Sharif Digital Repository

DNA microarrays consist of DNA microscopic points that are attached to a solid surface such as glass, plastic or silicon chip and formed as an array. The fixed pieces of DNA are considered as searchers. In an experiment, we can use thousands of searchers. Therefore, any microarray consists of the same number of genetic tests as the experiment performed on all of them in parallel. Whit this ability, arrays have speeded up the biological investigations. Microarray technology can be seen as a continued development of southern blotting. However, the most important stage in this technology, analysis of data, requires reliable bioinformatics tools achieving high reliabilities. Infectious diseases,... 

Microarray which is also known as Biochip is a flat substrate of glass with the size of 1 ×1 cm on which a numerous number of biosensors are placed in an array format. Microarray DNAs are used to measure expression level of thousands of genes. Repeating these experiments in different conditions can result in patterns of expression. After preparation, the florescent sample is hybridized with the sensors of microarray surface and fluoresce intensities of the spots are measured by a special camera called CCD. The obtained pictures are examined by a computer and the spot lights converted into numerical data by image processing algorithms. Putting these numbers into matrices of size m×n is... 

Nowadays, many progresses in biology and medicine such as diagnosis of diseases and drug discoveries depend heavily on analyzing biological datasets collected from advanced machines. DNA Microarrays are amongst such machines applicable in measuring the expressions levels of thousand of genes and genotyping of a set of single nucleotide polymorphic sites to name a few. Compared to the more advanced Next Generation Sequencing (NGS) technology, the microarray platform produces lower quality of datasets. However, there has been tones of efforts to produce, process, and curate datasets from microarrays based on well designed protocols for sample preparation, hybridization, image processing, and... 

Datasets extracted from gene expression microarrays contain information about the phenotypic behavior of organisms. Turning this information into knowledge, i.e. finding associative genes with a given phenotype, is a daunting task. This is due to the high dimensionality of the data as the number of features on a gene expression microarray is usually very large. Moreover, a phenotype may change the expression pattern of a set of genes rather than changing each gene’s expression independently. To tackle the second problem, integrating other sources of information such as Protein-Protein Interaction (PPI) networks is required. In this thesis, the PPI network extracted from the String database... 

Gene expression using microarrays technology has special importance in biology studies especially in cancer researches. Using this technology we can assess expression level of several thousand genes simultaneously. One of the major applications of this technology is diseases’ diagnosis which cancers are the most important types of them. The problem in classifying microarray data is its high dimension; although number of samples hardly exceeds a hundred. This severe asymmetry and high dimensionality, results in reduction of accuracy, speed, stability and generalization of classification methods. Thus, one of the most important issues in cancer diagnosis is selection of related and useful... 

Classification is one of the most important problems in machine learning area. Reliable and successful classification is essential for diagnosing patients for further treatment. In many applications such as bioinformatics unlabeled data is abundant and available. However labeling data is much more difficult and expensive to obtain. This dissertation presents a novel transductive approach for the development of robust microarray data classification. The transduction problem is to estimate the value of classification function at the given points in the working set. This contrasts with the standard inductive learning problem of estimating the classification method at all possible values and... 

One of the branches of genetic studies is population genetics. Each population has its own characteristics due to its evolutionary history, cultural characteristics and geography, which distinguish it from other populations. Scientific and technological advances in recent decades have led to the production of new generation sequencing machines and the creation of large genetic data. These data contain important genetic information and answers to many questions about the origin of humans, the history of populations and their evolutionary process. More and better understanding of the human genome and the distance between populations can help to better understand biological mechanisms and deal... 

Gene expression microarrays include thousands of probes spotted on their surface to measure the expression level of a set of genes. Identifying the amount of a transcript level by hybridization, each probe is complementary to a fragment of a specific gene transcripts. Although probes are designed to avoid crosshybridization to non-specific transcripts, occurrences of cross-hybridizations is inevitable due to massive probes that are spotted on microarrays. The main question is whether these non-specific cross-hybridization have significant effect on the downstream analysis of gene expression microarray datasets. This thesis aims at answering to this question by considering datasets from... 

T lymphocytes or T cells are a type of white blood cell that play an important role in the immune system. Memory T cells are a subset of them that are able to reactivate when being re-exposed to the pathogen. Because of the properties of these cells, they are an attractive choice for immunotherapy. Initial memory subgroups were shown to be more persistently effective in immunotherapies. However little is known about development and differentiation of these subgroups. With the discovery of a new subset of T cells, called T memory stem cells (TSCM), They are considered to develop through four stages, naive T cells (TN), T memory stem cells (TSCM), central memory T cells (TCM) and effector... 

The classification of different tumor types is of great importance in cancer diagnosis and drug discovery. However, most previous cancer classification studies are clinical based and have limited diagnostic ability. Cancer classification using gene expression data is known to contain the keys for addressing the fundamental problems relating to cancer diagnosis. The recent advent of DNA microarray technique has made simultaneous monitoring of thousands of gene expressions possible. With this abundance of gene expression data, researchers have started to explore the possibilities of cancer classification using gene expression data and quite a number of Pattern Recognition approaches have been... 

Gene expression microarrays can be used for precision medicine and targeted therapies. The data generated by microarrays are high-dimensional causing statistical inference of any parameter a daunting task. In this thesis, it is shown that regardless of high-dimensional datasets produced by microarrays, the inference can be robust in the sense that random selection of features results in the same conclusion as far as the number of selected features are chosen appropriately. Stratifying patients with breast cancer based on their gene expression levels shows that patient subtypes are almost independent of the feature selection strategy. Moreover, using less noisy datasets coming from RNAseq...