Koji Ooka, Munehito Arai
Accurate prediction of protein folding mechanisms by simple structure-based statistical mechanical models
Nature communications
livestock manure one important pools antibiotic resistance genes args environment aerobic composting effectively reduce spread antibiotic resistance risk livestock manure understanding effect aerobic composting process parameters manuresourced args important control spreading risk study effects process parameters args aerobic composting pig manure explored data mining based 191 valid data collected literature machine learning ml models xgboost random forest utilized predict rate args changes pig manure composting model evaluation index xgboost model r2 0651 higher random forest r2 0490 indicating xgboost better prediction performance feature importance calculated xgboost model xgboost black box model interpreted shapley additive explanations analysis results indicated influencing factors args variation pig manure sequentially divided thermophilic period total composting period composting real time thermophilic stage average temperature findings gave insight application ml models predict decipher arg changes manure composting provided suggestions better composting manipulation optimization process parameters