Fayth Keldgord, Yu-Hui Ching
Teachers' Experiences with and Perceptions of Virtual Manipulatives Following the COVID-19 Pandemic
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esophageal cancer ec remains global health challenge often diagnosed advanced stages leading high mortality rates current diagnostic tools ec limited efficacy study aims harness potential micrornas mirnas novel noninvasive diagnostic biomarkers ec objective determine diagnostic accuracy mirnas particularly distinguishing mirnas associated ec control mirnas applied machine learning ml techniques weka waikato environment knowledge analysis tensorflow keras dataset mirna sequences gene targets assessing predictive power several classifiers nave bayes multilayer perceptron hoeffding tree random forest random tree data subjected infogain feature selection identify informative mirna sequence gene target descriptors ml models abilities distinguish mirna implicated ec control group mirna tested tested weka classifiers top 3 performing ones random forest hoeffding tree nave bayes tensorflow keras neural network model subsequently trained tested models predictive power validated using independent dataset tensorflow keras gave accuracy 091 weka best algorithm nave bayes model yielded accuracy 094 results demonstrate potential mlbased mirna classifiers diagnosing ec however studies necessary validate findings explore full clinical potential approach