and predict patient outcomes is still limited. Thus, unraveling the underlying factors and mechanisms contributing to this phenotypic variability is crucial. …”
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ACE phenotyping in human heart. by Victoria E Tikhomirova, Olga A Kost, Olga V Kryukova, Elena Z Golukhova, Naida I Bulaeva, Aigerim Z Zholbaeva, Leo A Bokeria, Joe G N Garcia, Sergei M Danilov

“…<h4>Methods and results</h4>We performed ACE phenotyping (ACE levels, conformation and kinetic characteristics) in the human heart and compared it with that in the lung. …”
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P112: Phenotype prediction using cell-free DNA fragmentation as a predictor of clinical severity in neurofibromatosis type 1 (NF1) by Maha Tageldein, Derek Wong, Julia Sobotka, Carolina Barnett-Tapia, Hamid Sadeghian, Emily Thain, Katharine Cardiff, Particia Parkin, Raymond Kim, Trevor Pugh

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