CogNet: classification of gene expression data based on ranked active-subnetwork-oriented KEGG pathway enrichment analysis

The purpose of this paper is to introduce transductive inference with support vector machines (TSVM) as a powerful methodology for classification of gene expression data, using training and prediction data sets. The following classification problems will be considered: determination of cancer diagnosis categories and classification of genes from the budding yeast Saccharomyces cerevisiae in functional groups. In the case of training samples, experts have already classified the samples in their respective classes. So, given each prediction sample, the purpose is to determine its corresponding class. The main aspect of TSVM is that the classification task will be implemented in just one step, improving the generalization capability of the classifier. The TSVM will be compared with the traditional inductive method (SVM) in a series of experiments concerning the two classification problems, with promising results.

Chapter 14 - Review on hybrid feature selection and classification of microarray gene expression data

In this review, we discuss the main problems and state‐of‐the‐art solutions applied to the field of gene expression. Specific data analysis workflows have been developed in parallel with the technology and currently cover a very wide spectrum of methods and applications needed to give answers to a lot of scientific questions that this type of data are producing. Computer science and, more specifically, the data mining area is still benefiting from a large set of real‐case scenarios to apply and develop new ideas and tools for discovering biological knowledge and new information from this experimental data. In this article, we make the reader aware of the main problems that still persist and provide a description of the methodologies that are applied for classification, clustering, and functional exploration of gene expression data. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 381–396 DOI: 10.1002/widm.29This article is categorized under: Algorithmic Development > Biological Data Mining Application Areas > Science and Technology Technologies > Classification

Gene expression (GE) profiles expansively revised to disclose intuition into the multifariousness of cancer furthermore to discover concealed information which provides biological knowledge for the classification of cancer. Precise cancer classification straightly through original GE profiles stays challenging on account of the intrinsic high-dimension feature along with the small magnitude of the data samples. Therefore, choosing high discriminative genes as of the GE data have turn into progressively fascinating in the bioinformatics field. This given paper gives a technique for the GE data classification utilising entropy-based graph classifier. Initially, the proposed technique evaluate the GE data's signal to noise ratio (SNR) values, additionally, selects the relevant features using krill herd (KH) optimization process. The truth is that not all features are helpful for classification, and some redundant together with the irrelevant features might even serve as outlier. To dispose the outliers, feature reduction is done with the assist of Euclidean distance. Classification is made utilising entropy-based graph classifier. The proposed process' effectiveness contrasted with the existing method concerning classifications is established from the experimental outcome.

Analysis of large-scale gene expression data is a research hotspot in the field of bioinformatics, which can be used to study abnormal phenomenon in plant growth process. This paper proposes a biological knowledge integration method based on parallel clustering to select gene subsets effectively. Gene ontology is utilised to obtain the biological functional similarity, and combined with gene expression data. Parallelised affinity propagation algorithm is used to cluster data since it can not only obtain more biologically meaningful subsets, but also avoid the loss of some potential value in genes from simple gene primary selection. The algorithm is verified with four typical plant datasets and compared with other well-known integration methods. Experimental results on plant stress response datasets demonstrate that the proposed method can select genes with stronger classification ability.

Despite the considerable efforts that are done for Microarray gene expression profile, biomarker discovery or gene selection process remains a complex challenge for biologists and computer scientists. This article presented an Artificial Bee Colony (ABC) based approach for accurate classification of cancer microarray data using support vector machine (SVM) as a classifier. The approach is able to deal with the high dimensionality of the microarray data. According to literature for microarray data analysis, up to our knowledge, this is the first attempt to apply Artificial Bee Colony based algorithm as gene selection method for cancer classification problem using Microarray gene expression profile. Comparing with other algorithms that are proposed in the literature, we can conclude that our proposed algorithm (ABC-SVM) obtains promising results for gene selection and cancer classification problems.

In recent years, many studies have shown that microarray gene expression data is useful for disease identification and cancer classification. However, since gene expression data may contain thousands of genes simultaneously, successful microarray classification can be rather difficult. Feature (gene) selection is a frequently used pre-processing technology for successful classification of microarray gene expression data. Selecting a useful gene subset as a classifier not only decreases the computational time and cost, but also increases the classification accuracy. It is therefore imperative to extract only a small number of genes, which are exclusively relevant for the classification of a particular cancer/disease type. In this paper, correlation-based binary particle swarm optimizations is proposed to select the relevant genes, and a K-nearest neighbor with the leave-one-out cross-validation method serves as a classifier to evaluate the classification performance on six published cancer classification data sets. The experimental results show that the proposed method selects fewer gene subsets, while still resulting in higher prediction accuracy than the other literature methods.

Classification of data from electronic nose using relevance vector machines

Though DNA microarray technology simultaneously measures the expression levels of thousands of genes, only a few underlying gene features may account for significant data variation in gene classification problems. Selection of features from huge data set is difficult and so dimension reduction of gene expression data set is essential in order to determining important features, which play key role in predicting an outcome. Rough set theory (RST) has been used recently for dimension reduction of data, however, the existing methods are inadequate to finding minimal reduct. The paper proposes a RST based technique, applied on gene expression data for dimension reduction by obtaining single reduct in one pass. The gene expression data are discretized using linguistic terms with proper semantics and represented by fuzzy sets. The discretized values are calculated using Gaussian membership function with varied mean and standard deviation in order to eliminate the ambiguity between different linguistic terms. The genes are classified using linguistic decision attribute values based on the frequency of gene expression data. Discritization and classification of gene expression data are performed simultaneously, which significantly reduces time complexity of the procedure. Thus, the proposed framework selects the most significant samples for gene classification, resulting dimension reduction. The Proposed method produces output, which exhibits no variation with experimental microarray gene information unlike other existing methods.

Machine Learning algorithms have been widely used for gene expression data classification, despite the fact that these data have often intrinsic limitations, such as high dimensionality and a small number of examples. Few studies try to characterize to which extent these aspects can influence the performance of the classification models induced. In this paper we compute different measures characterizing the complexity of gene expression data sets for cancer diagnosis. We then investigate how these measures relate to the classification performances achieved by support vector machines, a popular Machine Learning technique usually employed in the analysis of gene expression data. The results obtained indicate that some of the complexity indices utilized are indeed successful in explaining the difficulty involved in the classification of cancer gene expression data.

Microarray technology has transformed the biotechnological research to next level in the recent years. It provides the expression levels of various genes involved in a particular disease. Prostate cancer disease turned into life threatening cancer. The genes causing this disease are identified through the classification methods. These gene expression data have problems like high dimensional with low sample size which imposes active challenges in the existing classification algorithms. Feature selection techniques are applied in order to address the dimensionality issues. . This paper aims in analyzing the feature selection methods for classification of gene expression data of Prostate and identify the significant genes that have a major influence on the disease. The three different feature selection methods such as Filters, wrappers and embedded selectors are applied before the classification process for selecting the top ranked genes. Then, the extracted top ranked genes are applied on the classification algorithms such as SVM, k-NN, Random Forest and Artificial Neural Network. After the inclusion of feature selection technique, the classification accuracy is significantly boosted even with less number of genes. Random Forest classification algorithm outperforms other classification methods. The significant genes that has the major influence in prostate cancer disease are identified such as KLK3, GFI1, CXCR2 and TNFRSF10C.

Objective: Thalassemia is a hereditary hemoglobinopathy and remains a significant public health problem, particularly in Mediterranean regions. Although genetic testing represents the gold standard for subtype classification, access to such testing is limited in many clinical settings. This pilot study aimed to explore the feasibility of using machine learning models based on routinely available clinical and laboratory parameters to support the differentiation of thalassemia subtypes in the absence of genetic testing.Methods: This retrospective cross-sectional study included 83 individuals (57 thalassemia major, 11 thalassemia intermedia, and 15 healthy controls). Demographic, clinical, and laboratory variables were analyzed using the R programming language. A supervised Random Forest algorithm was applied for multiclass classification. Model performance was assessed using accuracy, class-specific sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). To further evaluate the distinction between thalassemia major and intermedia, a simplified logistic regression model was constructed, and Firth logistic regression was applied to address the small sample size and class imbalance.Results: The Random Forest model demonstrated an overall test-set accuracy of 85.7%. Sensitivity was 80% for thalassemia major and 100% for both thalassemia intermedia and healthy controls. Variable importance analysis identified red cell distribution width (RDW), hematocrit, ferritin, and hemoglobin as the most influential predictors. In the simplified logistic regression model distinguishing thalassemia major from intermedia, RDW was the only variable reaching statistical significance (p = 0.0476). Model performance metrics, including high AUC values, should be interpreted cautiously given the limited sample size.Conclusion: The Random Forest model demonstrated an overall test-set accuracy of 85.7%. Sensitivity was 80% for thalassemia major and 100% for both thalassemia intermedia and healthy controls. Variable importance analysis identified red cell distribution width (RDW), hematocrit, ferritin, and hemoglobin as the most influential predictors. In the simplified logistic regression model distinguishing thalassemia major from intermedia, RDW was the only variable reaching statistical significance (p = 0.0476). Model performance metrics, including high AUC values, should be interpreted cautiously given the limited sample size.

Data mining techniques have been used by several researchers to detect illnesses. Some methods are intended to predict a single sickness, while others are intended to predict a wide variety of diseases. It is also possible to improve the accuracy of sickness prediction. In this post, we provided an overview of data classification approaches that are available. These algorithms are mostly represented by themselves. The classification of data is a common and computationally difficult procedure. A massive amount of information must be analyzed in order to come up with a sound strategy for dealing with disease. There are a number of common scenarios, including as early disease detection, severity assessment, and prediction. This will decrease the progression of the disease, improve the well-being of the patients, and reduce the associated costs of medical care. In this regard, machine learning techniques have been employed. This article presents a machine learning based framework for heart disease data classification and prediction.

Gender identification is one of the major problem speech analysis today. Tracing the gender from acoustic data i.e., pitch, median, frequency etc. Machine learning gives promising results for classification problem in all the research domains. There are several performance metrics to evaluate algorithms of an area. Our Comparative model algorithm for evaluating 5 different machine learning algorithms based on eight different metrics in gender classification from acoustic data. Agenda is to identify gender, with five different algorithms: Linear Discriminant Analysis (LDA), K-Nearest Neighbour (KNN), Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) on basis of eight different metrics. The main parameter in evaluating any algorithms is its performance. Misclassification rate must be less in classification problems, which says that the accuracy rate must be high. Location and gender of the person have become very crucial in economic markets in the form of AdSense. Here with this comparative model algorithm, we are trying to assess the different ML algorithms and find the best fit for gender classification of acoustic data.

The microarray data classification problem is a recent complex pattern recognition problem. The most important goal in supervised classification of microarray data, is to select a small number of relevant genes from the initial data in order to obtain high predictive classification accuracy. With the framework of a hybrid filter-wrapper, we study in this paper the role of the multi-parent recombination operator. For this purpose, we introduce a Random Multi Parent crossover (RMPX) and we analyze their effects in a genetic algorithm (GA) which is combined with Fisher's Linear Discriminant Analysis (LDA). This hybrid algorithm has the major characteristic that the GA uses not only a LDA classifier in its fitness function, but also LDA's discriminant coefficients to integrate a multi-parent specialized crossover and mutation operation to improve the performance of gene selection. In the experimental results it is observed that RPMX operator work very well by achieving lower classification error rates.