Computational Learning Across Biological And Industrial Systems: Bayesian And Segmentation Models For Epigenomic And Manufacturing Data

Abstract

In this thesis, we made a complete dual-domain investigation using a machine learning approach into two different scientific areas which were epigenetic analysis of seal species and pore identification in the work of additive manufacturing. The study showcased the flexibility and strength of modern computational tools in addressing complicated issues in various biological and industrial systems. We further investigated the epigenetics by using the Bayesian Neural Network and other machine learning methods to conduct a study on the DNA methylation pattern within three pinniped species (the northern elephant seal, Hawaiian monk seal, and Weddell seal) with perfect precision on species type identification and tissue origin differences. In the manufacturing field, we proposed U-SAMNet, an uncertainty-aware self-attention multi-task network, for the pore detection in Additive Manufacturing, conveying 99.83% accuracy and 91.11% F1-score in an efficient manner. The cross-domain comparison showed several shared challenges, such as the data imbalance, uncertainty quantification, and the requirement to design a robust pre-processing pipeline. In addition, this work introduced new methods to two different fields and it showed the potential translation of machine learning to scientific practice. Index Terms: Additive manufacturing, Bayesian neural networks, DNA methylation, epigenetics, machine learning, multi-task learning, uncertainty quantification