Browsing Graduate School - MU Theses and Dissertations (MU) by Thesis Advisor "Cheng, Jianlin"
Now showing items 1-14 of 14
-
Computational optimization algorithms for protein structure refinement
(University of Missouri--Columbia, 2014) -
CONFOLD new version : contact-guided ab initio protein folding with new features
(University of Missouri--Columbia, 2019)CONFOLD is an ab initio protein folding method that can build three-dimensional models using predicted contacts and secondary structures. Under this method, we can translate contact distance map and secondary structure ... -
Deep learning bandgaps of topologically doped graphene
(University of Missouri--Columbia, 2018)Manipulation of physical and chemical properties of materials via precise doping affords an extensive range of tunable phenomena to explore. Recent advance shows that in the atomic and nano scales topological states of ... -
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 ...