Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Comparative statistical microarray analysis of yeast data under heat shock stress
Download
index.pdf
Date
2014
Author
Varol, Duygu
Metadata
Show full item record
Item Usage Stats
204
views
108
downloads
Cite This
The microarray technology is one of the widely used experimental methods in biological and biochemical sciences. By this innovation, a number of genes can be analyzed simultaneously by means of statistical methods. Hereby in this study we analyze a new one-channel microarray dataset that is measured to investigate the changes in heat shock stress of yeast. The data that are generated in the Molecular Biology and Biotechnology R-D Center at the Middle East Technical University has not been evaluated yet in different researches. Hence in this study we perform detailed comparative analyses of these measurements and critically assessed the biological findings. For this purpose, in the thesis, we implement the normalization, the detection of differentially expressed genes, multiple testing under different error rates, clustering and the search of gene annotation as well as pathway analyses by comparing the most well-known approaches in each step. Finally, the biological results are evaluated to get new knowledge about the yeast under changes in temperature.
Subject Keywords
Yeast.
,
Biometry.
,
Statistics.
URI
http://etd.lib.metu.edu.tr/upload/12617506/index.pdf
https://hdl.handle.net/11511/23685
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
Gene expression indices for single channel microarrays
Akal, Tülay; Purutçuoğlu Gazi, Vilda; Department of Statistics (2013)
The microarray technology is one of the recent and advance tools in biological sciences. This optical technology aims to measure the amount of changes in transcripted message for each gene by RNA via quantifying the colour intensity on the arrays. But due to the different exper- imental conditions, these measurements can include both systematic and random erroneous intensities. In this study, we deal with one of these systematic sources of errors, called background sig- nals, for one-channel microarrays. He...
Modeling and predicting binding affinity of phencyclidine-like compounds using machine learning methods
Erdas, Ozlem; Buyukbingol, Erdem; Alpaslan, Ferda Nur; Adejare, Adeboye (Wiley, 2010-01-01)
Machine learning methods have always been promising in the science and engineering fields, and the use of these methods in chemistry and drug design has advanced especially since the 1990s. In this study, molecular electrostatic potential (MEP) surfaces of phencyclidine-like (PCP-like) compounds are modeled and visualized in order to extract features that are useful in predicting binding affinities. In modeling, the Cartesian coordinates of MEP surface points are mapped onto a spherical self-organizing map ...
Using Adaptive Neuro-Fuzzy Inference System for Classification of Microarray Gene Expression Cancer Profiles
Haznedar, Bülent; Arslan, Mustafa Turan; Kalınlı, Adem (2018-05-01)
Microarray is a technology that enables simultaneously analysis of thousands of genes in DNA structure depending on the advances in biochemistry. With this technology, it has become possible to diagnose and treat heredity diseases by analyzing thousands of gene expression levels. This study proposes an artificial intelligence method, Adaptive neuro-fuzzy inference system (ANFIS), to classify cancer gene expression profiles. The findings obtained with the proposed ANFIS approach are compared with the results...
Computational approaches leveraging integrated connections of multi-omic data toward clinical applications
Demirel, Habibe Cansu; Tunçbağ, Nurcan (2021-10-01)
In line with the advances in high-throughput technologies, multiple omic datasets have accumulated to study biological systems and diseases coherently. No single omics data type is capable of fully representing cellular activity. The complexity of the biological processes arises from the interactions between omic entities such as genes, proteins, and metabolites. Therefore, multi-omic data integration is crucial but challenging. The impact of the molecular alterations in multi-omic data is not local in the ...
A new contribution to nonlinear robust regression and classification with mars and its applications to data mining for quality control in manufacturing
Yerlikaya, Fatma; Weber, Gerhard Wilhelm; Department of Scientific Computing (2008)
Multivariate adaptive regression spline (MARS) denotes a modern methodology from statistical learning which is very important in both classification and regression, with an increasing number of applications in many areas of science, economy and technology. MARS is very useful for high dimensional problems and shows a great promise for fitting nonlinear multivariate functions. MARS technique does not impose any particular class of relationship between the predictor variables and outcome variable of interest....
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
D. Varol, “Comparative statistical microarray analysis of yeast data under heat shock stress,” M.S. - Master of Science, Middle East Technical University, 2014.
