首页|Prediction of anti-freezing proteins from their evolutionary profile
Prediction of anti-freezing proteins from their evolutionary profile
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News - According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from biorxiv.org: “Prediction of antifreeze proteins (AFPs) holds significant importance due to th eir diverse applications in healthcare. An inherent limitation of current AFP pr ediction methods is their reliance on unreviewed proteins for evaluation. This s tudy evaluates proposed and existing methods on an independent dataset containin g 81 AFPs and 73 non-AFPs obtained from Uniport, which have been already reviewe d by experts. Initially, we constructed machine learning models for AFP predicti on using selected compositionbased protein features and achieved a peak AUC of 0.90 with an MCC of 0.69 on the independent dataset. Subsequently, we observed a notable enhancement in model performance, with the AUC increasing from 0.90 to 0.93 upon incorporating evolutionary information instead of relying solely on the primary sequence of proteins.
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