Gene expression microarray classification using PCA–BEL

Gene Expression-Based Cancer Classification for Handling the Class Imbalance Problem and Curse of Dimensionality

Cancer is a leading cause of death globally. The majority of cancer cases are only diagnosed in the late stages of cancer due to the use of conventional methods. This reduces the chance of survival for cancer patients. Therefore, early detection consequently followed by early diagnoses are important tasks in cancer research. Gene expression microarray technology has been applied to detect and diagnose most types of cancers in their early stages and has gained encouraging results. In this paper, we address the problem of classifying cancer based on gene expression for handling the class imbalance problem and the curse of dimensionality. The oversampling technique is utilized to overcome this problem by adding synthetic samples. Another common issue related to the gene expression dataset addressed in this paper is the curse of dimensionality. This problem is addressed by applying chi-square and information gain feature selection techniques. After applying these techniques individually, we proposed a method to select the most significant genes by combining those two techniques (CHiS and IG). We investigated the effect of these techniques individually and in combination. Four benchmarking biomedical datasets (Leukemia-subtypes, Leukemia-ALLAML, Colon, and CuMiDa) were used. The experimental results reveal that the oversampling techniques improve the results in most cases. Additionally, the performance of the proposed feature selection technique outperforms individual techniques in nearly all cases. In addition, this study provides an empirical study for evaluating several oversampling techniques along with ensemble-based learning. The experimental results also reveal that SVM-SMOTE, along with the random forests classifier, achieved the highest results, with a reporting accuracy of 100%. The obtained results surpass the findings in the existing literature as well.

Utility of Banff Human Organ Transplant Gene Panel in Human Kidney Transplant Biopsies

BACKGROUND

Microarray transcript analysis of human renal transplantation biopsies has successfully identified the many patterns of graft rejection. To evaluate an alternative, this report tests whether gene expression from the Banff Human Organ Transplant (B-HOT) probe set panel, derived from validated microarrays, can identify the relevant allograft diagnoses directly from archival human renal transplant formalin-fixed paraffin-embedded biopsies. To test this hypothesis, principal components (PCs) of gene expressions were used to identify allograft diagnoses, to classify diagnoses, and to determine whether the PC data were rich enough to identify diagnostic subtypes by clustering, which are all needed if the B-HOT panel can substitute for microarrays.

METHODS

RNA was isolated from routine, archival formalin-fixed paraffin-embedded tissue renal biopsy cores with both rejection and nonrejection diagnoses. The B-HOT panel expression of 770 genes was analyzed by PCs, which were then tested to determine their ability to identify diagnoses.

RESULTS

PCs of microarray gene sets identified the Banff categories of renal allograft diagnoses, modeled well the aggregate diagnoses, showing a similar correspondence with the pathologic diagnoses as microarrays. Clustering of the PCs identified diagnostic subtypes including non-chronic antibody-mediated rejection with high endothelial expression. PCs of cell types and pathways identified new mechanistic patterns including differential expression of B and plasma cells.

CONCLUSIONS

Using PCs of gene expression from the B-Hot panel confirms the utility of the B-HOT panel to identify allograft diagnoses and is similar to microarrays. The B-HOT panel will accelerate and expand transcript analysis and will be useful for longitudinal and outcome studies.

GSEnet: feature extraction of gene expression data and its application to Leukemia classification

Gene expression data is highly dimensional. As disease-related genes account for only a tiny fraction, a deep learning model, namely GSEnet, is proposed to extract instructive features from gene expression data. This model consists of three modules, namely the pre-conv module, the SE-Resnet module, and the SE-conv module. Effectiveness of the proposed model on the performance improvement of 9 representative classifiers is evaluated. Seven evaluation metrics are used for this assessment on the GSE99095 dataset. Robustness and advantages of the proposed model compared with representative feature selection methods are also discussed. Results show superiority of the proposed model on the improvement of the classification precision and accuracy.

The Higher-Order of Adaptive Lasso and Elastic Net Methods for Classification on High Dimensional Data

The lasso and elastic net methods are the popular technique for parameter estimation and variable selection. Moreover, the adaptive lasso and elastic net methods use the adaptive weights on the penalty function based on the lasso and elastic net estimates. The adaptive weight is related to the power order of the estimator. Normally, these methods focus to estimate parameters in terms of linear regression models that are based on the dependent variable and independent variable as a continuous scale. In this paper, we compare the lasso and elastic net methods and the higher-order of the adaptive lasso and adaptive elastic net methods for classification on high dimensional data. The classification is used to classify the categorical data for dependent variable dependent on the independent variables, which is called the logistic regression model. The categorical data are considered a binary variable, and the independent variables are used as the continuous variable. The high dimensional data are represented when the number of independent variables is higher than the sample sizes. For this research, the simulation of the logistic regression is considered as the binary dependent variable and 20, 30, 40, and 50 as the independent variables when the sample sizes are less than the number of the independent variables. The independent variables are generated from normal distribution on several variances, and the dependent variables are obtained from the probability of logit function and transforming it to predict the binary data. For application in real data, we express the classification of the type of leukemia as the dependent variables and the subset of gene expression as the independent variables. The criterion of these methods is to compare by the average percentage of predicted accuracy value. The results are found that the higher-order of adaptive lasso method is satisfied with large dispersion, but the higher-order of adaptive elastic net method outperforms on small dispersion.

Predicting cancer using supervised machine learning: Mesothelioma

BACKGROUND

Pleural Mesothelioma (PM) is an unusual, belligerent tumor that rapidly develops into cancer in the pleura of the lungs. Pleural Mesothelioma is a common type of Mesothelioma that accounts for about 75% of all Mesothelioma diagnosed yearly in the U.S. Diagnosis of Mesothelioma takes several months and is expensive. Given the risk and constraints associated with PM diagnosis, early identification of this ailment is essential for patient health.

OBJECTIVE

In this study, we use artificial intelligence algorithms recommending the best fit model for early diagnosis and prognosis of Malignant Pleural Mesothelioma (MPM).

METHODS

We retrospectively retrieved patients' clinical data collected by Dicle University, Turkey and applied multilayered perceptron (MLP), voted perceptron (VP), Clojure classifier (CC), kernel logistic regression (KLR), stochastic gradient decent (SGD), adaptive boosting (AdaBoost), Hoeffding tree (VFDT), and primal estimated sub-gradient solver for support vector machine (s-Pegasos). We evaluated the models, compared and tested them using paired t-test (corrected) at 0.05 significance based on their respective classification accuracy, f-measure, precision, recall, root mean squared error, receivers' characteristic curve (ROC), and precision-recall curve (PRC).

RESULTS

In phase 1, SGD, AdaBoost.M1, KLR, MLP, VFDT generate optimal results with the highest possible performance measures. In phase 2, AdaBoost, with a classification accuracy of 71.29%, outperformed all other algorithms. C-reactive protein, platelet count, duration of symptoms, gender, and pleural protein were found to be the most relevant predictors that can prognosticate Mesothelioma.

CONCLUSION

This study confirms that data obtained from biopsy and imaging tests are strong predictors of Mesothelioma but are associated with a high cost; however, they can identify Mesothelioma with optimal accuracy.

Low dimensional representations along intrinsic reaction coordinates and molecular dynamics trajectories using interatomic distance matrices

Most chemical transformations (reactions or conformational changes) that are of interest to researchers have many degrees of freedom, usually too many to visualize without reducing the dimensionality of the system to include only the most important atomic motions. In this article, we describe a method of using Principal Component Analysis (PCA) for analyzing a series of molecular geometries (e.g., a reaction pathway or molecular dynamics trajectory) and determining the reduced dimensional space that captures the most structural variance in the fewest dimensions. The software written to carry out this method is called PathReducer, which permits (1) visualizing the geometries in a reduced dimensional space, (2) determining the axes that make up the reduced dimensional space, and (3) projecting the series of geometries into the low-dimensional space for visualization. We investigated two options to represent molecular structures within PathReducer: aligned Cartesian coordinates and matrices of interatomic distances. We found that interatomic distance matrices better captured non-linear motions in a smaller number of dimensions. To demonstrate the utility of PathReducer, we have carried out a number of applications where we have projected molecular dynamics trajectories into a reduced dimensional space defined by an intrinsic reaction coordinate. The visualizations provided by this analysis show that dynamic paths can differ greatly from the minimum energy pathway on a potential energy surface. Viewing intrinsic reaction coordinates and trajectories in this way provides a quick way to gather qualitative information about the pathways trajectories take relative to a minimum energy path. Given that the outputs from PCA are linear combinations of the input molecular structure coordinates (i.e., Cartesian coordinates or interatomic distances), they can be easily transferred to other types of calculations that require the definition of a reduced dimensional space (e.g., biased molecular dynamics simulations).

Two-Stage Hybrid Gene Selection Using Mutual Information and Genetic Algorithm for Cancer Data Classification

Cancer is a deadly disease which requires a very complex and costly treatment. Microarray data classification plays an important role in cancer treatment. An efficient gene selection technique to select the more promising genes is necessary for cancer classification. Here, we propose a Two-stage MI-GA Gene Selection algorithm for selecting informative genes in cancer data classification. In the first stage, Mutual Information based gene selection is applied which selects only the genes that have high information related to the cancer. The genes which have high mutual information value are given as input to the second stage. The Genetic Algorithm based gene selection is applied in the second stage to identify and select the optimal set of genes required for accurate classification. For classification, Support Vector Machine (SVM) is used. The proposed MI-GA gene selection approach is applied to Colon, Lung and Ovarian cancer datasets and the results show that the proposed gene selection approach results in higher classification accuracy compared to the existing methods.

Gene expression cancer classification using modified K-Nearest Neighbors technique

Gene expression microarray classification is a crucial research field as it has been employed in cancer prediction and diagnosis systems. Gene expression data are composed of dozens of samples characterized by thousands of genes. Hence, an accurate and effective classification of such samples is a challenge. Machine learning techniques have been broadly utilized to build substantial and precise classification models. This paper proposes a new classification technique for gene expression data, which is called Modified k-nearest neighbor (MKNN). MKNN is applied in two scenarios namely; smallest modified KNN (SMKNN) and largest modified KNN (LMKNN). Both implementations are undertaken to enhance the performance of KNN. The key idea is to employ robust neighbors from training data by using a new weighting strategy. Several experiments have been performed on six different gene expression datasets. Experiments have shown that MKNN in its both scenarios outperforms traditional as well as recent ones. MKNN has been compared against (i) KNN, (ii) weighted KNN, (iii) support vector machine (SVM), (iv) fuzzy support vector machine, (v) brain emotional learning (BEL) in terms of classification accuracy, precision, and recall. On the other hand, results show that MKNN introduces smaller testing time than both KNN and weighted KNN.

Gene Selection in Cancer Classification Using Sparse Logistic Regression with L1/2 Regularization

In recent years, gene selection for cancer classification based on the expression of a small number of gene biomarkers has been the subject of much research in genetics and molecular biology. The successful identification of gene biomarkers will help in the classification of different types of cancer and improve the prediction accuracy. Recently, regularized logistic regression using the L 1 regularization has been successfully applied in high-dimensional cancer classification to tackle both the estimation of gene coefficients and the simultaneous performance of gene selection. However, the L 1 has a biased gene selection and dose not have the oracle property. To address these problems, we investigate L 1 / 2 regularized logistic regression for gene selection in cancer classification. Experimental results on three DNA microarray datasets demonstrate that our proposed method outperforms other commonly used sparse methods ( L 1 and L E N ) in terms of classification performance.

The biological knowledge discovery by PCCF measure and PCA-F projection

In the process of biological knowledge discovery, PCA is commonly used to complement the clustering analysis, but PCA typically gives the poor visualizations for most gene expression data sets. Here, we propose a PCCF measure, and use PCA-F to display clusters of PCCF, where PCCF and PCA-F are modeled from the modified cumulative probabilities of genes. From the analysis of simulated and experimental data sets, we demonstrate that PCCF is more appropriate and reliable for analyzing gene expression data compared to other commonly used distances or similarity measures, and PCA-F is a good visualization technique for identifying clusters of PCCF, where we aim at such data sets that the expression values of genes are collected at different time points.

Detecting Susceptibility to Breast Cancer with SNP-SNP Interaction Using BPSOHS and Emotional Neural Networks

Studies for the association between diseases and informative single nucleotide polymorphisms (SNPs) have received great attention. However, most of them just use the whole set of useful SNPs and fail to consider the SNP-SNP interactions, while these interactions have already been proven in biology experiments. In this paper, we use a binary particle swarm optimization with hierarchical structure (BPSOHS) algorithm to improve the effective of PSO for the identification of the SNP-SNP interactions. Furthermore, in order to use these SNP interactions in the susceptibility analysis, we propose an emotional neural network (ENN) to treat SNP interactions as emotional tendency. Different from the normal architecture, just as the emotional brain, this architecture provides a specific path to treat the emotional value, by which the SNP interactions can be considered more quickly and directly. The ENN helps us use the prior knowledge about the SNP interactions and other influence factors together. Finally, the experimental results prove that the proposed BPSOHS_ENN algorithm can detect the informative SNP-SNP interaction and predict the breast cancer risk with a much higher accuracy than existing methods.