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An ensemble framework for microarray data classification based on feature subspace partitioning

COMPUTERS IN BIOLOGY AND MEDICINE 2022年 148卷 105820-105820页

作者： Nosrati, Vahid Rahmani, Mohsen Arak Univ Fac Engn Dept Comp Engn Arak *** Iran

Feature selection is exposed to the curse of dimensionality risk, and it is even more exacerbated with high -dimensional data such as microarrays. Moreover, the low-instance/high-feature (LIHF) property of microarray data needs considerable processing time to do some calculations and comparisons among features to choose the best subset of them, which has led to many efforts to subdue the LIHF property of such genomic medicine data. Due to the promising results of the ensemble models in machine learning problems, this paper presents a novel framework, named feature-level aggregation-based ensemble based on overlapped feature subspace partitioning (FLAE-OFSP) for microarray data classification. The proposed ensemble has three main steps: after generating several subsets by the proposed partitioning approach, a feature selection algorithm (i.e., a feature ranker) is applied on each subset, and finally, their results are combined into a single ranked list using six defined aggregation functions. Evaluation of the presented framework based on seven microarray datasets and using four measures, including stability, classification accuracy, runtime, and Modscore shows substantial runtime improvement and also quality results in other evaluated measures compared to individual methods.

关键词： Ensemble learning Feature selection Feature subspace partitioning microarray data

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Comparative Study of Embedded Feature Selection Methods on microarray data 17th

Comparative Study of Embedded Feature Selection Methods on M...

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17th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI) / 6th 5G-PINE Workshop / AI-BIO Workshop / DAAI Workshop / DARE Workshop / EEAI Workshop / 10th MHDW Workshop

作者： Hamla, Hind Ghanem, Khadoudja Univ Abdelhamid Mehri Constantine2 Constantine Algeria Lab Modelling & Implementat Complex Syst Constantine Algeria

ISBN: (纸本)9783030791520;9783030791506;9783030791490

microarray data collects information from tissues that could be used in early diagnosis such as cancer. However, the classification of microarray data is a challenging task due to the high number of features and a small number of samples leading to poor classification accuracy. Feature selection is very effective in reducing dimensionality;it eliminates redundant and irrelevant features to enhance the classifier's performance. In order to shed light on the strengths and weaknesses of the existing techniques, we compare the performances of five embedded feature selection methods namely decision tree, random forest, lasso, ridge, and SVM-RFE. Ten well-known microarray datasets are tested. Obtained results show the outperformance of SVM-RFE in term of accuracy, and comes in the second position after decision tree in terms of number of selected features and execution time.

关键词： Feature selection Machine learning Embedded methods microarray data SVM-RFE

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Cooperative Coevolutionary Multiobjective Genetic Programming for microarray data Classification

Cooperative Coevolutionary Multiobjective Genetic Programmin...

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2nd Genetic and Evolutionary Computation Conference (GECCO)

作者： Qing, Yang Ma, Chi Zhou, Yu Zhang, Xiao Xia, Haowen Shenzhen Univ Shenzhen Peoples R China South Cent Univ Nationalities Wuhan Peoples R China

ISBN: (纸本)9781450383509

DNA microarray data contain valuable biological information, which makes it of great importance in disease analysis and cancer diagnosis. However, the classification of microarray data is still a challenging task because of the high dimension and small sample size, especially for multiclass data accompanied by class imbalance. In this paper, we propose a cooperative coevolutionary multiobjective genetic programming (CC-MOGP) for microarray data classification. It converts a multiclass problem into a set of tractable binary problems and coevolves the corresponding population. And a cooperative coevolutionary Pareto archived evolution strategy (CCPAIRS) is employed to approximate the Pareto front. During this procedure, we propose a synergy test method to assist in guiding the coevolution between populations. Experimental results on 8 multiclass microarray data show that CC-MOGP can obtain competitive prediction accuracy compared with several state-of-art evolutionary computation and traditional methods.

关键词： Genetic Programming Coevolution microarray data Classification

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Multi-objective feature selection based on quasi-oppositional based Jaya algorithm for microarray data

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KNOWLEDGE-BASED SYSTEMS 2022年第0期236卷 107804-107804页

作者： Chaudhuri, Abhilasha Sahu, Tirath Prasad Natl Inst Technol Raipur Dept Informat Technol Chhattisgarh 492010 India

Feature selection is the highly sought-after pre-processing technique for microarray data classification. microarray data is a phenomenally imbalanced, high-dimensional dataset which requires a specifically designed feature selection technique. A hybrid feature selection (FS) technique is designed in this work for microarray datasets. The proposed FS technique has two phases. In the first phase, aggregate ranking of five well-known filter methods is obtained using the PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluations) method and is used to remove the unwanted features. The features selected in the first phase are then passed on to the second phase for further refinement. In the second phase, the quasi-oppositional-based multi-objective Jaya algorithm is designed for wrapper-based feature selection. Moreover, a novel quasi-opposite number generation technique for binary numbers is proposed. Ten benchmark microarray datasets are used to evaluate the performance of the proposed technique. Seven other state-of-the-art feature selection techniques are used for comparison and the proposed technique is found to outperform other techniques in terms of classification error rate and the selected number of features. In addition, the proposed FS technique is found to be ten times faster than other FS techniques. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Multi-objective feature selection microarray data Jaya algorithm Quasi-oppositional PROMETHEE

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Identification of Alzheimer associated differentially expressed gene through microarray data and transfer learning-based image analysis

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NEUROSCIENCE LETTERS 2022年第0期766卷 136357-136357页

作者： George, Benu Gokhale, Sheetal D. Yaswanth, P. M. Vijayan, Ajay Devika, S. Suchithra, T., V Natl Inst Technol Calicut Sch Biotechnol Kozhikode 673601 India KJ Somaiya Coll Engn Dept Informat Technol Mumbai 400077 Maharashtra India

Major factors contribute to mental stress and enhance the progression of late-onset Alzheimer's disease (AD). The factors that lead to neurodegeneration, such as tau protein hyperphosphorylation and increased amyloid-beta production, can be mimicked in animal stress models. The present study identifies differentially expressed genes (DEGs) data and its corresponding predictive image analysis in rat models. The gene expression profile of GSE72062, GSE85162, GSE143951 and GSE85238 was downloaded from NCBI, GEO archive to analyse DEGs. Functional enrichment and pathway relationship networks, gene signal, protein interaction and micro-RNA interaction DEGs networks were constructed and investigated. The image analysis of histopathological slides of rat brain images corresponding to AD microarray-based DEGs profile was undertaken using the convolution neural networks (ConvNets) model. Enrichment of network in terms of GO concluded with 10 DEGs, namely ARHGAP32, GNA11, NR5A1, GNAT3, FOSL1, HELZ2, NMUR2, BDKRB1, RPL3L and RPL39L as potential gene targets to control neurodegeneration and progression of sporadic AD. The image analysis of AD microarray-based DEGs profile builds a successful predictive model of 89% and 61% training and test accuracy with a minimum of 2.480% loss using transfer learning, VGG16 model. Interestingly, the ARHGAP32 gene, a Rho GTPase activating class, was identified to have a functional relationship with two significant genes BCL2 and MMP9, that are well explored in AD. The current investigation upgrades the traditional pre-clinical AD research using microarray data analysis and ConvNets. The model successfully predicts DEG from histopathology slides of rat brain samples, paving the way for image analysis to determine the underlying molecular makeup of the test samples.

关键词： Gene expression data microarray data Alzheimer Neurodegeneration VGG-16 Transfer learning

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Cancer classification from time series microarray data through regulatory Dynamic Bayesian Networks

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COMPUTERS IN BIOLOGY AND MEDICINE 2020年 116卷 103577-103577页

作者： Kourou, Konstantina Rigas, George Papaloukas, Costas Mitsis, Michalis Fotiadis, Dimitrios I. Univ Ioannina Dept Mat Sci & Engn Unit Med Technol & Intelligent Informat Syst GR-45110 Ioannina Greece Univ Ioannina Dept Biol Applicat & Technol GR-45110 Ioannina Greece Univ Ioannina Fac Med Sch Hlth Sci Dept Surg GR-45110 Ioannina Greece Univ Ioannina Fac Med Sch Hlth Sci Canc Biobank Ctr GR-45110 Ioannina Greece Fdn Res & Technol Hellas Dept Biomed Res Inst Mol Biol & Biotechnol GR-45110 Ioannina Greece

Genomic profiling of cancer studies has generated comprehensive gene expression patterns for diverse phenotypes. Computational methods which employ transcriptomics datasets have been proposed to model gene expression data. Dynamic Bayesian Networks (DBNs) have been used for modeling time series datasets and for the inference of regulatory networks. Furthermore, cancer classification through DBN-based approaches could reveal the importance of exploiting knowledge from statistically significant genes and key regulatory molecules. Although microarray datasets have been employed extensively by several classification methods for decision making, the use of new knowledge from the pathway level has not been addressed adequately in the literature in terms of DBNs for cancer classification. In the present study, we identify the genes that act as regulators and mediate the activity of transcription factors that have been found in all promoters of our differentially expressed gene sets. These features serve as potential priors for distinguishing tumor from normal samples using a DBN-based classification approach. We employed three microarray datasets from the Gene Expression Omnibus (GEO) public functional repository and performed differential expression analysis. Promoter and pathway analysis of the identified genes revealed the key regulators which influence the transcription mechanisms of these genes. We applied the DBN algorithm on selected genes and identified the features that can accurately classify the samples into tumors and controls. Both accuracy and Area Under the Curve (AUC) were high for the gene sets comprising of the differentially expressed genes along with their master regulators (accuracy: 70.8%-98.5%;AUC: 0.562-0.985).

关键词： Genomic profiling microarray data Dynamic Bayesian Networks Cancer classification Regulatory genes

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Ensemble Feature Selection for Breast Cancer Classification using microarray data

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INTELIGENCIA ARTIFICIAL-IBEROAMERICAN JOURNAL OF ARTIFICIAL INTELLIGENCE 2020年第65期23卷 100-114页

作者： Hengpraprohm, Supoj Jungjit, Suwimol Nakhon Pathom Rajabhat Univ Fac Sci & Technol Data Sci Program Muang Nakhon Pathom Thailand Thaksin Univ Fac Sci Dept Comp & Informat Technol Phatthalung Thailand

This paper proposes an ensemble filter feature selection approach, EnSNR, for breast cancer data classification. The microarray dataset used in the experiments contains 50,739 features (genes) for each of 32 patients. The main idea of the EnSNR approach is to combine informative features which are obtained using two different sets of feature evaluation criteria. Features in the EnSNR subset are those features which are present in both sets of evaluation results. Entropy and SNR evaluation functions are used to generate the EnSNR feature subset. Entropy is a measure of the amount of uncertainty in the outcome of a random experiment, while SNR is an effective function for measuring feature discriminative power. Entropy and SNR functions provide some advantages for the EnSNR approach. For example, the number of features in the EnSNR subset is not user-defined (the EnSNR subset is generated automatically);and the operation of the EnSNR function is independent of the type of classification algorithm employed. Also, only a small amount of processing time is required to generate the EnSNR feature subset. A Genetic Algorithm (GA) generates the breast cancer classification 'model' using the EnSNR feature subset. The efficiency of the 'model' is validated using 10-Fold Cross-Validation re-sampling. When the `EnSNR' feature subset is used, as well as giving a high degree of prediction accuracy (the average prediction accuracy obtained in the experiments in this paper is 86.92 +/- 5.47), the EnSNR approach significantly reduces the number of irrelevant features (genes) to be analyzed for cancer classification.

关键词： Ensemble approach Feature selection microarray data Genetic Algorithm Cancer Classification

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Identification of calcineurin as a predictor of oocyte quality and fertilization competence based on microarray data

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2021年 94卷 107561-107561页

作者： Daei-Farshbaf, Neda Aflatoonian, Reza Amjadi, Fatemeh-sadat Nikniyaz, Hossein Taleahmad, Sara Bakhtiyari, Mehrdad Iran Univ Med Sci Fac Med Dept Anat POB 14155-5983 Tehran Iran Acad Ctr Educ Culture & Res Dept Endocrinol & Female Infertil Reprod Biomed Res Ctr Royan Inst Reprod Biomed POB 16635-148 Tehran Iran Iran Univ Med Sci Fac Med Cellular & Mol Res Ctr POB 14155-5983 Tehran Iran Acad Ctr Educ Culture & Res Dept Mol Syst Biol Cell Sci Res Ctr Royan Inst Stem Cell Biol & Technol RI SCBT POB 16635-148 Tehran Iran

Objective: The aim of our study was to detect a biomarker for selection of competent oocytes with acceptable fertilization potential. Calcium ion fluctuation play the most critical role of modulating intercellular signaling pathways in oocyte maturation, egg activation and the egg-to-embryo transition. Since, the stimulatory action of calcium ion is mediated by binding to certain proteins, the calcium/calmodulin-binding genes (CBGs), as the main calcium binding group, was analyzed in detail. Methods: In this work, bioinformatics analysis was conducted on the CBGs of human cumulus cells (CCs) to elucidate a reliable biomarker for fertile oocyte selection. Calcineurin (CaN) or protein phosphatase 3 (PPP3) was selected which consists of a catalytic subunit A with PPP3CA (A alpha), PPP3CB (A beta), and PPP3CC (A gamma) isoforms and a regulatory subunit B. Whereas CaN A regulates calcium ion function, our study gives insights to probable role of related isoforms within human oogenesis process. The presence of CaN A in CCs surrounding growing and mature oocytes was confirmed by western blotting and the expression patterns of related isoforms were examined by reverse transcription-quantitative PCR (RT-qPCR). Results: Our results indicated the increased expression of the catalytic subunit of CaN protein in the CCs of metaphase (M) II oocytes. The expression level of PPP3CB was significantly elevated in CCs of fertile MII compared with those in the germinal vesicle (GV), MI and unfertilized MII oocytes (P <= 0.05). Conclusion: Elevated level of PPP3CB isoform in the CCs of fertile MII oocyte could be a reliable indication of oocyte fertilization potential. However, further researches are required to introduce CaN A beta as an appropriate biomarker for oocyte selection in assisted reproduction technique (ART) programs.

关键词： Human cumulus cells Calcineurin Protein phosphatase 3 Bioinformatics microarray data Calmodulin-binding genes

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microarray data Classification Based on Computational Verb

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IEEE ACCESS 2019年 7卷 103310-103324页

作者： Liu, Kun-Hong Ng, Vincent To Yee Liong, Sze-Teng Hong, Qingqi Xiamen Univ Sch Informat Xiamen 361005 Fujian Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Feng Chia Univ Dept Elect Engn Taichung 40724 Taiwan

Computational verb (CV) theory is a relatively new research field in mathematics and has been applied to many different fields. In the field of pattern recognition, the CV-based rule induction algorithm can generate some simple rules with CVs and adverbs by linguistically interpretable forms. In this paper, we present an interpretable rule extraction framework based on CV rule theory for the classification of microarray data. In contrast to the existing rule-based methods, the CV method enables to explicitly express the relationships of the genes based on some mathematical templates and hence enhance the understanding on the data results. Stay is a typical verb used in the CV to describe the trend of changes. In our algorithm, Stay is applied to generate CVR by a gene pair, named SCVR. The corresponding evolving and similarity functions for calculating the difference between SCVR rules are also presented to illustrate this process. Similar to other rule-based methods, the SCVR can achieve significant gene selection and cancer classification task concurrently. To evaluate the performance of our proposed approach, we conduct the experiments on several binary class and multiclass microarray datasets. Experiments confirm that the proposed method can outperform many rule-based classiers with the fusion of five rules.

关键词： Computational verb computational verb rules stay microarray data classifier ensemble

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Clustering-based hybrid feature selection approach for high dimensional microarray data

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2021年 213卷 104305-104305页

作者： Babu, Samson Anosh P. Annavarapu, Chandra Sekhara Rao Dara, Suresh IIT ISM Dhanbad Dept Comp Sci & Engn Jharkand India B V Raju Inst Technol Dept Comp Sci & Engn Narsapur Telangana India

The DNA microarrays are used to monitor the expression levels of significant genes. Most of the microarray data are assumed to be high dimensional, redundant, and noisy. This paper proposed a clustering-based hybrid gene selection approach to reduce the high dimensionality and increase the classification accuracy of cancer microarray data. The proposed approach uses the combined method of k-means clustering algorithm and signal-to-noise-ratio ranking method as a primary filtering method to reduce the high dimensionality of the microarray dataset. A cellular learning automaton combined with ant colony optimization is then applied on the reduced dataset as a wrapper method to get the optimized gene subset. The classifiers adopted to evaluate the proposed method are support vector machine, K-nearest neighbor, and Naive Bayes. The experiments showed promising results in gene subset selection and classification.

关键词： microarray data Clustering Ant colony optimization Classification

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