Development of visual analysis interfaces for large biological data and characterization of immunomodulatory noncoding rna networks cancer
tarafından
 
Kuş, Muammer Emre, author.

Başlık
Development of visual analysis interfaces for large biological data and characterization of immunomodulatory noncoding rna networks cancer

Yazar
Kuş, Muammer Emre, author.

Yazar Ek Girişi
Kuş, Muammer Emre, author.

Fiziksel Tanımlama
xii, 64 leaves: charts;+ 1 computer laser optical disc.

Özet
These days we are collecting data in higher and higher dimensions, processing it, and developing tools that have strong descriptive and predictive powers. Especially in the field of cancer, the processing of data collected from patients has substantial potential in terms of discovering new biomarkers, developing personalized treatment methods, and better prognosticators. However, there are significant difficulties in utilizing and analyzing high-dimensional data. A good level of coding skills is required to bring the data together and apply different analysis methods. With the visual interfaces created in this study, we offer the opportunity to examine and analyze the high-dimensional data of thousands of cancer patients, which are open to the public through The Cancer Genome Atlas initiative, especially for bench scientists who has no prior coding expertise. The Cancer Genome Explorer, shortly TCGEx, is a robust bioinformatic tool that we developed to facilitate high-throughput cancer data analysis through several sophisticated algorithms. With special features like subset-specific analysis and comparative analysis by using multiple cancer data, TCGEx can contribute to the literature by accelerating the studies, especially in hypothesis-driven research. This study also describes a use-case scenario that demonstrates how hypothesis-driven research can be performed using TCGExplorer for melanoma. In melanoma, elucidating the interactions between the tumor and the immune system at the miRNA level is crucial for developing new therapeutics. In this study, we characterize the properties of potential therapeutic targets that act on tumor and immune cells, which we have identified using various statistical analysis methods including machine learning, dimensionality reduction, and survival modeling using the TCGEx portal.

Konu Başlığı
Non-coding RNA.

Yazar Ek Girişi
Ekiz, Hüseyin Atakan,

Tüzel Kişi Ek Girişi
İzmir Institute of Technology. Molekular Biology and Genetics.

Tek Biçim Eser Adı
Thesis (Master)--İzmir Institute of Technology:Molecular Biology and Genetics.
 
İzmir Institute of Technology: Molekular Biology and Genetics --Thesis (Master).

Elektronik Erişim
Access to Electronic Versiyon.


LibraryMateryal TürüDemirbaş NumarasıYer NumarasıDurumu/İade Tarihi
IYTE LibraryTezT002764QP623 .K97 2023Tez Koleksiyonu