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Low-precision feature selection on microarray data: an information theoretic approach

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2022年第5期60卷 1333-1345页

作者： Moran-Fernandez, Laura Bolon-Canedo, Veronica Alonso-Betanzos, Amparo Univ A Coruna CITIC La Coruna Spain

The number of interconnected devices, such as personal wearables, cars, and smart-homes, surrounding us every day has recently increased. The Internet of Things devices monitor many processes, and have the capacity of using machine learning models for pattern recognition, and even making decisions, with the added advantage of diminishing network congestion by allowing computations near to the data sources. The main restriction is the low computation capacity of these devices. Thus, machine learning algorithms capable of maintaining accuracy while using mechanisms that exploit certain characteristics, such as low-precision versions, are needed. In this paper, low-precision mutual information-based feature selection algorithms are employed over DNA microarray datasets, showing that 16-bit and some times even 8-bit representations of these algorithms can be used without significant variations in the final classification results achieved.

关键词： microarray data Low precision Feature selection Mutual information Classification Edge computing Internet of Things

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microarray data Classification Using Feature Selection and Regularized Methods with Sampling Methods 2nd

Microarray Data Classification Using Feature Selection and R...

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2nd International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS)

作者： Jyothi, Saddi Reddy, Y. Sowmya Lavanya, K. Lakireddy Bali Reddy Coll Engn Mylavaram India

ISBN: (纸本)9789811925412;9789811925405

In recent studies of medical field especially, it is essential to assess the expression levels of genes using the microarray technology. Most of the medical diseases like breast cancer, lung cancer, and recent corona are estimated using the gene expressions. The study in this paper focused on performing both classification and feature selection on different microarray data. The gene expression data is high dimensional and extraction of optimal genes in microarray data is challenging task. The feature selection methods Recursive Feature Elimination (REE), Relief, LASSO (Least Absolute Shrinkage And Selection Operator) and Ridge were initially applied to extract optimal genes in microarray data. Later, applied a good number of multi classification methods which includes K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Multilayer Perceptron Networks (MLP), Random Forest (RF) and Logistic Regression (LR). But the combination of mentioned feature selection and classifications required high computation. However, resampling method (i.e., SMOIB = Synthetic Minority Oversampling Technique) prior to the feature selection which enhances the microarray data analysis in classification respectively. The resampling method, with combination of RFE and LASSO feature selection using SVM and LR classification outperforms compared to other methods.

关键词： Gene expression K-Nearest Neighbors LASSO Logistic Regression microarray data Multilayer Perceptron Networks Random Forest Recursive Feature Elimination Ridge Support vector machines

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A Modified Firefly Deep Ensemble for microarray data Classification

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COMPUTER JOURNAL 2022年第12期65卷 3265-3274页

作者： Vijay, Arul Antran S. Prakash, Jothi, V Karpagam Coll Engn Dept Comp Sci & Engn Coimbatore Tamil Nadu India Karpagam Coll Engn Dept Informat Technol Coimbatore Tamil Nadu India

Many researchers are using microarray technology to examine and investigate the levels of gene expression in a specific organism, which is an emerging trend in the field of genetic research. microarray studies have a wide range of applications in the health sector, including disease prediction and diagnostics, as well as cancer research. Due to the existence of irrelevant or duplicated data in microarray datasets, it is difficult to correctly and immediately capture possible patterns using existing algorithms. Feature selection (FS) has evolved into a critical approach for identifying and eliminating the most pertinent qualities. The enormous dimensionality of microarray datasets, on the other hand, presents a significant barrier to the majority of available FS techniques. In this research, we propose a Modified Firefly Feature Selection (MFFS) algorithm that will reduce the irrelevant attributes needed for classification and a Deep Learning Model for classifying the microarray data. The experimental outcomes show that the proposed MFFS algorithm combined with a Hybrid Deep Learning Algorithm outperforms the existing methods in terms of feature set size, accuracy, precision, recall, F-measure and AUC for a dataset with larger number of features.

关键词： deep learning ensemble technique firefly algorithm feature selection microarray data

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Missing value estimation of microarray data using Sim-GAN

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KNOWLEDGE AND INFORMATION SYSTEMS 2022年第10期64卷 2661-2687页

作者： Pati, Soumen Kumar Gupta, Manan Kumar Shai, Rinita Banerjee, Ayan Ghosh, Arijit Maulana Abul Kalam Azad Univ Technol Dept Bioinformat Haringhata 741249 WB India Calcutta Univ Behala Coll Dept Math Kolkata 700060 WB India Jalpaiguri Govt Engn Coll Dept Comp Sci & Engn Jalpaiguri 735102 WB India Calcutta Inst Engn & Management Dept Elect & Commun Engn Kolkata 700040 WB India

microarray data analysis needs utmost care as it plays a significant role in cancer study. Due to the excessive complexity of the data extraction process, it loses some relevant information (missing values) which leads to a significant irrecoverable disruption from the actual scenario. The imputation of missing values is a crucial preprocessing step in analyzing microarray data. Currently, numerous methodologies have been designed to resolve the problem, but the unsatisfactory outcome is obtained with high missing rates of data. In order to estimate the missing expression to complete the dataset, a novel method has been proposed based on the similarity index and generative adversarial network (Sim-GAN). Firstly, the raw dataset has been divided into two subsets, i.e., the target set (which contains genes with missing expression values) and the candidate set (contains without missing values). In the next step, the similarity index between target genes and candidate genes has been obtained. As microarray data represents several biological factors, three similarity matrices (structural similarity, functional similarity, and semantic similarity) have been derived to find the small subset of candidate genes for each target gene. In structural similarity, a novel approach has been used to reduce the time complexity is O(1) as well as tackle the nonlinearity. Now, the obtained subsets are fed into a generative adversarial network to compute the missing values of the targeted genomes. The experimental outcomes consolidate the claim that the proposed methodology gives a satisfactory performance in terms of meaningful expression values. A detailed comparative study based on several statistical (i.e., NRMSE, AUROC, etc.) and biological (i.e., CPP, BLCI) metrics to confirm that the proposed Sim-GAN outperforms the existing missing value estimation techniques.

关键词： microarray data Missing value Structural similarity Functional similarity Semantic similarity Generative adversarial network

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Statistical Analysis of microarray data Clustering using NMF, Spectral Clustering, Kmeans, and GMM

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第2期19卷 1173-1192页

作者： Mirzal, Andri King Fahd Univ Petr & Minerals Informat & Comp Sci Dept Dhahran 31261 Saudi Arabia

In unsupervised learning literature, the study of clustering using microarray gene expression datasets has been extensively conducted with nonnegative matrix factorization (NMF), spectral clustering, kmeans, and gaussian mixture model (GMM)are some of the most used methods. However, there is still a limited number of works that utilize statistical analysis to measure the significances of performance differences between these methods. In this paper, statistical analysis of performance differences between ten NMF, six spectral clustering, four GMM, and the standard kmeans algorithms in clustering eleven publicly available microarray gene expression datasets with the number of clusters ranges from two to ten is presented. The experimental results show that statistically NMFs and kmeans have similar performances and outperform spectral clustering. As spectral clustering can be used to uncover hidden manifold structures, the underperformance of spectral methods leads us to question whether the datasets have manifold structures. Visual inspection using multidimensional scaling plots indicates that such structures do not exist. Moreover, as the plots indicate that clusters in some datasets have elliptical boundaries, GMM methods are also utilized. The experimental results show that GMM methods outperform the other methods to some degree, and thus imply that the datasets follow gaussian distributions.

关键词： Clustering algorithms data models Matrix decomposition Task analysis Statistical analysis Standards Clustering methods Clustering gmm kmeans microarray data NMF spectral method statistical analysis

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MiniAnDE: A Reduced AnDE Ensemble to Deal with microarray data 1

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24th International Conference on Engineering Applications of Neural Networks (EANN)

作者： Torrijos, Pablo Gamez, Jose A. Puerta, Jose M. Univ Castilla La Mancha I3A Albacete 02071 Spain Univ Castilla La Mancha Dept Sistemas Informat Albacete 02071 Spain

ISBN: (数字)9783031342042

ISBN: (纸本)9783031342035;9783031342042

This article focuses on the supervised classification of datasets with a large number of variables and a small number of instances. This is the case, for example, for microarray data sets commonly used in bioinformatics. Complex classifiers that require estimating statistics over many variables are not suitable for this type of data. Probabilistic classifiers with low-order probability tables, e.g., Naive Bayes (NB) and Averaged One-Dependence Estimators (AODE), are good alternatives for dealing with this data type. AODE usually improves NB in accuracy but suffers from high spatial complexity since k models, each with n + 1 variables, are included in the AODE ensemble. In this paper, we propose MiniAnDE, an algorithm that includes only a small number of heterogeneous base classifiers in the ensemble, i.e., each model only includes a different subset of the k predictive variables. Experimental evaluation shows that using MiniAnDE classifiers on microarray data is feasible and outperforms NB and other ensembles such as bagging and random forest.

关键词： Bayesian network classifiers Averaged n-Dependence Estimators microarray data High dimensionality

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Feature Selection Technique for microarray data Using Multi-objective Jaya Algorithm Based on Chaos Theory

Feature Selection Technique for Microarray Data Using Multi-...

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International Conference on Machine Learning and Autonomous Systems (ICMLAS)

作者： Chaudhuri, Abhilasha Sahu, Tirath Prasad Natl Inst Technol Raipur 492001 CG India

ISBN: (纸本)9789811679964;9789811679957

microarray datasets are high-dimensional data that contain the gene expression profiling information used for cancer classification. This causes a curse of dimensionality and poses multiple challenges in gene classification. Moreover, the microarray datasets are imbalanced as they have a very small sample size, which deteriorates the performance of machine learning algorithms. In order to address these problems, dimensionality reduction technique is required. Meta-heuristic optimization algorithm-based feature selection is the efficient dimensionality reduction technique. This paper introduces a feature selection technique for microarray data classification using multi-objective Jaya algorithm based on chaos theory. The two objectives: minimization of selected gene subset and maximization of classification accuracy are considered. Chaos theory is used to improve the convergence of Jaya algorithm. Five benchmarkmicroarray datasetswere used to evaluate the performance of the proposed feature selection technique. Furthermore, a comparative study is carried out where the performance of the proposed technique is compared with four other competitors.

关键词： Chaotic Jaya algorithm microarray data Multi-objective feature selection

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Feature selection of microarray data using multidimensional graph neural network and supernode hierarchical clustering

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第3期57卷 63-63页

作者： Xie, Weidong Zhang, Shoujia Wang, Linjie Yu, Kun Li, Wei Northeastern Univ Sch Comp Sci & Engn Shenyang Peoples R China Northeastern Univ Key Lab Intelligent Comp Med Image MIIC Minist Educ Shenyang Peoples R China Northeastern Univ Coll Med & Bioinformat Engn Shenyang Peoples R China

Cancer remains a significant cause of mortality, and the application of microarray technology has opened new avenues for cancer diagnosis and treatment. However, due to the challenges in sample acquisition, the genetic dimension of microarray data surpasses the sample dimension, resulting in high-dimensional small sample data. Effective feature selection is crucial for identifying biomarkers and facilitating further analysis. However, existing methods struggle to fully exploit the interdependencies among genes, such as regulatory networks and pathways, to guide the feature selection process and construct efficient classification models. In this paper, we propose a novel feature selection algorithm and classification model based on graph neural networks to address these challenges. Our proposed method employs a multidimensional graph to capture intricate gene interactions. We leverage link prediction techniques to enhance the graph structure relationships and employ a multidimensional node evaluator alongside a supernode discovery algorithm based on spectral clustering for initial node filtering. Subsequently, a hierarchical graph pooling technique based on downsampling is used to further refine node selection for feature extraction and model building. We evaluate the proposed method on nine publicly available microarray datasets, and the results demonstrate its superiority over both classical and advanced feature selection techniques in various evaluation metrics. This highlights the effectiveness and advancement of our proposed approach in addressing the complexities associated with microarray data analysis and cancer classification.

关键词： Graph neural networks Feature selection Graph pooling Feature dependencies microarray data

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Optimization of dynamic bi-clustering based on improved genetic algorithm for microarray data

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PATTERN ANALYSIS AND APPLICATIONS 2024年第3期27卷 97-97页

作者： Ram, Pintu Kumar Kuila, Pratyay Natl Inst Technol Dept Comp Sci & Engn Ravangla 737139 Sikkim India

Due to the nature of microarray data, the analysis of genes/features for disease diagnosis is a challenging task. Generally, the data comes in the form of a 2D matrix, where the row represents the genes and the column indicates the various conditions. Bi-clustering is an emerging technique that can efficiently reveal patterns of genes. It can perform simultaneously with a subset of genes and conditions. Inspired by this, dynamic bi-clustering based on an improved genetic algorithm (GA) is proposed. The chromosomes are efficiently designed. In addition, the fitness function is derived by considering multiple conflicting objectives to measure the quality of a cluster. A novel mutation is designed by the correlation technique. The crossover and mutation rates are dynamically changed. The obtained outcomes of the proposed approach are compared with the various existing approaches, such as traditional GA, the dynamic dame parallel GA, the evolutionary local search algorithm, bi-phase evolutionary searching, and the evolutionary bi-clustering algorithm. Further, statistical tests such as the analysis of variance and Friedman test are executed to show the significance of the proposed model. A biological analysis is also performed.

关键词： Bi-cluster GA Correlation ANOVA microarray data

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microarray data Classification Based on Neighbourhood Components Analysis Projection Method 6

Microarray Data Classification Based on Neighbourhood Compon...

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IEEE 6th International Conference on Big data Analytics (ICBDA)

作者： Zhang, Chuanlei Liu, Lixin Zhang, Shanwen Huang, Chengliang Tianjin Univ Sci & Technol Sch Artificial Intelligence Tianjin 300222 Peoples R China Xijing Univ Sch Informat Engn Xian 710123 Peoples R China Ryerson Univ Ted Rogers Sch Informat Technol Management Toronto ON M5B 2K3 Canada

ISBN: (纸本)9781665415132

microarray data can contribute to precise classification of tumors. However, it is difficult to solve the problems caused by the curse of dimensionality and small sample size of the microarray data. Neighbourhood components analysis (NCA) is a method which can maximize a stochastic variant of the leave-one-out KNN score on the training data set and can effectively learn linear projection matrices for dimensionality reduction in classification. Based on NCA, a discriminant projection method, called NCADP, is proposed to solve the two problems in tumor classification task. Firstly, the project matrix is constructed by using the classification probability. Secondly, the high-dimensionality microarray data are projected into a lower dimensional space. Finally, the K-nearest neighbourhood classifier is employed to classify the tumors in the low-subspace. The proposed NCADP is non-parametric, without making assumptions about the shape of the class distributions or the boundaries between them, and it achieved higher correct classification rate than traditional ones in the experiments over two public microarray datasets, which verified that the proposed method is effective and feasible for tumor classification.

关键词： microarray data tumor classification neighbourhood components analysis (NCA) discriminant analysis

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