Master Degree / Yüksek Lisans Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/11147/3008

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Department: search.filters.department.Thesis (Master)--İzmir Institute of Technology, Molecular Biology and GeneticsSubject: search.filters.subject.Machine learning

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Now showing 1 - 2 of 2
  • Master Thesis
    Development of Visual Analysis Interfaces for Large Biological Data and Characterization of Immunomodulatory Noncoding Rna Networks Cancer
    (01. Izmir Institute of Technology, 2023) Ekiz, Hüseyin Atakan; Ekiz, Hüseyin Atakan; Ekiz, Hüseyin Atakan; 04.03. Department of Molecular Biology and Genetics; 04. Faculty of Science; 01. Izmir Institute of Technology
    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.
  • Master Thesis
    Cost and Benefit Analysis of Features Used in Machine Learning Based Pre-Mirna Detection
    (Izmir Institute of Technology, 2016) Suluyayla, Rabia; Allmaer, Jens; 01. Izmir Institute of Technology
    MicroRNAs (miRNAs) are short RNA molecules which play important roles in the post-trancriptional regulation of gene expression. Their transcription is followed by two RNA III endonuclease processing steps leading to mature miRNA formation. They are then incorporated into the RISC-complex which mediates mRNA targeting. Experimental miRNA prediction is difficult since detection relies on many factors therefore, computational methods have become indispensable. Therefore, machine learning methods rely on features describing precursor-miRNAs (pre-miRNAs) to be able to differentiate them from other hairpins in a genome. It is important to define feature groups which are informative, not highly correlated, and don’t incur a large computational cost in order to facilitate accurate miRNA detection. In this study for more than 800 pre-miRNA features the computational cost and benefit was analyzed. From these analyses five features (assl, lsr(%bp), lscm, asal and hpmfe rf I3), (four structural and one structuralthermodynamic one), which aren’t correlated, informative and are not computationally expensive are noticeable. Analyses are done with human hairpins, pseudo data; and a case study using the measles virus and the measles KEGG pathway genes. Overall calculation of human hairpins and measles virus took approximately 2 USD (United States Dollar) on Amazon web services. Supervised learning and random forest machine learning for miRNA prediction was applied and to two genes (TAB2 and BCC3) within the measles KEGG pathway and three hairpins were predicted. They were found to have human mature miRNA sequences embedded in them and their already annotated targets helped enlarge the KEGG measles pathway.