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Filtering by: Creator Welter, Stephen C. Remove constraint Creator: Welter, Stephen C. Degree Field Computer Science Remove constraint Degree Field: Computer Science Resource Type Dissertation Remove constraint Resource Type: Dissertation

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  • Computational Elucidation of the Regulatory SNPs in the Non-Coding Regions of the Human Genome

    Creator:
    Yao, Yao
    Abstract:
    We describe a series of novel computational models, CERENKOV (Computational Elucidation of the REgulatory NonKOding Variome) and its successors CERENKOV2, CERENKOV3, and Convolutional CERENKOV3, for discriminating regulatory single nucleotide polymorphisms (rSNPs) from non-regulatory SNPs within non-coding genetic loci. The CERENKOV models are designed for recognizing rSNPs in the context of...
    Resource Type:
    Dissertation
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    Elucidation of the Regulatory SNPs in the Non-Coding Regions of the Human Genome Abstract approved: Stephen

  • Analytical Methods for Improving Causal Inference

    Creator:
    Sinha, Meghamala
    Abstract:
    Causal inference is an important analytical tool to bridge the gap between prediction and decision-making. However, learning a causal network solely from data is a challenging task. In this work, various techniques have been explored for a better and improved causal network learning from data. Firstly, the problem of learning...
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    Dissertation
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    : Analytical Methods for Improving Causal Inference Abstract approved: Stephen A. Ramsey Causal inference is

  • Machine Learning for Precision Medicine: Application to Cancer Chemotherapy Response Prediction and Wound Healing Status Assessment

    Creator:
    Wei, Qi
    Abstract:
    Ph.D. candidate Qi Wei's thesis consists of two projects: Chemotherapy Project: a study based on the research paper "Predicting chemotherapy response of various cancer types using a variational auto-encoder approach" submitted to the bioRxiv preprint archive and accepted by the BMC bioinformatics; and Wound Monitor Project: implementing and assessing analytics...
    Resource Type:
    Dissertation
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    Assessment Abstract approved: Stephen A. Ramsey Ph.D. candidate Qi Wei’s thesis consists of two projects

  • A fast division algorithm

    Creator:
    Dum, Stephen Anthony
    Abstract:
    A radix 2n non-restoring division algorithm is described. The algorithm is designed to be compatible with hardware multiprecision multiplication methods currectly used in high speed digital computers. This enables the use of the same hardware, with only changes in control logic, to be used to implement both multiplication and division....
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    Dissertation
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    A fast division algorithm STEPHEN ANTHONY DUM (Name

  • AOPS : an abstraction oriented programming system for literate programming

    Creator:
    Shum, Stephen M.
    Abstract:
    The practice of literate programming is not widespread because existing literate programming systems have some undesirable characteristics such as programming language and text processor dependence and lack of flexible tools for viewing and manipulation of the source file. This dissertation describes the literate programming system AOPS (Abstraction Oriented Programming System)...
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    Dissertation
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    literate programming AN ABSTRACT OF THE THESIS OF Stephen M. Shum for the degree of Doctor of Philosophy

  • Exploratory Study to Uncover Student Mental Models of Singly Linked Lists in the C Programming Language

    Creator:
    Almadhoun, Eman
    Abstract:
    In computer science, learning abstract fundamental programming concepts requiring students to understand memory management can be very difficult and lead to misunderstandings that carry on into advanced topics. This is especially true in data structures with abstract data types. Understanding how novice students think and reason about data structures is...
    Resource Type:
    Dissertation
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    : Exploratory Study to Uncover Student Mental Models of Singly Linked Lists in the C Programming Language

  • Deep Learning for Human and Biological Languages

    Creator:
    Hong, Junki
    Abstract:
    We explore the application of deep learning to the disparate fields of natural language processing and computational biology. Both the sentences uttered by humans as well as the RNA and protein sequences found within the cells of their bodies can be considered formal languages in computer science, as sets of...
    Resource Type:
    Dissertation
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    Adenine (A) prefers to bind with Uracil (U) and Cytosine (C) prefers to bind with Guanine (G). The

  • Learning with Limited Labeled Data in Natural Language Processing

    Creator:
    Obeidat, Rasha
    Abstract:
    The advent of deep learning models leads to a substantial improvement in a wide range of NLP tasks, achieving state-of-art performances without any hand-crafted features. However, training deep models requires a massive amount of labeled data. Labeling new data as a new task or domain emerges consumes time and efforts...
    Resource Type:
    Dissertation
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    . Liang Huang, Prof Stephen A Ramsey, Prof. Brett Tyler, and Prof. Sushma Naithani, for their insight

  • Interpretable Machine Learning: Applications in Biology and Genomics

    Creator:
    Danaee, Padideh
    Abstract:
    Machine learning (ML) and deep learning (DL) models impact our daily lives with applications in natural language modeling, image analysis, healthcare, genomics, and bioinformatics. The exponential growth of biological sequence data necessitates accompanying advances in computational methods. Although deep learning is highly effective for detecting and classifying biological sequences, challenges...
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    Dissertation
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    applicable more broadly to other areas of data science. c�Copyright by Padideh Danaee December 6, 2019

  • Learning with Partial Supervision for Clustering and Classification

    Creator:
    Pei, Yuanli
    Abstract:
    In the field of machine learning, clustering and classification are two fundamental tasks. Traditionally, clustering is an unsupervised method, where no supervision about the data is available for learning; classification is a supervised task, where fully-labeled data are collected for training a classifier. In some scenarios, however, we may not...
    Resource Type:
    Dissertation
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    triplets respectively; c) active learning of pairwise constraints for clustering, where the goal is to