Details

Autor(en) / Beteiligte

• Tran, Tuan Nguyen Anh

Titel

Advancing Learning Models for High-Dimensional Data: From Molecular Modeling to Motion Planning

Ort / Verlag

ProQuest Dissertations & Theses

Erscheinungsjahr

2024

Quelle

ProQuest Dissertations & Theses A&I

Beschreibungen/Notizen

• Processing and analyzing high-dimensional data, particularly in domains like protein research and robotics, introduces unique challenges. In protein analysis, high-dimensional data often arises from complex molecular structures and their interactions. Analyzing protein data involves intricate computational models and algorithms that must deal with the large size of molecular datasets and the need to extract biologically relevant information. This makes it challenging to discern significant protein structure-activity relationships, understand complex biological interactions, or predict protein behavior accurately. Similarly, in robotics, high-dimensional data is prevalent when dealing with sensory inputs, the dimensionality of the robot, or the complexity of the operational environments. High-dimensional motion planning is notoriously computationally intensive and presents challenges related to path optimization, collision avoidance, and real-time decision-making. In this research, we address these problems by introducing machine learning approaches that are able to process high dimensional data and feature analyzing techniques to extract underlying relationships within the data. Specifically, we propose multiple neural network approaches, including multilayer perceptron, autoencoder, and transformer, to reduce the complexity of the protein input data. The application of our research tackles protein-related problems such as binding pose prediction, and protein properties prediction. Overall, our work contributes a novel and efficient way to determine proteins’ characteristics and shows promising results for a specific parasite family, Plasmodium Falciparum. The findings of this research promote the development and usage of machine learning techniques to investigate multiple aspects of protein’s properties to facilitate the process of drug discovery and additionally offer a new perspective on solving high-dimensional motion planning problems.