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Feature selection methods have been widely used in gene expression analysis to identify differentially expressed genes and explore potential biomarkers for complex diseases. While a lot of studies have shown that incorporating feature structure information can greatly enhance the performance of feature selection algorithms, and genes naturally fall into groups with regard to common function and co-regulation,...
There are countless ways the human body fails. Breast cancer is one of them, especially for women. It is the most common cancer of women worldwide. It has been reported by the US Breast Cancer Registry that more than 25% and up to 50% of the decline in mortality was due to the increased use of screening mammography. The detection accuracy of these mammograms could be enhanced using suitable numerical...
Feature selection is commonly used in bioinformatics applications, such as gene selection from DNA micro array data. Recently, wrapper methods have been proposed as an improvement over traditionally used filter based feature selection methods. In wrapper methods, the goodness of a feature set is often measured using the cross-validation performance of a machine learning method trained with the features...
Many image processing techniques developed over the past two decades to help radiologists in diagnosing breast cancer. At the same time, many studies proven that an early diagnosis of breast cancer can increase five year survival rate from 60% to 80+%. That made screening programs a mandatory step for females. Therefore, radiologists have to examine a large number of images, which may lead to missed...
The detection of differential gene expression in microarray data can recognize genes with significant alteration of expression level with regard to varying experimental environment. Traditional differential gene expression detecting methods work on the assumption that all cancer samples are over-expressed compared with normal samples and need to define the key criterion with the mean of sample data...
This paper proposes an improved K-Modes clustering method based on Chi-square statistics, using Chi-square statistics to characterize the relationship between the attributes of data objects. On this basis, the new distance measure is proposed, The distance measure method not only take into account the value of an attribute of an object different from itself, but also take into account other attributes'...
In this paper we present an original framework to extract representative groups from a dataset, and we validate it over a novel case study. The framework specifies the application of different clustering algorithms, then several statistical and visualisation techniques are used to characterise the results, and core classes are defined by consensus clustering. Classes may be verified using supervised...
Breast cancer is the most frequently diagnosed cancer and the most common cause of cancer death among women. In developing countries, testing for detection of this cancer involves visual microscopic test of cytology samples such as Fine Needle Aspiration Cytology (FNAC) taken from the patient's breast. The result of analysis on this sample by Cyto-pathologist is crucial for breast cancer patient....
Selective ensemble learning is a learning algorithm, trains a number of based classifier and selects some of them to ensemble. Through the selective ensemble, the algorithm would be more effective than each single one and better than the algorithm that select all the based classifier, and the algorithm would have effective generalization ability. In this paper, we apply a multi-lever selective ensemble...
Partial AUC (pAUC) represents the area with a restricted range of specificity (e.g. low false positive rate). It may identify important regional differentiated genes missed by full-range analysis. Unlike the popular t-test, which is based on the mean difference and the standard deviation between the disease and health groups, pAUC based test statistic relies on the rank of a gene in different samples...
A novel super resolution method for enhancing the resolution of mammogram images based on statistical moment analysis (SMA) has been designed and implemented. The proposed SMA method enables high resolution mammogram images to be produced at lower levels of radiation exposure to the patient. The SMA method takes advantage of the statistical characteristics of the underlying breast tissues being imaged...
We present a study of the spatial variation of nuclear morphology of stromal and cancer-associated fibroblasts in the mouse mammary gland. The work is part of a framework being developed for the analysis of the tumor microenvironment in breast cancer. Recent research has uncovered the role of stromal cells in promoting tumor growth and progression. In specific, studies have indicated that stromal...
Breast density is considered a structural property of a mammogram that can change in various ways explaining different effects of medicinal treatments. The aim of the present work is to provide a framework for obtaining more accurate and sensitive measurements of breast density changes related to specific effects like Hormonal Replacement Therapy (HRT) and aging. Given effect-grouped patient data,...
In this paper we propose an application of local statistical models to the problem of identifying patients with pathologic complete response (PCR) to neoadjuvant chemotherapy. The idea of using local models is to split the input space (with data from PCR and NoPCR patients) and build a model for each partition. After the construction of the models we used bayesian classifiers and logistic regression...
The objective of study is to develop intelligent decision support system to aid radiologist in diagnosis using pattern recognition techniques to estimate diagnostic function. In this study 3 approaches investigated namely statistical, neural networks and optimization techniques which were applied on the Wisconsin dataset. Trained neural networks, with the data set used as input, improve on the independent...
With the development of modern science, the goal of medical research is not limit to explore a type of disease but more accurate multi-subtypes of this disease. For example breast cancer can be divided into three different subtypes: BRCA1, BRCA2 and Sporadic. Previous work only focuses on distinguishing several pairs of tumors. However, the simultaneous distinguish across multiple disease types has...
An explosion of new research is vastly changing the medical sciences understanding of the cancer biology and giving new clues about how to attack it. This research tries to use a new impact analysis approach to investigating how the age, year and sex affect the probability of getting breast cancer. The impact analysis is an approach that is used to investigate the association of the impact factor...
An increasing number of studies have profiled gene expressions in tumor specimens using distinct microarray platforms and analysis techniques. With the accumulating amount of microarray data, one of the most challenging tasks is to develop robust statistical models to integrate their findings. We compare some recent methodologies on the field, with respect to ER status, and focus on a unified among...
Gene expression patterns that can distinguish to a clinically significant degree disease subclasses not only play a prominent role in diagnosis but also lead to therapeutic strategies that tailor treatment to the particular biology of each disease. Nevertheless, gene expression signatures derived through statistical feature identification procedures on population datasets have received rightful criticism,...
An Univariate Analysis Of Variance (ANOVA) Discriminant Analysis (DA) classifier is proposed for classifying the masses present in mammogram. This approach combines the 19 shape properties of the mass regions and classifies the masses as benign or malignant using Univariate ANOVA. The experiment is performed on DDSM database images. Experimental results shows that the proposed method reaches high...
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