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Machine learning techniques for automatic discovery of biomarkers and construction of predictive models have been applied for the diagnosis of colorectal cancer. Strategies such as Empirical Mode Decomposition (EMD) combined with Least Square Support Vector Machine (LS-SVM) have been proposed. Other methods using Discrete Wavelet Transform (DFT) and Support Vector Machine classifier have also been...
Mass spectrometry based high throughput proteomics are used for protein analysis and clinical diagnosis. Many machine learning methods have been used to construct classifiers based on mass spectrometry data, for discrimination between cancer stages. However, the classifiers generated by machine learning such as SVM techniques typically lack biological interpretability. We present an innovative technique...
One of the most important link in improves diagnostic accuracy and disease cure rate is accurate classification of disease. The current gene chip's development and widely applications making the diagnosis based on tumor gene expression profiling expected to be on a fast and effective clinical diagnostic method. But the sample of gene is small and the expression data is multi-variable. In this article,...
In this work, we analyze and evaluate different strategies for comparing Feature Selection (FS) schemes on High Dimensional (HD) biomedical datasets (e.g. gene and protein expression studies) with a small sample size (SSS). Additionally, we define a new feature, Robustness, specifically for comparing the ability of an FS scheme to be invariant to changes in its training data. While classifier accuracy...
Mass spectrometry (MS) data has been widely analyzed for the detection of early stage cancers. Its potential for seeking proteomic biomarkers has received a great deal of attention in recent years. In the sparse representation classification (SRC) framework, a testing sample is represented as a sparse linear combination of training samples. The coefficient vector of representation is obtained by a...
Single nucleotide polymorphisms (SNPs) are the most common form of genetic variant in humans, which can be generally classified into disease related mutations and common ones. It has been generally accepted that SNPs caused amino acid substitutions are of particular interest as candidates for affecting susceptibility to complex diseases, such as cancer, which is a serious public issue affecting millions...
As a progressive, degenerative disease, ataxia telangiectasia (A-T) is caused by a gene mutation (ATM) and is a predisposition to cancer. Understanding the impaired signaling networks caused by ATM will help minimizing the damage and finding effective therapies. The goal of this work is to investigate the dynamic change of ATM-dependent signaling pathways under the treatment of different radiation...
In this paper a novel combinational feature selection method on high throughput SELDI-TOF mass-spectroscopy data for ovarian cancer classification is developed. The proposed method includes 3 steps: dataset normalization, dimensionality reduction using feature filtering, selecting the most informative features utilizing binary particle swarm optimization. Indeed, the method employs a combination of...
The MMPs and ADAMs are cell surface proteases which belong to metalloprotease family. They play an important role in skin aging, skin disorders, anticancer therapy and other physiological disorders. Thus there arises the need to understand the relationships among various parameters of these proteins for prediction of their classes, structures and functionality. The computational approaches for prediction...
Selecting differentially expressed genes (DEGs) is one of the most important tasks in microarray applications. However, the sample sizes typically used in current cancer studies may only partially reflect the widely altered gene expressions in cancers. By analyzing three large cancer datasets, we show that, in each cancer, a wide range of functional modules are altered and have high disease classification...
Cancer is a group of complex diseases, in which a relatively large number of genes are involved. One of the main goals of cancer research is to identify genes that causally relevant to the development and progress of cancer. The increasingly identified cancer genes and availability of genomic and proteomics data provide us opportunities to identify cancer genes by computational methods. In this work,...
Mass spectrometry technique is a revolutionary tool for diagnosing early stage cancer by analyzing protein mass spectra, and for detecting biomarkers. But because of the high dimensionality of the data, feature selection is a necessary procedure before classification and analysis. In this paper we present a genetic algorithm for feature selection for prostate protein mass spectrometry data. An elitism...
In this paper we present a fully automated morphology-based technique for segmentation of nuclei in cancer tissue images and we compare it with a common technique for biomedical image processing, namely active contours. We discuss the limitations of active contours in the processing of immunohistochemical images characterized by heterogeneously stained nuclear region and noise caused by the presence...
Protein mass spectrometry is an integration of mass spectrometry and biological chip techniques, and it shows great potential for exploration of biomarkers and diagnosis of diseases. But the curse of dimensionality inherently from mass spectrometry data makes the dimensionality reduction a necessary phase of proteomic pattern recognition before classification. This paper presents a simulated annealing...
The invention of DNA microarray technology has modernized the approach of biology research in such a way that scientists can now measure the expression levels of thousands of genes simultaneously in a single experiment. Although this technology has shifted a new era in molecular classification, interpreting microarray data still remain a challenging issue due to their innate nature of “high dimensional...
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