Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 17 of 17 for “"Cancer Classification"”.
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COMPOSITE KERNEL FEATURE ANALYSIS FOR CANCER CLASSIFICATION
… a promising technique for screening colorectal cancers by use of CT scans of the colon. Current CT technology allows a single image set of the colon to be acquired in 10-20 seconds, which translates into an easier, more comfortable examination than is available with other screening tests. …
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Feature selection for cancer classification using microarray gene expression data
… on gene expression levels. Its application in cancer studies has been shown great success in both diagnosis and elucidating the pathological mechanism. However, DNA microarray data usually contains thousands of genes and most of them are proved to be uninformative and redundant. Meanwhile, …
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Analysis of SELDI mass spectra for biomarker discovery and cancer classification
… (SELDI-TOF MS) for biomarker discovery and cancer classification. It investigated quantitative measures of reproducibility and found that SELDI protein profiles are affected by sample storage and processing procedure. Two new peak alignment algorithms were proposed, one of which achieved the …
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An improved directed random walk framework for cancer classification using gene expression data
Early diagnosis methods in cancer diagnosis studies are making great challenge as they require the involvement of different fields. Deoxyribonucleic acid (DNA) microarray analysis is one of the modern cancer diagnosis techniques used by scientists to measure the gene expression level changes in …
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Breast Cancer Classification of Mammographic Masses Using Circularity Max Metric, A New Method
<p>Breast cancer classification can be divided into two categories. The first category is a benign tumor, and the other is a malignant tumor. The main purpose of breast cancer classification is to classify abnormalities into benign or malignant classes and thus help physicians with further analysis …
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Gene Selection and Cancer Classification Using a Multidimensional Fuzzy Deep Learning Approach for Gene Expression Data
… techniques commonly employed for developing cancer prediction models using associated gene expression and mutation data. This thesis provides a comprehensive review of recent cancer studies that have employed gene expression data from several cancer types (i.e Breast, Lung, Kidney, Liver, …
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Microarray gene expression data analysis using machine learning and neural networks
… i.e., genetic regulatory networks inference and cancer classification, are addressed with machine learning and neural networks"--Abstract, page iii.</p>
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Convex matrix factorization for gene expression analysis
… analysis of the cell cycle and two problems in cancer classification.
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Integrative Biomarker Identification and Classification Using High Throughput Assays
… characterize the molecular fingerprints of cancer cells using gene expression, methylation, copy number, microRNA and SNP microarrays as well as next generation sequencing assays interrogating somatic mutation, insertion, deletion, translocation and structural rearrangements. Given the …
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Texture-based Deep Neural Network for Histopathology Cancer Whole Slide Image (WSI) Classification
… Whole Slide Image (WSI) analysis for cancer classification has been highlighted along with the advancements in microscopic imaging techniques. However, manual examination and diagnosis with WSIs is time-consuming and tiresome. Recently, deep convolutional neural networks have succeeded …
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Algorithms for analyzing complex structural variations in cancer genomes
Analysis of somatic alterations in cancer genomes has been accelerated through the rapid growth of the quantity, quality and depth of data generated by next-generation sequencing (NGS). Previously most of cancer genome studies were focusing on single nucleotide variations (SNVs), small insertions …
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Exploring topological data analysis in gene expression data topology-driven biomarker discovery and clinical outcome prediction in oncology
… that capture the complex relationships driving cancer development and progression. By embracing this perspective, we position Topological Data Analysis (TDA) and persistent homology at the core of a novel analytical framework designed to tackle two key challenges in cancer research: clinical …
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Network-based approaches for multi-omic data integration
… profiling data of mTOR perturbed human prostate cancer cells and mine several translation efficiency regulated modules associated with mTOR perturbation. We develop an R package, TERM, for implementation of the proposed approach which offers a useful tool for the research field. Next, we propose …
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Integrative Modeling and Analysis of High-throughput Biological Data
… cycle microarray data and Rsf-1-induced ovarian cancer microarray data. The results show that our knowledge-guided ICA approach can extract biologically meaningful regulatory modes and outperform several baseline methods for biomarker identification. Second, we propose a novel method for …
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Faster markov blanket with tabu search for efficient feature selection of microarray cancer datasets
… caused due to genetic reasons, the proper classification of genes is necessary to prescribe a cure for the same. Genes are required to be classified as per any particular characteristic that influences the cancer. Feature selection methods have been recognized as being important in this …