Digital Commons Network^™

Accurate Cytogenetic Biodosimetry Through Automated Dicentric Chromosome Curation And Metaphase Cell Selection, Jin Liu, Yanxin Li, Ruth Wilkins, Canadian Nuclear Laboratories, Joan H. Knoll, Peter Rogan

Biochemistry Publications

Accurate digital image analysis of abnormal microscopic structures relies on high quality images and on minimizing the rates of false positive (FP) and negative objects in images. Cytogenetic biodosimetry detects dicentric chromosomes (DCs) that arise from exposure to ionizing radiation, and determines radiation dose received based on DC frequency. Improvements in automated DC recognition increase the accuracy of dose estimates by reclassifying FP DCs as monocentric chromosomes or chromosome fragments. We also present image segmentation methods to rank high quality digital metaphase images and eliminate suboptimal metaphase cells. A set of chromosome morphology segmentation methods selectively filtered out FP DCs …

Classification With Large Sparse Datasets: Convergence Analysis And Scalable Algorithms, Xiang Li

Electronic Thesis and Dissertation Repository

Large and sparse datasets, such as user ratings over a large collection of items, are common in the big data era. Many applications need to classify the users or items based on the high-dimensional and sparse data vectors, e.g., to predict the profitability of a product or the age group of a user, etc. Linear classifiers are popular choices for classifying such datasets because of their efficiency. In order to classify the large sparse data more effectively, the following important questions need to be answered.

1. Sparse data and convergence behavior. How different properties of a dataset, such as …

Predicting Outcomes Of Hormone And Chemotherapy In The Molecular Taxonomy Of Breast Cancer International Consortium (Metabric) Study By Biochemically-Inspired Machine Learning, Peter Rogan, Eliseos J. Mucaki, Katherina Baranova, Huy Q. Pham, Iman Rezaeian, Dimo Angelov, Alioune Ngom, Luis Rueda

Biochemistry Publications

Genomic aberrations and gene expression-defined subtypes in the large METABRIC patient cohort have been used to stratify and predict survival. The present study used normalized gene expression signatures of paclitaxel drug response to predict outcome for different survival times in METABRIC patients receiving hormone (HT) and, in some cases, chemotherapy (CT) agents. This machine learning method, which distinguishes sensitivity vs. resistance in breast cancer cell lines and validates predictions in patients; was also used to derive gene signatures of other HT (tamoxifen) and CT agents (methotrexate, epirubicin, doxorubicin, and 5-fluorouracil) used in METABRIC. Paclitaxel gene signatures exhibited the best performance, …

Improving Long Term Stock Market Prediction With Text Analysis, Tanner A. Bohn

Electronic Thesis and Dissertation Repository

The task of forecasting stock performance is well studied with clear monetary motivations for those wishing to invest. A large amount of research in the area of stock performance prediction has already been done, and multiple existing results have shown that data derived from textual sources related to the stock market can be successfully used towards forecasting. These existing approaches have mostly focused on short term forecasting, used relatively simple sentiment analysis techniques, or had little data available. In this thesis, we prepare over ten years worth of stock data and propose a solution which combines features from textual yearly …

Investigating Citation Linkage Between Research Articles, Kokou Hospice Houngbo

Electronic Thesis and Dissertation Repository

In recent years, there has been a dramatic increase in scientific publications across the globe. To help navigate this overabundance of information, methods have been devised to find papers with related content, but they are lacking in the ability to provide specific information that a researcher may need without having to read hundreds of linked papers. The search and browsing capabilities of online domain specific scientific repositories are limited to finding a paper citing other papers, but do not point to the specific text that is being cited. Providing this capability to the research community will be beneficial in terms …

Using Machine Learning To Predict Chemotherapy Response In Cell Lines And Patients Based On Genetic Expression, Dimo Angelov

Electronic Thesis and Dissertation Repository

The goal of this thesis was to examine different machine learning techniques for predicting chemotherapy response in cell lines and patients based on genetic expression. After trying regression, multi-class classification techniques and binary classification it was concluded that binary classification was the best method for training models due to the limited size of available cell line data. We found support vector machine classifiers trained on cell line data were easier to use and produced better results compared to neural networks. Sequential backward feature selection was able to select genes for the models that produced good results, however the greedy algorithm …