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Cancers originating from different organs can show similar genomic alterations whereas cancers originating from the same organ can vary across patients. Therefore cancer stratification that does not depend on the tissue of the origin can play an important role to better understand cancers having similar genomic patterns irrespective of their origins. In this work, we formulated the problem as a weighted graph and communities were found using a modularity maximization based graph clustering method. We classified 3,199 subjects from twelve different cancer types into five clusters.


In this dissertation, we propose the first, to the best of our knowledge, PCA based algorithm to noninvasively recognize and classify different temporal stages of brain tumors given a large time series of MRI images. We propose an algorithm that addresses the challenging task of classifying stage of tumor over period of time while the tumor is being treated with VB-111 virotherapy. Our approach treats stage tumor recognition as a two-dimensional recognition problem.


Auditory selective attention plays a central role in the human capacity to reliably process complex sounds in multi-source environments. Stimulus reconstruction has been widely used for the investigation of selective auditory attention using multichannel electroencephalography (EEG). In particular, the influence of attention on sound representations in the brain has been modeled by linear time-variant filters and have been used to track the attentional state of individuals in multi-source environments.


Sleep-disordered breathing (SDB) is a highly prevalent condition associated with many adverse health problems. As the current means of diagnosis (polysomnography) is obtrusive and ill-suited for mass screening of the population, we explore a minimal-contact, automatic approach that uses acoustics-based methods in conjunction with pulse oximetry. We present a two-stage method for automatically classifying breathing sounds produced during sleep to track respiratory effort and predicting disordered breathing events using respiratory effort durations and oxygen desaturations.