查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News - A patent application by the inventor B onilla, Edgar (Chino HIlls, CA, US), filed on December 23, 2023, was made availa ble online on April 25, 2024, according to news reporting originating from Washi ngton, D.C., by NewsRx correspondents. This patent application has not been assigned to a company or institution.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News - Woods Ray F. (Salem, Ohio, United Stat es) has been issued patent number 11966876, according to news reporting originat ing out of Alexandria, Virginia, by NewsRx editors. The patent’s inventors are Woods, Rachel K (Salem, OH, US), Woods, Ray F (Salem, OH, US). This patent was filed on April 21, 2021 and was published online on April 23, 20 24.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News - A patent application by the inventor Shen, Quan ( Houston, TX, US), filed on October 20, 2022, was made available online on April 25, 2024, according to news reporting originating from Washington, D.C., by News Rx correspondents. This patent application has not been assigned to a company or institution.
查看更多>>摘要：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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