Contact Information

Computer Science Department
Colgate University
McGregory Hall, 3rd Floor
13 Oak Drive
Hamilton, NY 13346
(tel) 315.228.7719
(fax) 315.228.7009
department@cs.colgate.edu

Upcoming events

  • 8

    Sep

    2015

    Computer Science will participate in the 9th annual Ho Symposium on summer student research sponsored by the Division of Natural Sciences and Mathematics. Michael Chavinda, one of the student researchers working on a distinction-winning project mentored by Prof. Fourquet, will briefly present his research.


Past events

  • 1

    Sep

    2015

    Please join the COSC department for the first department tea of the semester as we welcome new and returning students back. Food will be served

  • 21

    Apr

    2015

    Join us for our next department tea! We will have two speakers, Farah Fouladi '15 and Sam Daulton '15. Each will speak about their final project from Professor Ay's course on Modeling of Biological Systems.

    Lunch will be served following the talks.

    Title: Predicting Cancer using Mutual Information-based Gene Association Networks
    Speaker: Sam Daulton '15
    Abstract: Machine-learning algorithms can be used on gene expression data as an alternative to traditional clinical methods for diagnosing cancer and determining a patient’s prognosis. A standardized, accurate approach to classifying and diagnosing colon cancer does not exist. Little is known about molecular alterations associated with the heterogeneity of the disease, and no molecular marker has been validated for clinical practice as a diagnostic or prognostic parameter. We propose a novel supervised classification algorithm, the NBC-A method, for predicting cancer from gene expression data with greater accuracy and for identifying genetic biomarkers. NBC-A improves the NBC method developed by Ay et al. in 2014 by constructing gene association networks using mutual information as the statistical metric instead of Pearson correlation. We expect NBC-A to produce transcriptional networks that are more indicative of the underlying biological pathways of colorectal cancer, leading to discovery of new biomarkers. NBC-A yields higher classification accuracies than many traditional classifiers including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Naïve Bayes (NB) on the tested colon cancer dataset. In addition, NBC-A outperforms all other methods tested (NBC, SVM, kNN, NB, and Random Forest (RF)) on a lung cancer dataset.

    Title: TBD
    Speaker: Farah Fouladi '15
    Abstract: TBD

Events history