A Study of Machine Learning Techniques in Solving Biochemical and Chemical Problems

Data-driven approaches to solving problems in biology and chemistry require utilization of reliable techniques and machine learning algorithms are the modern reliable approach. This work presents three problems that involve use of supervised learning techniques when classification is the goal and unsupervised learning techniques when global data representation is the goal. In the first problem, we demonstrate the use of unsupervised clustering techniques, self-organizing maps and K-means, to ascertain analyte detection capabilities of carbon nitride dots. In the second problem, we add scalability features to a functional group classification model applied to infrared data and evaluate its ability to inform technologists of materials aging trends. In the last problem, we compare stacking and mixture of experts ensemble techniques’ aptitudes for base calling in new microarray resequencing technologies for evolving viral genomes.