首页|Central China Normal University Reports Findings in Machine Learning (A Machine Learning Method for RNA-Small Molecule Binding Preference Prediction)
Central China Normal University Reports Findings in Machine Learning (A Machine Learning Method for RNA-Small Molecule Binding Preference Prediction)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating in Wuhan, People’ s Republic of China, by NewsRx journalists, research stated, “The interaction be tween RNA and small molecules is crucial in various biological functions. Identi fying molecules targeting RNA is essential for the inhibitor design and RNA-rela ted studies.” The news reporters obtained a quote from the research from Central China Normal University, “However, traditional methods focus on learning RNA sequence and sec ondary structure features and neglect small molecule characteristics, and result ing in poor performance on unknown small molecule testing. To overcome this limi tation, we developed a double-layer stacking-based machine learning model called ZHMol-RLinter. This approach more effectively predicts RNA-small molecule bindi ng preferences by learning RNA and small molecule features to capture their inte raction information. ZHMol-RLinter also combines sequence and secondary structur al features with structural geometric and physicochemical environment informatio n to capture the specificity of RNA spatial conformations in recognizing small m olecules. Our results demonstrate that ZHMol-RLinter has a success rate of 90.8% on the published RL98 testing set, representing a significant improvement over e xisting methods. Additionally, ZHMol- RLinter achieved a success rate of 77.1% on the unknown small molecule UNK96 testing set, showing substantial improvement over the existing methods. The evaluation of predicted structures confirms that ZHMol-RLinter is reliable and accurate for predicting RNA-small molecule bindin g preferences, even for challenging unknown small molecule testing.”
WuhanPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesGeneticsMachine Learning
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