Browsing Computer Science electronic theses and dissertations (MU) by Thesis Advisor "Cheng, Jianlin"
Now showing items 1-12 of 12
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Computational optimization algorithms for protein structure refinement
(University of Missouri--Columbia, 2014) -
EM algorithm for reconstructing 3D structures of human chromosomes from chromosomal contact data
(University of Missouri--Columbia, 2016)Recent research suggested that chromosomes have preferred spatial conformations to facilitate necessary long-range interactions and regulations within a nucleus. So that, getting the 3D shape of chromosomes of a genome is ... -
Exploring deep learning techniques to tackle the sparsity problem in recommender systems
(University of Missouri--Columbia, 2020)With the inception of e-commerce in the early twenty-first century, people's lifestyles have drastically changed. People today tend to do many of their daily routines online, such as shopping, reading the news, and watching ... -
Gene expression prediction based on deep learning
(University of Missouri--Columbia, 2016)Gene expression is a critical process in a biological system that is influenced and modulated by many factors including genetic variation. Thus, it is important to understand how genotypes affect the gene expression levels. ... -
Gradient descent optimization and deep reinforcement learning for protein-protein interaction
(University of Missouri--Columbia, 2022)Reconstruction of the 3D structure of protein dimers is a crucial and challenging task. Although inter-protein contacts have been found useful in the modeling process of protein complexes, a few methods have been introduced ... -
Iterative reconstruction of three-dimensional model of human genome from chromosomal contact data
(University of Missouri--Columbia, 2014)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] 3D genome structures are important because they help us understand spatial gene regulation, transcription efficiency, genome interpretation, function ... -
PRO3DCNN : convolutional neural network for mapping protein structure into folds
(University of Missouri--Columbia, 2019)Motivation: SCOPe 2.07 is a dataset of 276,231 protein domains that have been partitioned into varying folds according to their shape and function. Since a protein's fold reveals valuable information about it's shape and ... -
Protein contact distance and structure prediction driven by deep learning
(University of Missouri--Columbia, 2023)Proteins, fundamental building blocks of living organisms, play a crucial role in various biological processes. Understanding protein structure is essential for unraveling their functions and designing therapeutics. However, ... -
Protein tertiary structure prediction and refinement using deep learning
(University of Missouri--Columbia, 2022)Building the high-quality structure of a protein from its amino acid sequence has important applications in protein engineering and drug design. The problem of accurate protein three-dimensional structure prediction from ... -
Protein-DNA interaction prediction and protein structure modeling by machine learning
(University of Missouri--Columbia, 2022)Proteins are large, complex molecules that perform most essential functions within organisms. In this work, we mainly focus on two important aspects that determine their functional properties: the tertiary structure of the ... -
Structural modeling of the 3D genome using machine learning
(University of Missouri--Columbia, 2021)This dissertation, submitted as a partial requirement for completion of the Doctorate of Philosophy, outlines the research performed by Max Highsmith in the BDM Lab. This work includes a functional expansion of a ... -
Using machine learning approach to predict enzyme family classes by fusing AM-PSE-AAC and PSE-PSSM
(University of Missouri--Columbia, 2016)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Protein function prediction is one of the most challenging problems in the postgenomic era. One approach for function prediction is to classify a ...