首页|Data on Machine Learning Reported by Alexia Pelloux and Colleagues (Machine lear ning-based q-RASAR predictions of the bioconcentration factor of organic molecul es estimated following the organisation for economic co-operation and developmen t ...)
Data on Machine Learning Reported by Alexia Pelloux and Colleagues (Machine lear ning-based q-RASAR predictions of the bioconcentration factor of organic molecul es estimated following the organisation for economic co-operation and developmen t ...)
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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 from Lund, Sweden, by NewsRx journalists, research stated, “In this study, we utilized an innovative quantita tive read-across (RA) structure-activity relationship (q-RASAR) approach to pred ict the bioconcentration factor (BCF) values of a diverse range of organic compo unds, based on a dataset of 575 compounds tested using Organisation for Economic Co-operation and Development Test Guideline 305 for bioaccumulation in fish. In itially, we constructed the q-RASAR model using the partial least squares regres sion method, yielding promising statistical results for the training set (R =0.7 1, Q=0.68, mean absolute error [MAE]=0.54) .” The news correspondents obtained a quote from the research, “The model was furth er validated using the test set (Q=0.77, Q=0.75, MAE=0.51). Subsequently, we exp lored the q-RASAR method using other regression-based supervised machine-learnin g algorithms, demonstrating favourable results for the training and test sets. A ll models exhibited R and Q values exceeding 0.7, Q values greater than 0.6, and low MAE values, indicating high model quality and predictive capability for new , unidentified chemical substances.”
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