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In recent years, with the development of microarray technique, discovery of useful knowledge from microarray data has become very important. Biclustering is a very useful data mining technique for discovering genes which have similar behavior. In microarray data, several objectives have to be optimized simultaneously and often these objectives are in conflict with each other. A multi objective model...
One of the most characteristic features of biological molecular networks is that the network structure itself changes, depending on the cellular environment. Indeed, activated molecules show a variety of responses to distinctive cell conditions, and subsequently the network structures of active molecules also change. Here we present an approach to trace the network structure changes by using the graphical...
This paper proposes a new type of regularization in the context of multi-class support vector machine for simultaneous classification and gene selection. By combining the huberized hinge loss function and the elastic net penalty, the proposed support vector machine can do automatic gene selection and further encourage a grouping effect in the process of building classifiers, thus leading a sparse...
Analysis of gene expression data includes classification of the data into groups and subgroups based on similar expression patterns. Standard clustering methods for the analysis of gene expression data only identifies the global models while missing the local expression patterns. In order to identify the missed patterns biclustering approach has been introduced. Various biclustering algorithms have...
A common problem in biology is to partition a set of experimental data into clusters in such a way that the data points within the same cluster are highly similar while data points in different clusters are very different. In this direction, clustering microarray time-series data via pairwise alignment of piece-wise linear profiles has been recently introduced. We propose a EM clustering approach...
The combined analysis of the microarray and drug-activity datasets has the potential of revealing valuable knowledge about various relations among gene expressions and drug activity patterns in tumor cells. However, the huge amount of biological data needs appropriate data mining models in order to extract interesting patterns and useful information. In this paper, the NCI60 dataset has been analyzed...
In this paper, we propose a new method for tumor classification using gene expression data. The new method expresses each testing sample as a linear combination of a set of metasamples extracted from all the training samples. Classification is achieved by a defined discriminating functions using the coefficient vector for the metasamples extracted from each category, which is obtained by l1-regularized...
In this paper, we propose to apply non-uniform weighs to the genes in the pathway based analysis and present two weighting schemes for the genes. Specifically, we incorporate our weighting schemes into the global test pathway based analysis approach and investigate the effects of our weighting schemes. We observe that when non-uniform weights are applied, some originally lower ranked pathways are...
We propose a general method for matrix factorization based on decomposition by parts. It can reduce the dimension of expression data from thousands of genes to several factors. Unlike classification and regression, matrix decomposition requires no response variable and thus falls into category of unsupervised learning methods. We demonstrate the effectiveness of this approach to the supervised classification...
Association rules mining methods have been recently applied to gene expression data analysis to reveal relationships between genes and different conditions and features. However, not much effort has focused on detecting the relation between gene expression maps and related gene functions. Here we describe such an approach to mine association rules among gene functions in clusters of similar gene expression...
Mutual information algorithms have been used for the identification of gene-gene interactions in gene expression data. These methods have been hindered by a high false-positive rate. We explored the use of free-text abstracts as an additional source of information for assessing the biological relevance of predicted gene interactions. Our results suggest that the performance of a mutual information...
The investigation of potential microarray markers, which in turn will speed up the molecular analysis and provide reliable results on the benefit of patient care is of significant importance. Feature selection techniques, which aim at minimizing the dimensionality of the microarray data by keeping the most significant genes according to their expression values is a necessary component towards this...
The proper application of statistics, machine learning, and data-mining techniques in routine clinical diagnostics to classify diseases using their genetic expression profile is still a challenge. One critical issue is the overall inability of most state-of-the-art classifiers to identify out-of-class samples, i.e., samples that do not belong to any of the available classes. This paper shows a possible...
Feature selection has become the cornerstone of many classification problems. It has been applied in many domains such as Web mining, text categorization, gene expression microarray analysis, image analysis, and combinatorial chemistry. One type of well-studied feature selection methodology is filtering, which is typically divided into ranking and subset evaluation. This work provides an empirical...
This paper motivates the use of qualitative probabilistic networks (QPNs) in conjunction with or in lieu of Bayesian Networks (BNs) for reconstructing gene regulatory networks from microarray expression data. QPNs are qualitative abstractions of Bayesian Networks that replace the conditional probability tables associated with BNs by qualitative influences, which use signs to encode how the values...
Drugs and biological experiments are designed to affect a particular target gene or pathway. However, they might inadvertently activate other pathways and cause side effects. Because of the existence of complex cellular mechanisms responding to stimuli, it is difficult to detect the presence of such side effects. Therefore, identification of pathways that function together under identical conditions...
One of the most demanding problems in mining temporal data is to identify how multivariate change associations might be discovered and used to better understand data interactions and dependencies. This paper introduces a framework to mine associations among significant changes in multivariate time-series data. Building on statistical methods, we detect significant changes in time-series data and use...
In this paper a hybrid metaheuristic for biclustering based on Scatter Search and Genetic Algorithms is presented. A general scheme of Scatter Search has been used to obtain high-quality biclusters, but a way of generating the initial population and a method of combination based on Genetic Algorithms have been chosen. Moreover, in the own algorithm the overlapping among biclusters is controlled adding...
Various classification methods have been used to predict the class of tissue samples based on gene expression data. prediction analysis for microarrays (PAM) is one of the top classifiers that has been extensively used for cancer classification. In this paper a novel method of combining expression data from gene pairs is used to improve the overall accuracy of PAM. Recent studies suggest that deregulation...
In this paper, we propose image enhancement of microarray images using histogram specification method. The proposed approach consists of system model that discuss about finding the type of noise present in the image and enhancing image by removing the noise present in the image. The proposed method is very efficient as it enhances image by revealing most of the microarray spots which is used for subsequent...
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