Digital Commons Network^™

Heterogeneous Multi-Layered Network Model For Omics Data Integration And Analysis, Bohyun Lee, Shuo Zhang, Aleksandar Poleksic, Lei Xie

Faculty Work

Advances in next-generation sequencing and high-throughput techniques have enabled the generation of vast amounts of diverse omics data. These big data provide an unprecedented opportunity in biology, but impose great challenges in data integration, data mining, and knowledge discovery due to the complexity, heterogeneity, dynamics, uncertainty, and high-dimensionality inherited in the omics data. Network has been widely used to represent relations between entities in biological system, such as protein-protein interaction, gene regulation, and brain connectivity (i.e. network construction) as well as to infer novel relations given a reconstructed network (aka link prediction). Particularly, heterogeneous multi-layered network (HMLN) has proven successful …

Customer And Employee Social Media Comments/Feedback And Stock Purchasing Decisions Enhanced By Sentiment Analysis, Drew Mikel Hall

Walden Dissertations and Doctoral Studies

The U.S. Securities and Exchange Commission (SEC) warns professional investors that sentiment analysis tools may lead to impulsive investment decision-making. This warning comes despite evidence showing that aided social sentiment investment decision tools can increase accurate investment decision-making by 18%. Using Fama's theory of efficient market hypothesis, the purpose of this quantitative correlational study was to examine whether customer Twitter comments and employee Glassdoor feedback sentiment predicted successful investing decisions measured by business stock prices. Two thousand records from 3 archival U.S. public NASDAQ 100 datasets from March 28, 2016, to June 15, 2016 (79 days) of 53 companies with …

Bio-Inspired Learning And Hardware Acceleration With Emerging Memories, Shruti R. Kulkarni

Dissertations

Machine Learning has permeated many aspects of engineering, ranging from the Internet of Things (IoT) applications to big data analytics. While computing resources available to implement these algorithms have become more powerful, both in terms of the complexity of problems that can be solved and the overall computing speed, the huge energy costs involved remains a significant challenge. The human brain, which has evolved over millions of years, is widely accepted as the most efficient control and cognitive processing platform. Neuro-biological studies have established that information processing in the human brain relies on impulse like signals emitted by neurons called …

9th Annual Postdoctoral Science Symposium, University Of Texas Md Anderson Cancer Center Postdoctoral Association

Annual Postdoctoral Science Symposium Abstracts

The mission of the Annual Postdoctoral Science Symposium (APSS) is to provide a platform for talented postdoctoral fellows throughout the Texas Medical Center to present their work to a wider audience. The MD Anderson Postdoctoral Association convened its inaugural Annual Postdoctoral Science Symposium (APSS) on August 4, 2011.

The APSS provides a professional venue for postdoctoral scientists to develop, clarify, and refine their research as a result of formal reviews and critiques of faculty and other postdoctoral scientists. Additionally, attendees discuss current research on a broad range of subjects while promoting academic interactions and enrichment and developing new collaborations.

Designing And Sample Size Calculation In Presence Of Heterogeneity In Biological Studies Involving High-Throughput Data., Sudhir Srivastava

Electronic Theses and Dissertations

The designing and determination of sample size are important for conducting high-throughput biological experiments such as proteomics experiments and RNA-Seq expression studies, thus leading to better understanding of complex mechanisms underlying various biological processes. The variations in the biological data or technical approaches to data collection lead to heterogeneity for the samples under study. We critically worked on the issues of technical and biological heterogeneity. The quantitative measurements based on liquid chromatography (LC) coupled with mass spectrometry (MS) often suffer from the problem of missing values (MVs) and data heterogeneity. We considered a proteomics data set generated from human kidney …

High-Performance Reductive Strategies For Big Data From Lc-Ms/Ms Proteomics, Muaaz Gul Awan

Dissertations

Mass Spectrometry (MS)-based proteomics utilizes high performance liquid chromatography in tandem with high-throughput mass spectrometers. These experiments can produce MS data sets with astonishing speed and volume that can easily reach peta-scale level, creating storage and computational problems for large-scale systems biology studies. Each spectrum output by a mass spectrometer may consist of thousands of peaks, which must all be processed to deduce the corresponding peptide. However, only a small percentage of peaks in a spectrum are useful for further processing, as most of the peaks are either noise or are not useful. Our experiments have shown that 90 to …

Cophosk: A Method For Comprehensive Kinase Substrate Annotation Using Co-Phosphorylation Analysis, Marzieh Ayati, Danica Wiredja, Daniela Schlatzer, Sean Maxwell, Ming Li, Mehmet Koyutürk, Mark R. Chance

Computer Science Faculty Publications

We present CoPhosK to predict kinase-substrate associations for phosphopeptide substrates detected by mass spectrometry (MS). The tool utilizes a Naïve Bayes framework with priors of known kinase-substrate associations (KSAs) to generate its predictions. Through the mining of MS data for the collective dynamic signatures of the kinases’ substrates revealed by correlation analysis of phosphopeptide intensity data, the tool infers KSAs in the data for the considerable body of substrates lacking such annotations. We benchmarked the tool against existing approaches for predicting KSAs that rely on static information (e.g. sequences, structures and interactions) using publically available MS data, including breast, colon, …

Deep Learning Based Medical Image Analysis With Limited Data, Jiaxing Tan

Dissertations, Theses, and Capstone Projects

Deep Learning Methods have shown its great effort in the area of Computer Vision. However, when solving the problems of medical imaging, deep learning’s power is confined by limited data available. We present a series of novel methodologies for solving medical imaging analysis problems with limited Computed tomography (CT) scans available. Our method, based on deep learning, with different strategies, including using Generative Adversar- ial Networks, two-stage training, infusing the expert knowledge, voting based or converting to other space, solves the data set limitation issue for the cur- rent medical imaging problems, specifically cancer detection and diagnosis, and shows very …

Empirical Bayesian Approach To Testing Multiple Hypotheses With Separate Priors For Left And Right Alternatives, Naveen K. Bansal, Mehdi Maadooliat, Steven J. Schrodi

Mathematics, Statistics and Computer Science Faculty Research and Publications

We consider a multiple hypotheses problem with directional alternatives in a decision theoretic framework. We obtain an empirical Bayes rule subject to a constraint on mixed directional false discovery rate (mdFDR≤α) under the semiparametric setting where the distribution of the test statistic is parametric, but the prior distribution is nonparametric. We proposed separate priors for the left tail and right tail alternatives as it may be required for many applications. The proposed Bayes rule is compared through simulation against rules proposed by Benjamini and Yekutieli and Efron. We illustrate the proposed methodology for two sets of …

Bird Abundance At Bird Feeders In Response To Temperature, Wind Speed And Precipitation During The Winter Season, Siddhant Kahal

Master's Theses

The goal of this project is to explore how 23 different bird species respond to 3 climatic attributes. These attributes are lower than average temperatures, wind speed and precipitation level. Information about the bird species and all of the data associated with them is provided by Project FeederWatch (PFW). This is a citizen based survey study that provides key information about bird species abundance through the use of backyard and community feeders. The study volunteers from across the United States and Canada monitor these bird feeders and note important information about the species such as the number of individuals seen. …

Type I General Exponential Class Of Distributions, Gholamhossein G. Hamedani, Haitham M. Yousof, Mahdi Rasekhi, Morad Alizadeh, Seyed Morteza Najibi

Mathematics, Statistics and Computer Science Faculty Research and Publications

We introduce a new family of continuous distributions and study the mathematical properties of the new family. Some useful characterizations based on the ratio of two truncated moments and hazard function are also presented. We estimate the model parameters by the maximum likelihood method and assess its performance based on biases and mean squared errors in a simulation study framework.

Ultra-Fast And Memory-Efficient Lookups For Cloud, Networked Systems, And Massive Data Management, Ye Yu

Theses and Dissertations--Computer Science

Systems that process big data (e.g., high-traffic networks and large-scale storage) prefer data structures and algorithms with small memory and fast processing speed. Efficient and fast algorithms play an essential role in system design, despite the improvement of hardware. This dissertation is organized around a novel algorithm called Othello Hashing. Othello Hashing supports ultra-fast and memory-efficient key-value lookup, and it fits the requirements of the core algorithms of many large-scale systems and big data applications. Using Othello hashing, combined with domain expertise in cloud, computer networks, big data, and bioinformatics, I developed the following applications that resolve several major …

Phosphoproteomics Profiling Of Nonsmall Cell Lung Cancer Cells Treated With A Novel Phosphatase Activator, Danica Wiredja, Marzieh Ayati, Sahar Mazhar, Jaya Sangodkar, Sean Maxwell, Daniela Schlatzer, Goutham Narla, Mehmet Koyutürk, Mark R. Chance

Computer Science Faculty Publications

Activation of protein phosphatase 2A (PP2A) is a promising anti-cancer therapeutic strategy, as this tumor suppressor has the ability to coordinately downregulate multiple pathways involved in the regulation of cellular growth and proliferation. In order to understand the systems-level perturbations mediated by PP2A activation, we carried out mass spectrometry-based phosphoproteomic analysis of two KRAS mutated non-small cell lung cancer (NSCLC) cell lines (A549 and H358) treated with a novel Small Molecule Activator of PP2A (SMAP). Overall, this permitted quantification of differential signaling across over 1,600 phosphoproteins and 3,000 phosphosites. Kinase activity assessment and pathway enrichment implicated collective downregulation of RAS …

Network Exploration Of Correlated Multivariate Protein Data For Alzheimer's Disease Association, Matthew J. Lane

Theses

Alzheimer Disease (AD) is difficult to diagnose by using genetic testing or other traditional methods. Unlike diseases with simple genetic risk components, there exists no single marker determining as to whether someone will develop AD. Furthermore, AD is highly heterogeneous and different subgroups of individuals develop the disease due to differing factors. Traditional diagnostic methods using perceivable cognitive deficiencies are often too little too late due to the brain having suffered damage from decades of disease progression. In order to observe AD at early stages prior to the observation of cognitive deficiencies, biomarkers with greater accuracy are required. By using …

Scalable Unsupervised Learning With Game Theory, Alireza Chakeri

USF Tampa Graduate Theses and Dissertations

Recently dominant sets, a generalization of the notion of the maximal clique to edge-weighted graphs, have proven to be an effective tool for unsupervised learning and have found applications in different domains. Although, they were initially established using optimization and graph theory concepts, recent work has shown fascinating connections with evolutionary game theory, that leads to the clustering game framework. However, considering size of today's data sets, existing methods need to be modified in order to handle massive data. Hence, in this research work, first we address the limitations of the clustering game framework for large data sets theoretically. We …

Integrative Pathway Analysis Pipeline For Mirna And Mrna Data, Diana Mabel Diaz Herrera

Wayne State University Theses

The identification of pathways that are involved in a particular phenotype helps us understand the underlying biological processes. Traditional pathway analysis techniques aim to infer the impact on individual pathways using only mRNA levels. However, recent studies showed that gene expression alone is unable to capture the whole picture of biological phenomena. At the same time, MicroRNAs (miRNAs) are newly discovered gene regulators that have shown to play an important role in diagnosis, and prognosis for different types of diseases. Current pathway analysis techniques do not take miRNAs into consideration. In this project, we investigate the effect of integrating miRNA …

On Detecting Faces And Classifying Facial Races With Partial Occlusions And Pose Variations, Tarik Alafif Alafif

Wayne State University Theses

In this dissertation, we present our contributions in face detection and facial race classification.

Face detection in unconstrained images is a traditional problem in computer vision community. Challenges still remain. In particular, the detection of partially occluded faces with pose variations has not been well addressed. In the first part of this dissertation, our contributions are three-fold. First, we introduce our four image datasets consisting of large-scale labeled face dataset, noisy large-scale labeled non-face dataset, CrowdFaces dataset, and CrowdNonFaces dataset intended to be used for face detection training. Second, we improve Viola-Jones (VJ) face detection results by first training a …

All Sites But Skin Cancer Incidences Analyzed Worldwide By Sex, Age, And Skin Type Over Time (1955-2007), Advancing Age, And Uvb Dose Reveals Important Carcinogenic Drivers, Dianne E. Godar, Rossen Gurov, Stephen Merrill

Mathematics, Statistics and Computer Science Faculty Research and Publications

Because we observed increasing incidences over time, advancing age, higher estrogen levels, decreasing UVB (290-315 nm) doses, or lower vitamin D₃, and Human Papillomavirus hiding in immune-privileged sites of hair follicles play roles in melanoma, we wondered if the majority of cancers might have similar carcinogenic drivers. To investigate this possibility, we performed worldwide analysis of all sites but skin cancer over time (1955-2007), advancing age, and UVB doses for males and females with all skin types and ages (0-85+) and in five age groups using IARC data. To investigate Human Papillomavirus’s role, we analyzed the incidences of …

Prediction Of Local Quality Of Protein Structure Models Considering Spatial Neighbors In Graphical Models., Woong Hee Shin, Xuejiao Kang, Jian Zhang, Daisuke Kihara

Department of Biological Sciences Faculty Publications

Protein tertiary structure prediction methods have matured in recent years. However, some proteins defy accurate prediction due to factors such as inadequate template structures. While existing model quality assessment methods predict global model quality relatively well, there is substantial room for improvement in local quality assessment, i.e. assessment of the error at each residue position in a model. Local quality is a very important information for practical applications of structure models such as interpreting/designing site-directed mutagenesis of proteins. We have developed a novel local quality assessment method for protein tertiary structure models. The method, named Graph-based Model Quality assessment method …

Improving Peptide Identification By Considering Ordered Amino Acid Usage, Ahmed Al-Qurri

Theses and Dissertations

Proteomics has made major progress in recent years after the sequencing of the genomes of a substantial number of organisms. A typical method for identifying peptides uses a database of peptides identified using tandem mass spectrometry (MS/MS). The profile of accurate mass and elution time (AMT) for peptides that need to be identified will be compared with this database. Restricting the search to those peptides detectable by MS will reduce processing time and more importantly increase accuracy. In addition, there are significant impacts for clinical studies. Proteotypic peptides are those peptides in a protein sequence that are most likely to …

Investigate Genomic 3d Structure Using Deep Neural Network, Yan Zhang

Theses and Dissertations

The 3D structures of the chromosomes play fundamental roles in essential cellular functions, e.g., gene regulation, gene expression, evolution and Hi-C technique provides the interaction density between loci on chromosomes. In this dissertation, we developed multiple algorithms, focusing the deep learning approach, to study the Hi-C datasets and the genomic 3D structures.

Building 3D structure of the genome one of the most critical purpose of the Hi-C technique. Recently, several approaches have been developed to reconstruct the 3D model of the chromosomes from HiC data. However, all of the methods are based on a particular mathematical model and lack of …

Contextual Representation Of Documents, Entities, And Faces Of People Using A News Corpus, Md Abdul Kader

Open Access Theses & Dissertations

A massive amount of unstructured data, in this information age, is composed of document collections. Examples include news articles, blog posts, scholarly publications, and reports generated by organizations as well as people. Many data mining and machine learning algorithms have been developed in the past decade to support text mining in many applications. Text mining applications cover a wide range of tasks spanning from personal information management --- to organizational decision making, to disease control through epidemic prediction, and to intelligence analysis for national security. One bottleneck has been dominating data mining and machine learning theories for all these text …

Using Software Testing Techniques To Infer Biological Models, Mikaela Cashman

School of Computing: Dissertations, Theses, and Student Research

Years of research in software testing has given us novel ways to reason about and test the behavior of complex software systems that contain hundreds of thousands of lines of code. Many of these techniques have been inspired by nature such as genetic algorithms, swarm intelligence, and ant colony optimization. However, they use a unidirectional analogy – taking from nature without giving back.

In this thesis we invert this view and ask if we can utilize techniques from testing and modeling of highly-configurable software systems to aid in the emerging field of systems biology which aims to model and predict …

Another Generalized Transmuted Family Of Distributions: Properties And Applications, Faton Merovci, Morad Alizadeh, Gholamhossein Hamedani

Mathematics, Statistics and Computer Science Faculty Research and Publications

We introduce and study general mathematical properties of a new generator of continuous distributions with two extra parameters called the Another generalized transmuted family of distributions. We present some special models. We investigate the asymptotes and shapes. The new density function can be expressed as a linear combination of exponentiated densities based on the same baseline distribution. We obtain explicit expressions for the ordinary and incomplete moments and generating functions, Bonferroni and Lorenz curves, asymptotic distribution of the extreme values, Shannon and Renyi entropies and order statistics, which hold for any baseline model, certain characterisations are presented. Further, we …

Gene Set Enrichment And Projection: A Computational Tool For Knowledge Discovery In Transcriptomes, Karl Douglas Stamm

Dissertations (1934 -)

Explaining the mechanism behind a genetic disease involves two phases, collecting and analyzing data associated to the disease, then interpreting those data in the context of biological systems. The objective of this dissertation was to develop a method of integrating complementary datasets surrounding any single biological process, with the goal of presenting the response to a signal in terms of a set of downstream biological effects. This dissertation specifically tests the hypothesis that computational projection methods overlaid with domain expertise can direct research towards relevant systems-level signals underlying complex genetic disease. To this end, I developed a software algorithm named …

Novel Dynamic Partial Reconfiguration Implementations Of The Support Vector Machine Classifier On Fpga, Hanaa Hussain, Khaled Benkrid, Hüseyi̇n Şeker

Turkish Journal of Electrical Engineering and Computer Sciences

The support vector machine (SVM) is one of the highly powerful classifiers that have been shown to be capable of dealing with high-dimensional data. However, its complexity increases requirements of computational power. Recent technologies including the postgenome data of high-dimensional nature add further complexity to the construction of SVM classifiers. In order to overcome this problem, hardware implementations of the SVM classifier have been proposed to benefit from parallelism to accelerate the SVM. On the other hand, those implementations offer limited flexibility in terms of changing parameters and require the reconfiguration of the whole device. The latter interrupts the operation …

A Computational Study Of The Evolution Of Cretan And Related Scripts, Peter Revesz

School of Computing: Conference and Workshop Papers

Crete was the birthplace of several ancient writings, including the Cretan Hieroglyphs, the Linear A and the Linear B scripts. Out of these three only Linear B is deciphered. The sound values of the Cretan Hieroglyph and the Linear A symbols are unknown and attempts to reconstruct them based on Linear B have not been fruitful. In this paper, we compare the ancient Cretan scripts with four other Mediterranean and Black Sea scripts, namely Phoenician, South Arabic, Greek and Old Hungarian. We provide a computational study of the evolution of the three Cretan and four other scripts. This study encompasses …

A Visual Analysis Of Articulated Motion Complexity Based On Optical Flow And Spatial-Temporal Features, Beau Michael Christ

School of Computing: Dissertations, Theses, and Student Research

The understanding of motion is an important problem in computer vision with applications including crowd-flow analysis, video surveillance, and estimating three-dimensional structure. A less-explored problem is the visual characterization and quantification of motion complexity. An important motion class that is prevalent in living beings is articulated motion (segments connected by joints). At present, no known standardized measure for quantifying the complexity of articulated motion exists. Such a measure could facilitate advanced motion analysis with applications including video indexing, motion comparison, and advanced biological study of visual signals in organisms.

This dissertation presents an in-depth study of the development of several …

Multiblock Discriminant Analysis For Integrative Genomic Study, Mingon Kang, Dong-Chul Kim, Chunyu Liu, Jean Gao

Computer Science Faculty Publications

Human diseases are abnormal medical conditions in which multiple biological components are complicatedly involved. Nevertheless, most contributions of research have been made with a single type of genetic data such as Single Nucleotide Polymorphism (SNP) or Copy Number Variation (CNV). Furthermore, epigenetic modifications and transcriptional regulations have to be considered to fully exploit the knowledge of the complex human diseases as well as the genomic variants. We call the collection of the multiple heterogeneous data “multiblock data.” In this paper, we propose a novel Multiblock Discriminant Analysis (MultiDA) method that provides a new integrative genomic model for the multiblock analysis …

Algorithmic Information Theory Applications In Bright Field Microscopy And Epithelial Pattern Formation, Hamid Mohamadlou

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The incredible patterns of multicellular organisms emerge as a result of the operation of Gene Regulatory Networks (GRN) that work during development. Understanding how GRNs produce these complex multicellular patterns is a significant challenge in biology. The primary goal of this dissertation is to employ Algorithmic Information Theory (AIT), also known as Kolmogorov complexity, to unravel the information complexity of GRNs and the resultant multicellular patterns. To obtain a better understanding of Kolmogorov complexity performance, first we study an application in cell image segmentation.

There are an estimated 20,000-25,000 protein-coding genes in the human genome. The sheer size of the …