Manifold and spatiotemporal learning on multispectral unoccupied aerial system imagery for phenotype prediction by Fared Farag, Trevis D. Huggins, Jeremy D. Edwards, Anna M. McClung, Ahmed A. Hashem, Jason L. Causey, Emily S. Bellis

“…Abstract Timeseries data captured by unoccupied aircraft systems (UASs) are increasingly used for agricultural applications requiring accurate prediction of plant phenotypes from remotely sensed imagery. …”
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Phenotyping, genome‐wide dissection, and prediction of maize root architecture for temperate adaptability by Weijun Guo, Fanhua Wang, Jianyue Lv, Jia Yu, Yue Wu, Hada Wuriyanghan, Liang Le, Li Pu

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P025: Newborn screening predicts phenotype conversion and developmental outcome in hyperphenylalaninemia by Aaron Williams, Kristian Divin, Lindsay Burrage, Fernando Scaglia, William Craigen, Vernon Sutton, Claudia Soler-Alfonso, Kevin Glinton, Ronit Marom

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Causality-aware graph neural networks for functional stratification and phenotype prediction at scale by Charalampos P. Triantafyllidis, Ricardo Aguas

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Crushing Force Prediction Method of Controlled-Release Fertilizer Based on Particle Phenotype by Linlin Sun, Xiubo Chen, Zixu Chen, Linlong Jing, Jinxing Wang, Xinpeng Cao, Shenghui Fu, Yuanmao Jiang, Hongjian Zhang

“…This study proposed a method to predict the crushing force of controlled-release fertilizer granules based on their phenotypic characteristics to prevent coating damage during production, transport, and fertilization, which could affect nutrient diffusion rates. …”
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Machine learning based metabolomic and genetic profiles for predicting multiple brain phenotypes by Xueli Zhang, Yu Huang, Shunming Liu, Shuo Ma, Min Li, Zhuoting Zhu, Wei Wang, Xiayin Zhang, Jiahao Liu, Shulin Tang, Yijun Hu, Zongyuan Ge, Honghua Yu, Mingguang He, Xianwen Shang

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Phenotype augmentation using generative AI for isocitrate dehydrogenase mutation prediction in glioma by Ha Kyung Jung, Changyong Choi, Ji Eun Park, Seo Young Park, Jae Ho Lee, Namkug Kim, Ho Sung Kim

“…These findings highlight the value of phenotype-specific augmentation for IDH prediction, while emphasizing the need to optimize augmentation proportion to avoid performance degradation.…”
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Detection and prediction of real-world severe asthma phenotypes by application of machine learning to electronic health records by Mehmet Furkan Bağcı, MSc, Toan Do, MD, Samantha R. Spierling Bagsic, PhD, Rahul F. Gomez, MD, Judy H. Jun, MD, Anna L. Ritko, MA, MPhil, Sally E. Wenzel, MD, Truong Nguyen, PhD, Yusuf Öztürk, PhD, Brian D. Modena, MD, MSc

“…Leveraging machine learning in analyzing electronic health records (EHRs) provides an opportunity to identify real-world asthma phenotypes. Objective: We utilized machine-learning techniques applied to EHRs to detect and predict real-world severe asthma (SA) phenotypes and improve the precision of asthma severity diagnoses. …”
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Using single‐plant‐omics in the field to link maize genes to functions and phenotypes by Daniel Felipe Cruz, Sam De Meyer, Joke Ampe, Heike Sprenger, Dorota Herman, Tom Van Hautegem, Jolien De Block, Dirk Inzé, Hilde Nelissen, Steven Maere

“…Here, we test a new experimental design to unravel the molecular wiring of plants and study gene–phenotype relationships directly in the field. We molecularly profiled a set of individual maize plants of the same inbred background grown in the same field and used the resulting data to predict the phenotypes of individual plants and the function of maize genes. …”
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Influenza virus genotype to phenotype predictions through machine learning: a systematic review by Laura K. Borkenhagen, Martin W. Allen, Jonathan A. Runstadler

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A Hypergraph powered approach to Phenotype-driven Gene Prioritization and Rare Disease Prediction by Shrinithi Natarajan, Niveditha Kundapuram, Nisarga Bhaskar, Sai Sailaja Policharla, Bhaskarjyoti Das

“…We have designed and implemented a sophisticated computational framework for phenotype-driven disease prediction that leverages hypergraphs and genomic data to enhance the accuracy of disease identification, leading to more precise and timely treatments for patients. …”
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DeepAT: A Deep Learning Wheat Phenotype Prediction Model Based on Genotype Data by Jiale Li, Zikang He, Guomin Zhou, Shen Yan, Jianhua Zhang

Subjects: “…phenotype prediction…”
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Hospitalised COVID-19 outcomes are predicted by hypoxaemia and pneumonia phenotype irrespective of the timing of their emergence by Jennifer LY Tsang, Laura Spatafora, Andrew P Costa, MyLinh Duong, Aaron Jones, Mats Junek, Terence Ho, Rebecca Kruisselbrink, Tyler Pitre, Brittany Salter, Bianca DeBenedictis, Jessica Kapralik, Candice Luo, Steven Qiu, Laura Dawson, Marla Beauchamp

“…Patients were stratified according to hypoxaemia/pneumonia phenotype and prevalence. Clinical parameters were compared between phenotypes using χ2 and one-way Analysis of variance (ANOVA). …”
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Genome Landscapes of Disease: Strategies to Predict the Phenotypic Consequences of Human Germline and Somatic Variation. by Rachel Karchin, Ruth Nussinov

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Phenotypes of prediabetes: pathogenesis and consequences for prediction and prevention of type 2 diabetes and cardiovascular diseases by Norbert Stefan

“…As the the hyperglycemic state of prediabetes is already associated with an increased risk of cardiometabolic diseases it is necessary to investigate the impact of phenotypes for predictive and preventive outcomes already in this early state of hyperglycemia. …”
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18 The role of PSMA expression in predicting aggressive disease phenotypes in patients with prostate cancer by G. Gandaglia, S. Scuderi, F. Barletta, A. Stabile, V. Cucchiara, F. Montorsi, A. Briganti

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Genome-Wide Association Study and Phenotype Prediction of Reproductive Traits in Large White Pigs by Hao Zhang, Shiqian Bao, Xiaona Zhao, Yangfan Bai, Yangcheng Lv, Pengfei Gao, Fuzhong Li, Wuping Zhang

“…These findings provide new molecular markers for the genetic study of reproductive traits in Large White pigs. For the phenotypic prediction of NH and NW traits, several machine learning models (GBDT, RF, LightGBM, and Adaboost.R2), as well as traditional models (GBLUP, BRR, and BL), were evaluated using SNP data in varying proportions. …”
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Prediction method of sugarcane important phenotype data based on multi-model and multi-task. by Jihong Sun, Chen Sun, Zhaowen Li, Ye Qian, Tong Li

“…The aim was to establish an intelligent model ensemble for predicting two crucial phenotypic characteristics-stem diameter and plant height-that determine sugarcane yield, ultimately enhancing the overall yield.The experimental findings indicate that the XGBoost algorithm outperforms the other seven algorithms in predicting these significant phenotypic traits of sugarcane. …”
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Genome‐scale models of metabolism and gene expression extend and refine growth phenotype prediction by Edward J O'Brien, Joshua A Lerman, Roger L Chang, Daniel R Hyduke, Bernhard Ø Palsson

“…We formalize these constraints and apply the principle of growth optimization to enable the accurate prediction of multi‐scale phenotypes, ranging from coarse‐grained (growth rate, nutrient uptake, by‐product secretion) to fine‐grained (metabolic fluxes, gene expression levels). …”
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Phenotyping Kidney Function in Young Adults With High Blood Pressure: The African‐PREDICT Study by Anja Degenaar, Ruan Kruger, Adriaan Jacobs, Catharina M. C. Mels

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