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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 License All rights reserved Remove constraint License: All rights reserved

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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
    Full Text:
    Elucidation of the Regulatory SNPs in the Non-Coding Regions of the Human Genome Abstract approved: Stephen

  • Variant Annotation

    Creator:
    He, Jun
    Abstract:
    With the development of technologies in genome sequencing and variant detection, a huge number of variants are detected. To further analyze the variants, it requires an efficient tool to annotate the functional effect of variants. This project managed to develop an efficient program to annotate the functional effect of variants...
    Resource Type:
    Capstone Project
    Full Text:
    approved: Stephen Ramsey With the development of technologies in genome sequencing and variant detection

  • Exploration of regression models for cancer noncoding mutation recurrence

    Creator:
    Xu, Tanjin
    Abstract:
    An important impact of the genome technology revolution will be the elucidation of mechanisms of cancer pathogenesis, leading to improvements in the diagnosis of cancer and the selection of cancer treatment. Integrated with current well-studied massive knowledge and findings about the role of protein-coding mutations in cancer, demystifying the functional...
    Resource Type:
    Masters Thesis
    Full Text:
    for Cancer Noncoding Mutation Recurrence Abstract approved: Stephen A. Ramsey An important impact

  • 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...
    Resource Type:
    Dissertation
    Full Text:
    : Analytical Methods for Improving Causal Inference Abstract approved: Stephen A. Ramsey Causal inference is

  • 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
    Full Text:
    : 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
    Full Text:
    Adenine (A) prefers to bind with Uracil (U) and Cytosine (C) prefers to bind with Guanine (G). The

  • A Formal Foundation for Variational Programming Using the Choice Calculus

    Creator:
    Hubbard, Spencer
    Abstract:
    In this thesis, we present semantic equivalence rules for an extension of the choice calculus and sound operations for an implementation of variational lists. The choice calculus is a calculus for describing variation and the formula choice calculus is an extension with formulas. We prove semantic equivalence rules for the...
    Resource Type:
    Masters Thesis
    Full Text:
    Coq proof assistant. c© Copyright by Spencer Hubbard June 1, 2016 All Rights Reserved A Formal

  • Visualization and analysis of sensor data for detecting microclimate cold air pools

    Creator:
    Singh, Satpreet Harcharan
    Abstract:
    Cold air pools are spatiotemporal phenomena that occur when cold air from higher elevations roll down the slope to accumulate in lower elevations. Behaviors like this lead to microclimate anomalies such as the city of Corvallis (Oregon) experiencing persistent cold weather even on a sunny day. We analyze multivariate temperature...
    Resource Type:
    Masters Thesis
    Full Text:
    studies to assess their performance under different microclimatic and sensor-performance conditions. c

  • Machine Learning Methods for the Discovery and Analysis of MicroRNAs

    Creator:
    Bell, Jimmy D.
    Abstract:
    MicroRNAs are a highly conserved class of small endogenous RNA, about ~22nt in length, involved in post-transcriptional gene silencing and have prominent roles in disease and development. Though the process of microRNA discovery was once an arduous task, the advent of high throughput sequencing technology has resulted in novel microRNAs...
    Resource Type:
    Masters Thesis
    Full Text:
    -7-Novel2 and fca-let-7-Novel3 (Figure 2.18a). The predicted novel miRs (Figure 2.18b-c) have

  • 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
    Full Text:
    . Liang Huang, Prof Stephen A Ramsey, Prof. Brett Tyler, and Prof. Sushma Naithani, for their insight