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Breast cancer is the most frequent cancer and the most frequent cause of cancer induced death in women in the world. Diagnosis and prognosis of this cancer can be done through the radiological, surgical, and pathologic assessments of breast tissue samples. In developing countries, testing for detection of this cancer involves visual microscopic test of cytology samples such as Fine Needle Aspiration...
Classical validity indices are limited to clusters of specific geometrical shapes, and do not allow for discovering the natural cluster structure in data. Moving from the traditional coherent gene expression clustering to exploring the connectivity of gene expression patterns demands the use of more efficient validity indices. In this work, the application of a novel validity measure to gene expression...
This paper aims to assess the effectiveness of three different clustering algorithms, used to detect breast cancer recurrent events. The performance of a classical k-means algorithm is compared with a much more sophisticated Self-Organizing Map (SOM-Kohonen network) and a cluster network, closely related to both k-means and SOM. The three clustering algorithms have been applied on a concrete breast...
Mining techniques are needed to extract important information from huge high dimensional gene expression sets. Targeting unique expression behavior as over/under-expression is specific to gene expression data and is needed to explore another direction in the relation of genes to tumor conditions. This research proposes criteria for filtering over-expression genes, identifying over-expression related...
Microcalcifications clusters are subtle signs of breast cancer and are very difficult to diagnose in the mammographic images because of their small size and low contrast with respect to the normal breast tissue. In this paper, we propose an automatic procedure for the identification of microcalcifications clusters to aid the radiologist in the detection of this kind of breast cancer signs.
Different genes have different biological functions, these functions interactions between genes, moreover they play an important role in gene regulatory networks. In the recent cancer research field, a definite conclusion for the regulatory mechanisms of tumor genesis and metastasis has yet been found. Therefore we will apply oncogenes and suppressor genes from ovarian carcinoma micro array data analysis,...
We introduce a novel multi-relational learning algorithm based on simultaneous nonnegative matrix factorizations, able to distinguish between “target” and “background” relations, deal with incomplete data and so-called “link” functions. The ability to handle incomplete data allows us to tackle both relation prediction and clustering. Moreover, the nonnegativity constraints are essential for the interpretability...
Clustered micro calcifications (MCs) are one of the early signs of breast cancer. In this paper, we propose a new computer aided diagnosis (CAD) system for automatic detection of MCs in two steps. First, pixels corresponding to potential micro calcifications are found using a multilayer feed-forward neural network. The input of this network consists of 4 wavelet and 2 gray-level features. The output...
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 an important deleterious disease. Mortality rate from this cancer is effectively high and rapidly increasing. The detection at the earlier state can help to reduce the mortality rate. In this paper, we apply the interval type-2 fuzzy system with automatic membership function generation using the Possibilistic C-Means (PCM) clustering algorithm. We utilize four features, i.e., B-descriptor,...
This paper details a methodology and preliminary results for applying a hierarchy of clustering units to mammographic image data. The identification of patients with breast cancer through the detection of microcalcifications and masses is a demanding classification problem; minimal false negatives are desired while simultaneously avoiding false positives that cause unnecessary cost to patients and...
In this paper, we present a graph-based multi-resolution approach for mitosis extraction in breast cancer histological whole slide images. The proposed segmentation uses a multi-resolution approach which reproduces the slide examination done by a pathologist. Each resolution level is analyzed with a focus of attention resulting from a coarser resolution level analysis. At each resolution level, a...
Breast cancer is one of the leading causes to women mortality in the world and early detection is an important means to reduce the mortality rate. The presence of microcalcifications clusters has been considered as a very important indicator of malignant types of breast cancer and its detection is important to prevent and treat the disease. This paper presents an alternative and effective approach...
Data mining techniques are used for the knowledge discovery process under the large data set environment. Clustering techniques are used to group up the relevant data sets. Hierarchical and partitioned clustering techniques are used for the clustering process. The clustering process is the complex task with high process time. The pattern extraction scheme is applied to find frequent item sets. Association...
Microarray datasets comprise a large number of gene expression values and a relatively small number of samples. Feature selection algorithms are very useful in these situations in order to find a compact subset of informative features. We propose a redundancy control method for algorithms in the recently proposed SPEC family of spectral-based feature selection algorithms. This method is applied to...
Inferring tumor subtypes based on the gene expression data alone does not appear to be as powerful as expected for the lack of robustness and clinical meaning. The ultimate aim of clustering tumor samples should be to support clinical evaluation or treatment. Therefore, clustering procedure should closely integrate the clinical outcome and/or treatment information for final representation of the tumor...
In this paper, we investigated the use of gene coexpression network analyses to identify potential biomarkers for breast carcinoma prognosis. The network mining algorithm CODENSE is used to identify highly connected genome-wide gene co-expression networks among a variety of cancer types, and the resulted gene clusters are applied to a series of breast cancer microarray sets to categorize the patients...
Clustered microcalcifications (MCs) in mammograms can be an important early sign of breast cancer in women. Their accurate detection is an important problem in computer aided detection. To improve the performance of detection, we propose a bagging-based twin support vector machine (B-TWSVM) to detect MCs. The ground truth of MCs in mammograms is assumed to be known as a priori. First each MCs is preprocessed...
A panel of cell lines of diverse molecular background offers an improved model system for high-content screening, comparative analysis, and cell systems biology. A computational pipeline has been developed to collect images from cell-based assays, segment individual cells and colonies, represent segmented objects in a multidimensional space, and cluster them for identifying distinct subpopulations...
This paper presents a novel hybrid data mining ensemble approach which is an effective combination of various clustering methods, in order to utilize the strengths of individual technique and compensate for each otherpsilas weaknesses. The proposed approach is formulated to cluster extracted features into dasiasoftpsila clusters using unsupervised learning strategies and fuse the cluster decisions...
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