Life Sciences Commons^™

Generation Of Neural Stem Cells (Nscs) From Human Fibroblasts Using Qq-Modified Sox2 And Neurod1 Proteins, Abdullah Ibrahim Alhomoudi

Wayne State University Theses

The generation of induced neural stem cells (iNSCs) and induced neuronal cells (iNCs) from somatic cells provides new avenues for basic research and potential transplantation therapies for neurological diseases. However, clinical applications must consider the tumor formation capabilities of the implanted cells, the inability of iNCs to self-renew in culture, and reprogramming methods that use retroviral transduction which permanently alter genetic network of the cells. Here we report the generation of protein-induced neural stem cells (piNSCs) from human dermal fibroblasts using QQ-SON pluripotent reprogramming as a tool to quickly reset the time clock of the human somatic fibroblasts to a …

Representation Learning With Autoencoders For Electronic Health Records, Najibesadat Sadatijafarkalaei

Wayne State University Theses

Increasing volume of Electronic Health Records (EHR) in recent years provides great opportunities for data scientists to collaborate on different aspects of healthcare research by applying advanced analytics to these EHR clinical data. A key requirement however

is obtaining meaningful insights from high dimensional, sparse and complex clinical data. Data science approaches typically address this challenge by performing feature learning in order to build more reliable and informative feature representations from clinical data followed by supervised learning. In this research, we propose a predictive modeling approach based on deep feature representations and word embedding techniques. Our method uses different deep …

Text Mining Of Variant-Genotype-Phenotype Associations From Biomedical Literature, Nafiseh Saberian

Wayne State University Theses

In spite of the efforts in developing and maintaining accurate variant databases, a large number of disease-associated variants are still hidden in the biomedical literature. Curation of the biomedical literature in an effort to extract this information is a challenging task due to i) the complexity of natural language processing, ii) inconsistent use of standard recommendations for variant description, and iii) the lack of clarity and consistency in describing the variant-genotype-phenotype associations in the biomedical literature. In this article, we employ text mining and word cloud analysis techniques to address these challenges. The proposed framework extracts the variant-gene-disease associations from …