DDI-KGAT: A Graph Attention Network on Biomedical Knowledge Graph for the Prediction of Drug-Drug Interactions by Iqra Naseer Kundi, Shahzad Amin Sheikh, Fahad Mumtaz Malik, Kamran Aziz Bhatti

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A prediction approach to COVID-19 time series with LSTM integrated attention mechanism and transfer learning by Bin Hu, Yaohui Han, Wenhui Zhang, Qingyang Zhang, Wen Gu, Jun Bi, Bi Chen, Lishun Xiao

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MRMS-CNNFormer: A Novel Framework for Predicting the Biochemical Recurrence of Prostate Cancer on Multi-Sequence MRI by Tao Lian, Mengting Zhou, Yangyang Shao, Xiaqing Chen, Yinghua Zhao, Qianjin Feng

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Development and validation of a novel nomogram to predict coronary artery calcification: a single center study by Xiangnan Shen, Xia Li, Fang He, Honglei Su, Hongtao He, Guobin Kang

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Pathways to care: Factors predicting women’s access to clinic versus pharmacy-based medication abortion in Ghana by Caesar Agula, Adriana Biney, Pearl Kyei, Ayaga A. Bawah

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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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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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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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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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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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