Biomedical Engineering and Bioengineering 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 …

Bibliometric Review On Applications Of Disease Detection Using Digital Image Processing Techniques, Jayant Jagtap, Rahil Sharma, Aryan Sinha, Nikhil Panda, Amulya Reddy

Library Philosophy and Practice (e-journal)

Advances around the field of deep learning and cognitive computing have allowed mankind to look and solve the problems of the world in a completely new way. Deep learning has been making huge advancements in the field of healthcare, which most importantly focuses upon disease detection and disease prediction. Techniques such as these have been conceptualized the idea of early detection and economical ways of treating the predicted disease in particular. Still, it has been observed that there seems to be no change in the way diagnosis of a particular disease takes place even in the 21^st generation of …

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 …

Compressive Sensing Framework For Mass Spectrometry Data Analysis, Khalfalla Ahmad Kh. Awedat

Dissertations

Mass Spectrometry (MS) data is ideal for identifying unique bio-signatures of diseases. However, the high dimensionality of MS data hinders any promising MS-based proteomics development. The goal of this dissertation is to develop an accurate classification tool by employing compressive sensing (CS). Not only can CS significantly reduce MS data dimensionality, but it also will allow for full reconstruction of original data. The framework developed in this work is based on using L2 and a mixed L2-L1 norms, allowing an overdetermined system to be resolved. The results show that the L2- based algorithm with regularization terms has a better performance …