Engineering Commons^™

A Machine Learning Framework For Identifying Molecular Biomarkers From Transcriptomic Cancer Data, Md Abdullah Al Mamun

FIU Electronic Theses and Dissertations

Cancer is a complex molecular process due to abnormal changes in the genome, such as mutation and copy number variation, and epigenetic aberrations such as dysregulations of long non-coding RNA (lncRNA). These abnormal changes are reflected in transcriptome by turning oncogenes on and tumor suppressor genes off, which are considered cancer biomarkers.

However, transcriptomic data is high dimensional, and finding the best subset of genes (features) related to causing cancer is computationally challenging and expensive. Thus, developing a feature selection framework to discover molecular biomarkers for cancer is critical.

Traditional approaches for biomarker discovery calculate the fold change for each …

Brain Connectivity Networks For The Study Of Nonlinear Dynamics And Phase Synchrony In Epilepsy, Hoda Rajaei

FIU Electronic Theses and Dissertations

Assessing complex brain activity as a function of the type of epilepsy and in the context of the 3D source of seizure onset remains a critical and challenging endeavor. In this dissertation, we tried to extract the attributes of the epileptic brain by looking at the modular interactions from scalp electroencephalography (EEG). A classification algorithm is proposed for the connectivity-based separation of interictal epileptic EEG from normal. Connectivity patterns of interictal epileptic discharges were investigated in different types of epilepsy, and the relation between patterns and the epileptogenic zone are also explored in focal epilepsy.

A nonlinear recurrence-based method is …

Multimodal Imaging For Enhanced Diagnosis And For Assessing Progression Of Alzheimer’S Disease, Chunfei Li

FIU Electronic Theses and Dissertations

A neuroimaging feature extraction model is designed to extract region-based image features whose values are predicted by base learners trained on raw neuroimaging morphological variables. The main objectives are to identify Alzheimer’s disease (AD) in its earliest manifestations, and be able to predict and gauge progression of the disease through the stages of mild cognitive impairment (EMCI), late MCI (LMCI) and AD. The model was evaluated on the ADNI database and showed 75.26% accuracy for the challenging EMCI diagnosis based on the 10-fold cross-validation. Our approach also performed well for the other binary classifications: EMCI vs. LMCI (72.3%), EMCI vs. …

An Integrated Neuroimaging Approach For The Prediction And Analysis Of Alzheimer’S Disease And Its Prodromal Stages, Qi Zhou

FIU Electronic Theses and Dissertations

This dissertation proposes to combine magnetic resonance imaging (MRI), positron emission tomography (PET) and a neuropsychological test, Mini-Mental State Examination (MMSE), as input to a multidimensional space for the classification of Alzheimer’s disease (AD) and it’s prodromal stages including amnestic MCI (aMCI) and non-amnestic MCI (naMCI). An assessment is provided on the effect of different MRI normalization techniques on the prediction of AD. Statistically significant variables selected for each combination model were used to construct the classification space using support vector machines. To combine MRI and PET, orthogonal partial least squares to latent structures is used as a multivariate analysis …

Automated Detection Of Hematological Abnormalities Through Classification Of Flow Cytometric Data Patterns, Mark A. Rossman

FIU Electronic Theses and Dissertations

Flow Cytometry analyzers have become trusted companions due to their ability to perform fast and accurate analyses of human blood. The aim of these analyses is to determine the possible existence of abnormalities in the blood that have been correlated with serious disease states, such as infectious mononucleosis, leukemia, and various cancers. Though these analyzers provide important feedback, it is always desired to improve the accuracy of the results. This is evidenced by the occurrences of misclassifications reported by some users of these devices. It is advantageous to provide a pattern interpretation framework that is able to provide better classification …