Bioinformatics Commons^™

Characterizing Endogenous Dicer Products To Unravel Novel Rnai Biogenesis Pathways, Jacob Oche Peter

Dissertations

ABSTRACT

RNA interference (RNAi) is a pervasive gene regulatory mechanism in eukaryotes based on the action of multiple classes of small RNA (sRNA). Exploiting RNAi pathways in non-model systems have great potential for creating potent RNAi technologies. Here, we accessed RNAi-mediated control of gene expression in the two-spotted spider mite, Tetranychus urticae (T. urticae) using engineered dsRNA designed to modulate the host RNAi pathway and increase RNAi efficacy. Analysis of Dicer (Dcr) generated fragments revealed how exogenous RNAs access the host RNAi pathway in this animal, opening avenues for designing RNAi technology for their control. Further, some organisms …

Model-Based Deep Autoencoders For Characterizing Discrete Data With Application To Genomic Data Analysis, Tian Tian

Dissertations

Deep learning techniques have achieved tremendous successes in a wide range of real applications in recent years. For dimension reduction, deep neural networks (DNNs) provide a natural choice to parameterize a non-linear transforming function that maps the original high dimensional data to a lower dimensional latent space. Autoencoder is a kind of DNNs used to learn efficient feature representation in an unsupervised manner. Deep autoencoder has been widely explored and applied to analysis of continuous data, while it is understudied for characterizing discrete data. This dissertation focuses on developing model-based deep autoencoders for modeling discrete data. A motivating example of …

Knowledge-Based Analysis Of Genomic Expression Data By Using Different Machine Learning Algorithms For The Purpose Of Diagnostic, Prognostic Or Therapeutic Application, Venkata Jagan Mohan Thodima

Dissertations

With more and more biological information generated, the most pressing task of bioinformatics has become to analyze and interpret various types of data, including nucleotide and amino acid sequences, protein structures, gene expression profiling and so on. In this dissertation, we apply the data mining techniques of feature generation, feature selection, and feature integration with learning algorithms to tackle the problems of disease phenotype classification, clinical outcome and patient survival prediction from gene expression profiles.

We analyzed the effect of batch noise in microarray data on the performance of classification. Batchmatch, a batch adjusting algorithm based on double scaling method …