Application of Protein Structure Encodings and Sequence Embeddings for Transporter Substrate Prediction by Andreas Denger, Volkhard Helms

“…Furthermore, we compare the performance of these features either using a previously developed SVM model, or with a regularized feedforward neural network (FNN). When evaluating these models on sugar and amino acid carriers in <i>A. thaliana</i>, as well as on three types of ion channels in human, we found that both the DL-based features and the FNN model led to a better and more consistent classification performance compared to previous methods. …”
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Compositional criteria to predict columnar to equiaxed transitions in metal additive manufacturing by Ryan Brooke, Duyao Zhang, Dong Qiu, Mark A. Gibson, Mark Easton

“…Verification was found by reconsidering results from additional alloy systems and AM methods. The numerical CET models also predict that P is closely related to dendrite tip undercooling at high growth velocities, as found in AM. …”
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Conventional and optional cardiovascular risk factors in predicting outcome in hemodialysis patients by O. A. Bilevich, N. V. Ovsyannikov, E. V. Usacheva, O. M. Kulikova

“…Aim. To evaluate the frequency and effects on prognosis of conventional and optional cardiovascular risk (CVR) factors in hemodialysis patients.Material and methods. …”
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Analysis of influencing factors and prediction model construction for asthenopia in mental laborers by WANG Hui, MA Xiaolu, ZHANG Yun, ZHANG Lingling, LIU Hui, ZHANG Zhenhua, SUN Jing, GU Jun

“…Univariate and multivariate Logistic regression analyses were conducted to identify factors associated with asthenopia and to construct a predictive model. The performance of the predictive model was comprehensively evaluated and validated using the receiver operating characteristic (ROC) curve and area under curve (AUC), calibration curve, decision curve analysis, and Bootstrap resampling method. …”
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Development of a nomogram for predicting the risk of carcinoma in chronic atrophic gastritis by Jia-Yi Zhang, Ding Li, Guo-Jie Hu

“…Abstract Objective To construct a machine learning (ML) model to predict the progression of chronic atrophic gastritis (CAG) to gastric cancer (GC), given its precancerous significance. …”
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Predicting time-to-first cancer diagnosis across multiple cancer types by Kien Lau, Gregory R. Hart, Jun Deng

“…We developed predictive models using the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial for training and the UK Biobank for evaluation to estimate time-to-first cancer diagnosis for high-incidence cancers, including lung, liver, and bladder cancers. …”
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Purchasing Prediction Using Machine Learning Algorithms for Optimizing Inventory Management by Reza Hamdi Prayetno, Rani Destika Purba, Kyrene Wirawan, Kelvin Sweet, Evta Indra

“…The LSTM method is used to predict spare parts stock with significant accuracy, demonstrated through evaluation metrics: Mean Absolute Error (MAE) 12%, Mean Squared Error (MSE) 2%, and Root Mean Square Error (RMSE) 15%. …”
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Pretreatment Demodex infestation predicts dupilumab-associated blepharoconjunctivitis in atopic dermatitis by Paulina Stepinska, Kamila Zawadzinska-Halat, Maciej Pastuszczak

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Predicting cancer risk using machine learning on lifestyle and genetic data by Mohamed Abdelmoaty Ahmed, Ahmed AbdelMoety, Asmaa Mohamed Ahmed Soliman

“…Nine supervised learning algorithms were evaluated and compared, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVMs), and several ensemble methods. …”
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Temporally robust models for predicting seed yield of moist‐soil plants by Joshua M. Osborn, Heath M. Hagy, Matthew D. McClanahan, Matthew J. Gray

“…We recommend use of updated most‐soil rapid‐assessment models incorporating seed bank estimates to predict waterfowl food availability and evaluate management practices. © 2017 The Wildlife Society.…”
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Large language models to facilitate pregnancy prediction after in vitro fertilization by Ping Cao, Ganesh Acharya, Andres Salumets, Masoud Zamani Esteki

“…Abstract We evaluated the efficacy of large language models (LLMs), specifically, generative pre‐trained transformer‐4 (GPT‐4), in predicting pregnancy following in vitro fertilization (IVF) treatment and compared its accuracy with results from an original published study. …”
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Application of <i>in vitro</i> studies to predict the pharmacokinetics of rivaroxaban tablets by A. V. Suvorova, P. A. Losenkova, Yu. V. Medvedev, E. A. Malashenko, K. K. Karnakova, N. S. Bagaeva, A. Yu. Savchenko, A. M. Poluyanov, I. E. Shohin

“…Conduct a study of rivaroxaban tablets on a physiologically relevant tester to predict pharmacokinetic profiles.Materials and methods. …”
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Application of Deep Learning to the Prediction of Solar Irradiance through Missing Data by R. Girimurugan, P. Selvaraju, Prabahar Jeevanandam, M. Vadivukarassi, S. Subhashini, N. Selvam, S. K. Hasane Ahammad, S. Mayakannan, Selvakumar Kuppusamy Vaithilingam

“…The task of predicting solar irradiance is critical in the development of renewable energy sources. …”
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Multilingual Prediction of Cognitive Impairment with Large Language Models and Speech Analysis by Felix Agbavor, Hualou Liang

“…Previous research has focused on speech analysis primarily conducted using English data, leaving multilingual settings unexplored. Methods: In this study, we present our results from the INTERSPEECH 2024 TAUKADIAL Challenge, where we aimed to automatically detect mild cognitive impairment (MCI) and predict cognitive scores for English and Chinese speakers (169 in total). …”
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Do handgrip strength and dexterity predict respiratory function in neuromuscular disease? by Ertugrul Safran, Aysel Yildiz Ozer, Hulya Nilgun Gurses

“…Methods The Vignos and Brooke Upper Extremity Functional Scales were used to evaluate functional status for patient recruitment. …”
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Enhanced Helicopter Vibration Prediction With Hybrid Sampling and Cost Mining Techniques by Jeonghun Kim, Keunho Choi, Donghee Yoo

“…Experimental results confirm that the proposed hybrid sampling methods outperform traditional oversampling and undersampling techniques in prediction performance. …”
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The q-Dixon sequence for MRI predicts osteoporotic vertebral compression fractures by Jing Zhang, Qiyuan Li, Yao Wang, Li Sun, Qingyuan Zhang, Chuanping Gao

“…ObjectivesTo evaluate whether q-Dixon sequence-based fat fraction (FF) values of the lumbar spine can predict osteoporotic vertebral compression fracture (OVCF) risk in older adult(s) osteoporosis patients.Materials &amp; methodsThirty OVCF patients and 15 osteoporosis patients were enrolled. …”
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Current applications and prospects of visceral fat in obesity diagnosis and comorbidity prediction by ZHANG Yifei, SHI Juan, XU Yuening

“…In the future, the diagnosis and clinical management of obesity and its comorbidities should integrate multiple assessment methods incorporating multidimensional stratification analysis and personalized precision evaluation to further enhance comprehensive clinical management of obesity.…”
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Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT by Rana Albelaihi

“…The proposed approach incorporates a Mobility Prediction Model to forecast client connectivity and resource availability and a Resource-Aware Client Evaluation mechanism to assess eligibility based on predicted latencies. …”
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External Validation of the International Prognosis Prediction Model of IgA Nephropathy by Lichao Hu, Yi Fang, Jiaxin Huang, Jin Liu, Lingling Xu, Weichun He

“…This investigation aimed to validate the International IgA Nephropathy Prediction Tool (IIgANPT) within a contemporary Chinese cohort.Methods Within this study,185 patients diagnosed with IgAN via renal biopsy at the Center for Kidney Disease, Second Affiliated Hospital of Nanjing Medical University, between January 2012 and December 2021, were encompassed. …”
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