DETERMINING MACROSCOPIC TRANSPORT PARAMETERS AND MICROBIOTA RESPONSE USING MACHINE LEARNING TECHNIQUES
Determining the macroscopic properties such as diffusivity, concentration, and viscosity is of paramount importance to many engineering applications. The determination of macroscopic properties from experimental or numerical data is a challenging task due to the inverse nature of these problems. Data analytic techniques with recent advances in machine learning as well as optimization techniques have enabled tackling problems that were once considered impossible to solve. In the current proposal, we focus on using Bayesian and the state of the art machine learning techniques to solve three problems that involve calculations of the macroscopic transport properties.
i) We developed a Bayesian approach to estimate the diffusion coefficient of rhodamine 6G in breast cancer spheroids. Determination of the diffusivity values of drugs in tumors is crucial to understanding drug resistivity, particularly in breast cancer tumors. To this end, we invoked Bayesian inference to solve the problem of determining the light attenuation coefficient and diffusion coefficient in breast cancer spheroids for Rhodamine 6G (R6G) as a mock drug for the tyrosine kinase inhibitor, Neratinib. We noticed that the diffusion coefficient values do not noticeably vary across a HER2+ breast cancer cell line as a function of transglutaminase 2 levels, even in the presence of fibroblast cells.
ii) We developed a multi-fidelity model to predict the rheological properties of a suspension of fibers using neural networks and Gaussian processes. Determining the rheological properties of fiber suspensions is of indispensable to many industrial applications. To this end, multi-fidelity Gaussian processes and neural networks were utilized to predict the apparent viscosity. Results indicated that with tuned hyperparameters, both the multi-fidelity Gaussian processes and neural networks lead to predictions with a high level of accuracy, where neural networks demonstrate marginally better performance.
iii) We developed machine learning models to analyze measles,
mumps, rubella, and varicella (MMRV) vaccines using Raman and absorption spectra. Monitoring the concentration of viral particles is indispensable to producing vaccines or anti-viral medications. To this end, we designed and optimized a convolutional neural network and random forest models to map spectroscopic signals to concentration values. Results indicated that when the joint Raman-absorption signals are used for training, prediction accuracies are higher, with the random forest model demonstrating marginally better performance.
iv) We developed four machine learning models, including random forest, support vector machine, artificial neural networks, and convolutional neural networks to classify diseases using gut microbiota data. We distinguished between Parkinson’s disease, Crohn’s disease (CD), ulcerative colitis (UC), human immune deficiency virus (HIV), and healthy control (HC) subjects in the
presence and absence of fiber treatments. Our analysis demonstrated that it would be possible to use machine learning to distinguish between healthy and non-healthy cases in addition to predicting four different types of diseases with very high accuracy.
- Doctor of Philosophy
- Mechanical Engineering
- West Lafayette