biocomputing.biolab.cs.kent.edu

Faculty Supervisor: Dr. L. Gwenn Volkert

Our lab is focused on the development of heuristic based tools for
bioinformatics and medical informatics. The students working on these
projects have all demonstrated a keen desire to bridge the
communication gap between biologists and computer scientists. This
often translates into a lot of extra work such as taking courses and
reading papers outside of the traditional computer science domain. As
a group we aim to make this extra work as painless as possible by
ensuring that each project has an external faculty collaborator that
functions minimally as a domain expert to whom we may address
biological or medical questions to. Additionally we have collected a
large repository of useful tutorials and encyclopedia-like reference
materials to which all group members have access.

Current students in our research group are:

Debbie Stoffer
Amin Assareh
Nilgoun Raihani
Ben Hienmann
Shilpa Ramana
Kaveh Noorbakhsh

Alumni and previous personnel are:

John Gale
Mahesh Tamboli
Katherine Koch
Olena Andriyevska

Our current projects are as follows:

Title: Foram Analysis for Climate Change Prediction
Current Personnel: Amin Assareh
Faculty Collaborator: Dr. Joesph Ortiz

ProjectDescription:

Title: Fractal Visualization of Complex Data Using Chaos Automata
Current Personnel: Debbie Stoffer
Faculty Collaborator: Dr. Daniel Ashlock (University of Guelph)

ProjectDescription:

Title: Microarray Expression Analysis
Current Personnel: Available
Faculty Collaborator: Dr. William Lynch

ProjectDescription:

Development of machine learning based research tools for microarray
expression analysis (MEA). MEA is a rapidly growing sub-field of
bioinformatics that entails the design and development of computational
techniques for aiding biologist with the interpretation of the large
amount of data produced in microarray experiments. We are developing
web-based systems that utilize machine learning based algorithms to
effectively reduce the dimensionality of the data. The resulting data
can then be presented to the biologist using a variety of 2D and 3D
visualization tools. When possible we utilize existing visualization
tools but are also looking into useful extensions to existing
visualization tools as well as development of new visualization tools.

Title: Phylogenetic Tree Reconstruction
Current Personnel: Available
Faculty Collaborators: Dr. Andrea Schwarzbach

ProjectDescription:

Development of a phylogenetic tree reconstruction algorithm that
supports different models of evolution for constructing different parts
of the phylologeny. Current computational based phylogenetic tree
reconstruction efforts utilize a single model of evolution even if the
biological evidence indicates that the resulting tree is incorrect.
The first step of this project is to quantify how to detect where the
boundaries for different models exists. This project also entails the
combining evolutionary algorithmic approaches and a quartet puzzeling
approaches to the computation phylogenetic tree reconstruction problem.