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GPCR-A17 MAAP: mapping modulators, agonists, and antagonists to predict the next bioactive target
Published 2025-07-01“…To address the growing need for effective treatments, the GPCR-A17 Modulator, Agonist, Antagonist Predictor (MAAP) was introduced as an advanced ensemble machine learning model that combines XGBoost, Random Forest, and LightGBM to predict the functional roles of agonists, antagonists, and modulators in GPCR-A17 interactions. …”
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Machine learning predicting acute pain and opioid dose in radiation treated oropharyngeal cancer patients
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Virtual ankle-brachial index - Can we predict the immediate outcome of femorodistal bypass surgery?
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Prediction of Graduate Career Relevance Based on Academic and Non-Academic Aspects using Machine Learning
Published 2025-07-01Get full text
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Machine learning-based pattern recognition of Bender element signals for predicting sand particle-size
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Predicting Injury in Collegiate Baseball and Softball Athletes Using Functional Testing: A Pilot Study
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GLC Prediction of Dioxin-Furan and Metals Emissions from a Hazardous Waste Incineration Plant
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Ex-Ante and Ex-Post Decomposition Strategy for Ultra-Short-Term Wind Power Prediction
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Lithium-ion battery RUL prediction based on optimized VMD-SSA-PatchTST algorithm
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CHINESE YUAN EXCHANGE RATE AGAINST THE INDONESIAN RUPIAH PREDICTION USING SUPPORT VECTOR REGRESSION
Published 2024-08-01Get full text
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