Feature selection by recursive binary gravitational search algorithm optimization for cancer classification

Zeroing in on the “holy grail”

Microarray databases are the most frequently used datasets for cancer analytics. Microarray databases are characterized by the presence of a very large number of genes, which exceeds the very little number of samples. So, the feature set accumulates the curse of dimensionality. Therefore, selecting a small subset of genes among thousands of genes in microarray data can potentially increase the accuracy for the classification of cancer. Many approaches, from the field of classical machine learning and soft computing, have been used to address the issue of feature selection and feature extraction for better classifications and clustering accuracy. The research outlined in this paper strives to look at a two-stage approach using minimum Redundancy Maximum Relevancy (mRMR), a feature ranking framework as the first stage followed by a hybrid genetic algorithm in the second stage that works on the features ranked by the mRMR. The proposed method is aimed to select the optimal feature subsets for better classification results in binary and multi class datasets to compensate for the curse of dimensionality in microarray datasets. The classifiers used to test the two-stage proposition are SVM, Naive-Bayes, Linear Discriminant Analysis, decision trees and random forest classifiers. The experimental results show that the gene subset selected by the mRMR-GA pipeline gives good results.

Expression profiling: Opportunities and pitfalls and impact on the study and management of allergic diseases

Cancer is a world-famous deadly disease. According to the World Health Organization (WHO), cancer is the second leading cause of death globally and is responsible for an estimated 9.6 million deaths in 2018. One well-known technique for cancer detection is the DNA microarray technique. DNA microarray technology provides an opportunity for researchers to analyze thousands of gene expression profiles at the same time to determine whether a person has cancer or not. However, one of the problems in DNA microarray data is the large number of features that require feature selection. In overcoming these problems, this study will use the feature selection Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and Chi-Square and use the Naive Bayes classification method. The accuracy results from using feature selection with those that are not will be compared. The accuracy between using the two feature selection methods will also be compared to find which feature selection method is better when combined with the Naive Bayes classification method. To get an overall picture of the performance comparison, this study also considers precision, recall, and F1-score. The best accuracy results obtained were 100% lung cancer data with SVM-RFE and Chi-Square, 99.6% ovarian cancer with SVM-RFE, 93.7% breast cancer with SVM-RFE, and 90% colon cancer with SVM- RFE.

Feature selection is a commonly addressed problem in classification. In gene expression-based cancer classification, a large number of genes in conjunction with a small number of samples makes the gene selection problem more important but also more challenging. Support vector machine as a popular classification algorithm, has been successfully used in SVM-RFE method for gene selection. This paper proposes a variant of SVM-RFE to do gene selection for cancer classification with expression data. Multiple support vector machine classifiers from a leave-one-out procedure are used to compute the feature ranking scores. The numerical experiments also show the good and stable performance of the proposed method.

DNA microarrays can simultaneously measure the expression level of thousands of gene within a particular mRNA sample that provide information about the state of cells and tissues. Though these expressive values are useful in cancer classification, and understand the mechanisms involved in the genesis of disease processes, however, only a few genes out of these thousands of genes contribute towards disease classification. On this basis, usage of feature selection algorithm is favourable, as the main goal of feature selection algorithm is to identify the relevant features (here genes) efficiently. In this paper, we have applied four filter Feature Selection (FS) methods, namely, Mutual Information (MI), Pearson Correlation Coefficient (PCC), Chi <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , ReliefF along with three well-known classifiers, namely, Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbour (KNN) on six microarray datasets (both binary and multi-class) namely, Leukemia, Lung, Lymphoma and Leukemia, Gastric, SRBCT and Childhood Tumor and recorded the accuracies.

Class prediction and feature selection is key in the context of diagnostic applications of DNA microarrays. Microarray data is noisy and typically composed of a low number of samples and a large number of genes. Perceptrons can constitute an efficient tool for accurate classification of microarray data. Nevertheless, the large input layers necessary for the direct application of perceptrons and the low samples available for the training process hamper its use. Two strategies can be taken for an optimal use of a perceptron with a favourable balance between samples for training and the size of the input layer: (a) reducing the dimensionality of the data set from thousands to no more than one hundred, highly informative average values, and using the weights of the perceptron for feature selection or (b) using a selection of only few genes that produce an optimal classification with the perceptron. In this case, feature selection is carried out first. Obviously, a combined approach is also possible. In this manuscript we explore and compare both alternatives. We study the informative contents of the data at different levels of compression with a very efficient clustering algorithm (Self Organizing Tree Algorithm). We show how a simple genetic algorithm selects a subset of gene expression values with 100% accuracy in the classification of samples with maximum efficiency. Finally, the importance of dimensionality reduction is discussed in light of its capacity for reducing noise and redundancies in microarray data.

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.

This paper presents a qualitative analysis of the Recursive Search Algorithm (RSA) technique. The RSA is a land cover change detection technique, which determines that a particular land cover is changed or not. It is a data mining approach and established on the segmentation technique. Furthermore, the RSA is a land cover change detection technique and identifies the land cover change by finding the sudden gains and drop-offs in a vegetation index time series data. The proficiency of the RSA technique is exhibited by using qualitative analysis. To exhibit the qualitative analysis, the RSA method is applied to the Naya Raipur Area data set, which is an enhanced vegetation index time series data set. It has been noticed that the significant land cover changes have been happening in the Naya Raipur Area.

Multimodal feature extraction and fusion for audio-visual speech recognition

A common situation in classification tasks is to deal with unbalanced datasets, an issue that appears when the majority class(es) has a large number of samples compared to the minority class(es). This problem is even more significant when the datasets have a large number of features but only a few samples, as is the case with microarray datasets. Traditionally, an approach to alleviate this problem has been the application of sampling methods to obtain more balanced classes, increasing the number of samples in the minority class (replicating samples or generating new synthetic samples), or decreasing the number of samples in the majority class. In this study, we have compared different balancing methods, including a novel method that applies sampling in both the minority and majority classes. The interest in applying feature selection in combination with balancing methods has also been explored. In view of the results, a recommendation of sampling method, feature selection, and classifier is proposed to improve the classification results according to the type of dataset.

Lessons from DNA microarray analysis: the gene expression profile of biofilms

The aim of the study was to develop a multiplex PCR-based DNA microarray technology for simultaneous detection and species identification of seven human herpes viruses, namely herpes simplex virus type 1, type 2 (HSV-1, HSV-2), varicella-zoster virus (VZV), Epstein-Barr virus (EBV), cytomegalovirus (CMV), and human herpes virus 6 (HHV-6A, HHV-6B), and to apply this technology to accurate diagnosis of herpesvirus-associated diseases. Primers and oligonucleotide probes were designed and synthesized based on the highly conserved regions of the DNA polymerase gene in human herpes viruses. DNA microarrays were made by printing the oligonucleotide probes onto special glass slides. After amplification and labeling with CY5, the PCR products were hybridized with the DNA microarrays and species identified. Sixty-one cerebrospinal fluid (CSF) and 132 blood specimens were analyzed by this technique, and the results were compared with those of TaqMan PCR. Several specimens were sequenced further after cloning. The PCR products of the seven human herpes viruses ranged from 224 to 252 bp, and could be species identified with DNA microarrays. The detection limits were 10(1) copies/microl for each virus. And the test showed no cross-reaction to DNA extracted from S. aureus, E. coli, hepatitis B virus, Cryptococcus neoformans, Candida albicans and human genome. Among 132 blood and 61 CSF specimens, 55 were tested positive for human herpes virus DNA. Compared with the results of TaqMan PCR, the sensitivity and specificity of the DNA microarray technology was 96.2% and 99.3%, respectively. This multiplex PCR-based DNA microarray technology, which is rapid, specific and sensitive, serves as an effective technique for simultaneous detection and species identification of seven human herpes viruses.

Cancer is one of the major causes of deaths worldwide. This disease is more ghastly as it doesn’t announce itself until it reaches in an advance stage. Still, mortality rate for cancer can be decreased if we diagnose & provide treatment at earliest. Though there are traditional clinical trials to predict a cancer there does not a single test which can correctly identify this disease. In the recent years DNA Microarray technology has been significantly used to analyze & predict the cancer. Analysis of gene expressions is not only interesting but also challenging as it is not only the concern of accuracy but also matter of life or death of a patient. DNA Microarray data is high dimensional, noisy & redundant, it makes task of classification more complicated as high computational cost is involved. Therefore feature selection & feature reduction becomes important task prior to classification. This paper presents comparative performance analysis of different dimensionality reduction techniques implemented on TCGA PANCANCER dataset.

Today, DNA microarray technology is the most widely employed functional genomics approach in the bioinformatics domain. Disclosing the patterns masked in gene expression data provides an opportunity for an enhanced comprehension of functional genomics. But, the large number of genes and the intricate biological networks has greatly increased the challenges of comprehending and interpreting the resulting mass of data, which often contains millions of measurements. So, clustering techniques are used to tackle this challenge. Several clustering techniques have been developed and used in the analysis of microarray data. But, such techniques are not much efficient in analyzing the gene expression data. In this paper, we propose a novel efficient hybrid clustering approach based on quad tree data structure. In the hierarchical clustering process, the creation of the closest pair data structure in each level is an important time factor that provides the processing time of clustering. Here, a quad tree based data structure is used for identifying the closest pair elements. Thus, the proposed approach reduces the processing time successfully and offers a better analysis of gene expression data.