A nomogram for predicting the risk of tigecycline-associated drug-induced liver injury in a Chinese population by Xin Zhang, Lanfang Li, Chuanpeng Zhang, Huixian Zhang, Haining Huang

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Predicting iron contents in the Tamra-Douahria mining site using a deep neural network approach by Zohra Kraiem, Fathi Maalaoui, Salah Bouhlel, Chaouki Sadik, Ali Sdiri

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Enhancing stroke risk prediction through class balancing and data augmentation with CBDA-ResNet50 by Muhammad Asim Saleem, Ashir Javeed, Wasan Akarathanawat, Aurauma Chutinet, Nijasri Charnnarong Suwanwela, Pasu Kaewplung, Surachai Chaitusaney, Watit Benjapolakul

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Prediction of technological properties of wheat flour by combination of UV-VIS-NIR spectroscopy and multivariate analysis methods by R. A. Platova, E. V. Zhirkova, D. A. Metlenkin, A. A. Lysenkova, Yu. T. Platov, V. A. Rassulov

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Prediction of the water solubility by a graph convolutional-based neural network on a highly curated dataset by Nadin Ulrich, Karsten Voigt, Anton Kudria, Alexander Böhme, Ralf-Uwe Ebert

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Predicting pile bearing capacity using gene expression programming with SHapley Additive exPlanation interpretation by Adil Khan, Majid Khan, Waseem Akhtar Khan, Muhammad Ali Afridi, Khawaja Atif Naseem, Ayesha Noreen

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Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast by Hua Deng, Yan Li, Yingchao Zhang, Hou Zhou, Peipei Cheng, Jia Wang, Muhammad Aqeel Ashraf

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Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility. by Irene V van Blokland, Pauline Lanting, Anil P S Ori, Judith M Vonk, Robert C A Warmerdam, Johanna C Herkert, Floranne Boulogne, Annique Claringbould, Esteban A Lopera-Maya, Meike Bartels, Jouke-Jan Hottenga, Andrea Ganna, Juha Karjalainen, Lifelines COVID-19 cohort study, COVID-19 Host Genetics Initiative, Caroline Hayward, Chloe Fawns-Ritchie, Archie Campbell, David Porteous, Elizabeth T Cirulli, Kelly M Schiabor Barrett, Stephen Riffle, Alexandre Bolze, Simon White, Francisco Tanudjaja, Xueqing Wang, Jimmy M Ramirez, Yan Wei Lim, James T Lu, Nicole L Washington, Eco J C de Geus, Patrick Deelen, H Marike Boezen, Lude H Franke

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Longitudinal history of mammographic breast density and breast cancer risk by familial risk, menopausal status, and initial mammographic density level in a high risk cohort: a nest... by Parisa Tehranifar, Erica J. Lee Argov, Shweta Athilat, Yuyan Liao, Ying Wei, Alexandra J. White, Katie M. O’Brien, Dale P. Sandler, Mary Beth Terry

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Meta-Features Extracted from Use of kNN Regressor to Improve Sugarcane Crop Yield Prediction by Luiz Antonio Falaguasta Barbosa, Ivan Rizzo Guilherme, Daniel Carlos Guimarães Pedronette, Bruno Tisseyre

“…A comparative analysis is presented with a projection based on the Uniform Manifold Approximation and Projection (UMAP) algorithm, showing that MFs had more proximity than the original features when projected, which demonstrates that MFs revealed a clear formation of well-defined clusters, with most points within each group sharing the same color, suggesting greater uniformity in the predicted values. Incorporating these MFs into subsequent regression models demonstrated improved performance, with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mover><mi>R</mi><mo>¯</mo></mover><mn>2</mn></msup></semantics></math></inline-formula> values higher than 0.9 for MF Grad Boost M3, MF GradientBoost M5, and all kNN MFs and reduced error margins compared to field-measured yield values. …”
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A multi-task deep neural network reveals inflowing river impacts for predictive lake management by Han Yan, Haoyang Fu, Zhuo Chen, An-Ran Liao, Mo-Yu Shen, Yi Tao, Yin-Hu Wu, Hong-Ying Hu

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Prediction of knee joint pain in Tai Chi practitioners: a cross-sectional machine learning approach by Yang Chen, Xiaojie Su, Fei Yao, Yushan Liu, Hua Xing, Yubin Ju, Zhiran Kang, Wuquan Sun, Lijun Yao, Li Gong

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Fatigue strength prediction of Cobalt alloys using material composition and monotonic properties: ML-based approach by Subraya Krishna Bhat, Amritanshu Ranjan, Y S Upadhyaya, Vishwanath Managuli

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Numerical Investigation of Cavitation Models Combined with RANS and PANS Turbulence Models for Cavitating Flow Around a Hemispherical Head-Form Body by Hyeri Lee, Changhun Lee, Myoung-Soo Kim, Woochan Seok

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Prediction of Lubrication Performance of Hyaluronic Acid Aqueous Solutions Using a Bayesian-Optimized BP Network by Xia Li, Feng Guo

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Improving polygenic prediction from summary data by learning patterns of effect sharing across multiple phenotypes. by Deborah Kunkel, Peter Sørensen, Vijay Shankar, Fabio Morgante

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Severe floods predictive ability: A proxy based probabilistic assessment of the Italian early warning system by Francesco Silvestro, Luca Molini, Fausto Guzzetti, Federico Schiavi, Fabio Castelli, Luca Ferraris

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Prediction of water inrush from coal seam floor based on machine learning with small sample data by Chenxi LI, Haifeng LU

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Remaining Useful Life (RUL) Prediction Based on the Bivariant Two-Phase Nonlinear Wiener Degradation Process by Lijun Sun, Yuying Liang, Zaizai Yan

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Integration of clinical and serological biomarkers in a nomogram for predicting interstitial lung disease in idiopathic inflammatory myopathies by Zhixia Wang, Jingyun Zhang, Jin Li, Xiaona Mao, Yangyang Li, Dekun Wang, Wenpeng Ge, Jingzhan Li, Changhua Liang, Zhiqiang Zhang

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