Artificial Intelligence and Machine Learning with Moment Generating Functions to Enhance Biological Count Data Analysis

Abstract

This research presents artificial intelligence with supervised machine learning to determine the median count given training data from biological laboratory experimentation. After determining the central moment of the median, machine learning is then used to predict the population median value. When computer programs learn they access available data, then autonomously analyze the information to make informed, data-driven decisions. Moment generating function restrictions identify a parameter of interest by restricting the expected value of the moment generating function. This research proposes a theoretical foundation for achieving such predictions. A summary of the key elements underlying the statistical power of the Mann– Whitney test, a nonparametric hypothesis test for the median of count data, is also presented. A nonparametric approach allows for accurate predictions while relaxing distributional assumptions such as normality.