Physical Sciences and Mathematics Commons^™

Creation Of A Digital Storage System For Genome Sequencing Metadata, Jacquelin W. Olexa

Undergraduate Theses, Professional Papers, and Capstone Artifacts

As the field of computational genomics continues to expand in both potential and application, it is now more imperative than ever to ensure that massive genetic sequencing datasets are properly stored in an accessible manner. This project sought to establish a practical, user-friendly, secure system for a genomics research lab (the Good Lab; thegoodlab.org) at the University of Montana. A MySQL database and connected web application was ruled the best configuration to maximize utility and accessibility for the lab’s researchers. Building the logical framework for the database, creating the server, and sourcing data occurred over several months. The dataset ranged …

Ungrading: Reflections Through A Feminist Pedagogical Lens, Erin M. Eggleston, Shelby Kimmel

Feminist Pedagogy

Ungrading is a pedagogical approach in which no grades are given on any assignments. Instead, students are provided with many opportunities to submit work and gain feedback. The goal is to shift student focus from achieving a grade to growth as a learner and a person. As instructors, our ungrading approach utilized personalized learning plans, checkpoint reflections, and student-professor learning conferences to put agency in the hands of our students. We employed this method in upper-level biology and computer science courses and provide critical reflections here regarding our experiences and the connections between this approach and feminist STEM pedagogy tenets. …

Deep Learning Image Analysis To Isolate And Characterize Different Stages Of S-Phase In Human Cells, Kevin A. Boyd, Rudranil Mitra, John Santerre, Christopher L. Sansam

SMU Data Science Review

Abstract. This research used deep learning for image analysis by isolating and characterizing distinct DNA replication patterns in human cells. By leveraging high-resolution microscopy images of multiple cells stained with 5-Ethynyl-2′-deoxyuridine (EdU), a replication marker, this analysis utilized Convolutional Neural Networks (CNNs) to perform image segmentation and to provide robust and reliable classification results. First multiple cells in a field of focus were identified using a pretrained CNN called Cellpose. After identifying the location of each cell in the image a python script was created to crop out each cell into individual .tif files. After careful annotation, a CNN was …

Deep Learning For Skin Photoaging, Gokul Srinivasan

Computer Science Senior Theses

Skin photoaging is the premature aging of skin that results from ultraviolet light exposure. It is a major risk factor for the development of skin cancer, among other malignant skin pathologies. Accordingly, understanding its etiology is important for both preventative and reparative clinical action. In this study, skin samples obtained from patients with ranging solar elastosis grades – a proxy for skin photoaging – were sequenced using next-generation sequencing techniques to further understand the genomic, epigenomic, and histological signs and signals of skin photoaging. The results of this study suggest that tissues with severe photoaging exhibit increases in the frequency …

Automated Classification Of Pectinodon Bakkeri Teeth Images Using Machine Learning, Jacob A. Bahn

MS in Computer Science Project Reports

Microfossil dinosaur teeth are studied by paleontologists in order to better under- stand dinosaurs. Currently, tooth classification is a long, manual, error-ridden process. Deep learning offers a solution that allows for an automated way of classifying images of these microfossil teeth. In this thesis, we aimed to use deep learning in order to develop an automated approach for classifying images of Pectinodon bakkeri teeth. The proposed model was trained using a custom topology and it classified the images based on clusters created via K-Means. The model had an accuracy of 71%, a precision of 71%, a recall of 70.5%, and …

The Role Of Machine Learning And Network Analyses In Understanding Microbial Composition In An Experimental Prairie, Ali Eastman Oku

Graduate Research Theses & Dissertations

Machine learning and network analyses are powerful modern tools can process and map out connections between large amount of ecological data from complex environmental communities. Random forests, an ensemble machine learning algorithm, are particularly powerful as they can capture complex patterns in data while remaining easily interpretable. These tools are specifically useful in experimental settings where different types of data are collected. The aim of this study was to demonstrate the utility of machine learning models and network analyses at analyzing diverse ecological data from dynamic plant-soil microbial communities in a prairie ecosystem. Our experimental system is an experimental prairie …

Simulating Polistes Dominulus Nest-Building Heuristics With Deterministic And Markovian Properties, Benjamin Pottinger

Undergraduate Honors Theses

European Paper Wasps (Polistes dominula) are social insects that build round, symmetrical nests. Current models indicate that these wasps develop colonies by following simple heuristics based on nest stimuli. Computer simulations can model wasp behavior to imitate natural nest building. This research investigated various building heuristics through a novel Markov-based simulation. The simulation used a hexagonal grid to build cells based on the building rule supplied to the agent. Nest data was compared with natural data and through visual inspection. Larger nests were found to be less compact for the rules simulated.

Ubjective Information And Survival In A Simulated Biological System, Tyler S. Barker, Massimiliano Pierobon, Peter J. Thomas

School of Computing: Faculty Publications

Information transmission and storage have gained traction as unifying concepts to characterize biological systems and their chances of survival and evolution at multiple scales. Despite the potential for an information-based mathematical framework to offer new insights into life processes and ways to interact with and control them, the main legacy is that of Shannon’s, where a purely syntactic characterization of information scores systems on the basis of their maximum information efficiency. The latter metrics seem not entirely suitable for biological systems, where transmission and storage of different pieces of information (carrying different semantics) can result in different chances of survival. …

Japanese Quail (Coturnix Japonica) As A Novel Model To Study The Relationship Between The Avian Microbiome And Microbial Endocrinology-Based Host-Microbe Interactions, Joshua M. Lyte, James Keane, Julia Eckenberger, Nicholas Anthony, Sandip Shrestha, Daya Marasini, Karrie M. Daniels, Valentina Caputi, Annie M. Donoghue, Mark Lyte

Department of Computer Science Publications

Microbial endocrinology, which is the study of neuroendocrine-based interkingdom signaling, provides a causal mechanistic framework for understanding the bi-directional crosstalk between the host and microbiome, especially as regards the effect of stress on health and disease. The importance of the cecal microbiome in avian health is well-recognized, yet little is understood regarding the mechanisms underpinning the avian host-microbiome relationship. Neuroendocrine plasticity of avian tissues that are focal points of host-microbiome interaction, such as the gut and lung, has likewise received limited attention. Avian in vivo models that enable the study of the neuroendocrine dynamic between host and microbiome are needed. …

The Role Of Software Engineering In Bioinformatics, Brendan Sean Lawlor

Theses

This thesis proposes that by applying state-of-the-art software engineering tools, techniques and frameworks to currently recognised challenges in bioinformatics, improved outcomes can be attained in that field. It begins by decomposing software engineering into two categories, namely process and architecture, and choosing two key challenges in the practice of bioinformatics: reproducibility and scalability. The body of the thesis is an exploration of the intersection between these two software engineering categories and these two bioinformatics challenges. The question is asked: Can best practices in professional software engineering be applied to address key issues in the bioinformatics domain, creating positive outcomes? And …

Computational Simulation And Analysis Of Neuroplasticity, Madison E. Yancey

Browse all Theses and Dissertations

Homeostatic synaptic plasticity is the process by which neurons alter their activity in response to changes in network activity. Neuroscientists attempting to understand homeostatic synaptic plasticity have developed three different mathematical methods to analyze collections of event recordings from neurons acting as a proxy for neuronal activity. These collections of events are from control data and treatment data, referring to the treatment of neuron cultures with pharmacological agents that augment or inhibit network activity. If the distribution of control events can be functionally mapped to the distribution of treatment events, a better understanding of the biological processes underlying homeostatic synaptic …

Characterizing The Behavior Of Mutated Proteins With Emcap: The Energy Minimization Curve Analysis Pipeline, Matthew Lee, Bodi Van Roy, Filip Jagodzinski

WWU Honors College Senior Projects

Studies of protein mutants in wet laboratory experiments are expensive and time consuming. Computational experiments that simulate the motions of protein with amino acid substitutions can complement wet lab experiments for studying the effects of mutations. In this work we present a computational pipeline that performs exhaustive single-point amino acid substitutions in silico. We perform energy minimization as part of molecular dynamics (MD) of our generated mutant proteins, and the wild type, and log the energy potentials for each step of the simulations. We motivate several metrics that rely on the energy minimization curves of the wild type and mutant, …

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.

The Open World Microorganism, Stephen Thompson

Stephen Thompson

Software Development For Genome Sequence Analysis, David Farr

Symposium Of University Research and Creative Expression (SOURCE)

The cost of genome sequencing has decreased rapidly, expanding availability for many biological applications (Muir 2016). For example, researchers can now obtain genome sequences from multiple populations under different types of selection. Comparison of these sequences allows for identification of chromosome regions and specific genes associated with adaptive evolution (Kelly 2013). As an increasing number of researchers engage in this type of inquiry, many have created in-house computer scripts to analyze the raw sequence data (e.g., Kelly 2013), creating a gap in both continuity and standardization.

Using a test dataset and preliminary results from an ongoing artificial selection experiment in …

2016-01-A3dsrinp-Csc-Sta-Cmb-522-Bps-542, Raymond Pulver, Neal Buxton, Xiaodong Wang, John Lucci, Jean Yves Hervé, Lenore Martin

Bioinformatics Software Design Projects

Cholesterol is carried and transported through bloodstream by lipoproteins. There are two types of lipoproteins: low density lipoprotein, or LDL, and high density lipoprotein, or HDL. LDL cholesterol is considered “bad” cholesterol because it can form plaque and hard deposit leading to arteries clog and make them less flexible. Heart attack or stroke will happen if the hard deposit blocks a narrowed artery. HDL cholesterol helps to remove LDL from the artery back to the liver.

Traditionally, particle counts of LDL and HDL plays an important role to understanding and prediction of heart disease risk. But recently research suggested that …

Selective Mutation Accumulation: A Computational Model Of The Paternal Age Effect, Eoin C. Whelan, Alexander C. Nwala, Christopher Osgood, Stephan Olariu

Biological Sciences Faculty Publications

Motivation: As the mean age of parenthood grows, the effect of parental age on genetic disease and child health becomes ever more important. A number of autosomal dominant disorders show a dramatic paternal age effect due to selfish mutations: substitutions that grant spermatogonial stem cells (SSCs) a selective advantage in the testes of the father, but have a deleterious effect in offspring. In this paper we present a computational technique to model the SSC niche in order to examine the phenomenon and draw conclusions across different genes and disorders.

Results: We used a Markov chain to model the probabilities of …

Rfid Microscope Lab, Patricia Carranza

Computer Engineering

The RFID Microscope Lab is a new exhibit created for the San Luis Obispo Children's Museum. The goal of the project is to create a modern, interactive exhibit that will teach children about different natural specimens through the use of technology. A computer, custom software, and RFID technology, will be used to display facts, microscopic images, and short videos of ten different specimens.

Evaluation Of The Signature Molecular Descriptor With Blosum62 And An All-Atom Description For Use In Sequence Alignment Of Proteins, Lindsay M. Aichinger

Williams Honors College, Honors Research Projects

This Honors Project focused on a few aspects of this topic. The second is comparing the molecular signature kernels to three of the BLOSUM matrices (30, 62, and 90) to test the accuracy of the mathematical model. The kernel matrix was manipulated in order to improve the relationship by focusing on side groups and also by changing how the structure was represented in the matrix by increasing the initial height distance from the central atom (Height 1 and Height 2 included).

There were multiple design constraints for this project. The first was the comparison with the BLOSUM matrices (30, 62, …

Beyond Computer Science: Computational Thinking Across Disciplines, Amber Settle, Debra S. Goldberg, Valerie Barr

Amber Settle

A Mesh Generation And Machine Learning Framework For Drosophila Gene Expression Pattern Image Analysis, Wenlu Zhang, Daming Feng, Rongjian Li, Andrey Chernikov, Nikos Chrisochoides, Christopher Osgood, Charlotte Konikoff, Stuart Newfeld, Sudhir Kumar, Shuiwang Ji

Computer Science Faculty Publications

Background: Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide …

Secondary Structure, A Missing Component Of Sequence- Based Minimotif Definitions, David P. Sargeant, Michael R. Gryk, Mark W. Maciejewsk, Vishal Thapar, Vamsi Kundeti, Sanguthevar Rajasekaran, Pedro Romero, Keith Dunker, Shun-Cheng Li, Tomonori Kaneko, Martin Schiller

Life Sciences Faculty Research

Minimotifs are short contiguous segments of proteins that have a known biological function. The hundreds of thousands of minimotifs discovered thus far are an important part of the theoretical understanding of the specificity of protein-protein interactions, posttranslational modifications, and signal transduction that occur in cells. However, a longstanding problem is that the different abstractions of the sequence definitions do not accurately capture the specificity, despite decades of effort by many labs. We present evidence that structure is an essential component of minimotif specificity, yet is not used in minimotif definitions. Our analysis of several known minimotifs as case studies, analysis …

Achieving High Accuracy Prediction Of Minimotifs, Tian Mi, Sanguthevar Rajasekaran, Jerlin Camilus Merlin, Michael R. Gryk, Martin Schiller

Life Sciences Faculty Research

The low complexity of minimotif patterns results in a high false-positive prediction rate, hampering protein function prediction. A multi-filter algorithm, trained and tested on a linear regression model, support vector machine model, and neural network model, using a large dataset of verified minimotifs, vastly improves minimotif prediction accuracy while generating few false positives. An optimal threshold for the best accuracy reaches an overall accuracy above 90%, while a stringent threshold for the best specificity generates less than 1% false positives or even no false positives and still produces more than 90% true positives for the linear regression and neural network …

2d Face Database Diversification Based On 3d Face Modeling, Qun Wang, Jiang Li, Vijayan K. Asari, Mohammad A. Karim, Manuel Filipe Costa (Ed.)

Electrical & Computer Engineering Faculty Publications

Pose and illumination are identified as major problems in 2D face recognition (FR). It has been theoretically proven that the more diversified instances in the training phase, the more accurate and adaptable the FR system appears to be. Based on this common awareness, researchers have developed a large number of photographic face databases to meet the demand for data training purposes. In this paper, we propose a novel scheme for 2D face database diversification based on 3D face modeling and computer graphics techniques, which supplies augmented variances of pose and illumination. Based on the existing samples from identical individuals of …

A Novel Approach To Phylogenetic Tree Construction Using Stochastic Optimization And Clustering, Ling Qin, Yixin Chen, Yi Pan, Ling Chen

Computer Science Faculty Publications

Background: The problem of inferring the evolutionary history and constructing the phylogenetic tree with high performance has become one of the major problems in computational biology.

Results: A new phylogenetic tree construction method from a given set of objects (proteins, species, etc.) is presented. As an extension of ant colony optimization, this method proposes an adaptive phylogenetic clustering algorithm based on a digraph to find a tree structure that defines the ancestral relationships among the given objects.

Conclusion: Our phylogenetic tree construction method is tested to compare its results with that of the genetic algorithm (GA). Experimental results show that …

Fast Filter-And-Refine Algorithms For Subsequence Selection, Beng-Chin Ooi, Hwee Hwa Pang, Hao Wang, Limsoon Wong, Cui Yu

Research Collection School Of Computing and Information Systems

Large sequence databases, such as protein, DNA and gene sequences in biology, are becoming increasingly common. An important operation on a sequence database is approximate subsequence matching, where all subsequences that are within some distance from a given query string are retrieved. This paper proposes a filter-and-refine algorithm that enables efficient approximate subsequence matching in large DNA sequence databases. It employs a bitmap indexing structure to condense and encode each data sequence into a shorter index sequence. During query processing, the bitmap index is used to filter out most of the irrelevant subsequences, and false positives are removed in the …