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An Adaptive Clustering Algorithm for Gene Expression Time-Series Data

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  • Wed, 09/20/2017 - 2:00pm - 3:30pm

An Adaptive Clustering Algorithm for Gene Expression Time-Series Data

MSc Thesis Proposal by:

Naveen Mangalakumar

Date:  Wednesday, September 20th, 2017
Time:  2: 00 pm – 3:30 pm
Location: 3105, Lambton Tower

Abstract: Studying gene expression through various time intervals of breast cancer survival may provide insights into the recovery of the disease. In this work, we propose a hierarchical clustering method to separate dissimilar groups of gene time-series profiles, which have the furthest distances from the rest of the profiles throughout different time intervals. The isolated outliers can be used as potential biomarkers of breast cancer survivability. We partition the time axis (time points) into bins of length six months starting from 0-6 up to 49-55 month intervals and, for each gene, we average its expression level over all patients who appear in a survival bin. Gene expressions throughout those time points are cubic spline interpolated to create a trending profile for each gene. After universally aligning the profiles to minimize the vertical area between each pair of profiles, we cluster them using hierarchical clustering based on minimized vertical distances. To the best of our knowledge, this work is the first time-series model that is built on the survival time of the patient after the treatment.

Thesis Committee:
Internal Reader: Dr. Mehdi Kargar
External Reader: Dr. Andrew Swan
Advisor: Dr. Luis Rueda, Dr.Alioune Ngom

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