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Regularized Sparse Modelling for microarray Missing Value Estimation

IEEE ACCESS 2021年 9卷 16899-16913页

作者： Wang, Aiguo Yang, Jing An, Ning Foshan Univ Sch Elect Informat Engn Foshan 528225 Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Peoples R China

The existence of missing values in microarray data inevitably hinders downstream biological analyses that expect complete data as input, therefore how to effectively explore the underlying structure of data to accurately estimate missing entries remains crucial and meaningful. In this study, we formalize the problem under a regularized sparse framework and accordingly propose local learning-based imputation models to capture the relationships that are hidden in gene expression profiles towards better imputation. Specifically, in view of the simultaneous variable selection and grouping effect of the elastic net penalty, we present an elastic net regularized local least squares-based imputation method to estimate the missing entries of a target gene with its neighbors. Besides, we investigate different similarity filtering metrics to select neighbor genes and develop another four imputation methods under the framework. Furthermore, the proposed methods process the target genes in ascending order of their associated missing rates. Finally, extensive comparative experiments against other eight commonly-used methods are conducted on multiple microarray datasets having varying missing rates. Results indicate the power of sparse regularization techniques and the superiority of elastic net over its competitors in terms of statistical analysis metrics.

关键词： microarray data missing value imputation local structure penalty

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Integrative Clustering Analysis with Application in Multi-Source Gene Expression data

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Journal of data Science 2022年第1期20卷 14-33页

作者： Yang, Liuqing Pan, Qing Zhao, Yunpeng Department of Statistics George Washington University Washington DC United States School of Mathematical and Natural Sciences Arizona State University Tempe AZ United States

In omics studies, different sources of information about the same set of genes are often available. When the group structure (e.g., gene pathways) within the genes are of interests, we combine the normal hierarchical model with the stochastic block model, through an integrative clustering framework, to model gene expression and gene networks jointly. The integrative framework provides higher accuracy in extensive simulation studies when one or both of the data sources contain noises or when different data sources provide complementary information. An empirical guideline in the choice between integrative versus separate clustering models is proposed. The integrative clustering method is illustrated on the mouse embryo single cell RNAseq and bulk cell microarray data, which identified not only the gene sets shared by both data sources but also the gene sets unique in one data source. © 2022 Center for Applied Statistics, School of Statistics, Renmin University of China. All rights reserved.

关键词： EM algorithm empirical guidelines microarray data normal hierarchical model single cell RNAseq stochastic block model

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MGRFE: Multilayer Recursive Feature Elimination Based on an Embedded Genetic Algorithm for Cancer Classification

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021年第2期18卷 621-632页

作者： Peng, Cheng Wu, Xinyu Yuan, Wen Zhang, Xinran Zhang, Yu Li, Ying Jilin Univ Coll Comp Sci & Technol Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Jilin Peoples R China Tsinghua Univ Sch Software Beijing Peoples R China

microarray gene expression data have become a topic of great interest for cancer classification and for further research in the field of bioinformatics. Nonetheless, due to the "large p, small n" paradigm of limited biosamples and high-dimensional data, gene selection is becoming a demanding task, which is aimed at selecting a minimal number of discriminatory genes associated closely with a phenotype. Feature or gene selection is still a challenging problem owing to its nondeterministic polynomial time complexity and thus most of the existing feature selection algorithms utilize heuristic rules. A multilayer recursive feature elimination method based on an embedded integer-coded genetic algorithm, MGRFE, is proposed here, which is aimed at selecting the gene combination with minimal size and maximal information. On the basis of 19 benchmark microarray datasets including multiclass and imbalanced datasets, MGRFE outperforms state-of-the-art feature selection algorithms with better cancer classification accuracy and a smaller selected gene number. MGRFE could be regarded as a promising feature selection method for high-dimensional datasets especially gene expression data. Moreover, the genes selected by MGRFE have close biological relevance to cancer phenotypes. The source code of our proposed algorithm and all the 19 datasets used in this paper are available at https://***/Pengeace/MGRFE-GaRFE.

关键词： Feature extraction Microwave integrated circuits Genetic algorithms Cancer Nonhomogeneous media Gene expression Heuristic algorithms Gene selection genetic algorithm recursive feature elimination microarray data cancer classification

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Classification of gene expression patterns using a novel type-2 fuzzy multigranulation-based SVM model for the recognition of cancer mediating biomarkers

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NEURAL COMPUTING & APPLICATIONS 2021年第9期33卷 4263-4281页

作者： Ghosh, Swarup Kr Ghosh, Anupam Sister Nivedita Univ Dept Comp Sci & Engn Kolkata India Netaji Subhash Engn Coll Dept Comp Sci & Engn Kolkata India

In this article, we propose a novel type-2 fuzzy multigranulation-based SVM model for gene expression pattern classification on human breast cancer dataset. Firstly, a type-2 fuzzy multigranulation system has been designed for the classification task dealing with noisy and nonlinear microarray gene expression patterns. Thereafter, the fuzzy if-then rules have been devised on the feature vectors of microarray to enable accurate bilinear classification process. The fuzzy if-then rules in the domain of type-2 fuzzy multigranulation system are able to identify efficient expression patterns that have been deferentially expressed from normal state to carcinogenic state. The proposed method reduces the structural complexity of the fuzzy if-then rules (type 1) since it works on upper and lower membership functions instead of a single membership function. In addition, a fuzzy rough approximation has been utilized in the model to reduce the computational cost. Lastly, the association among genes consisting of significantly different expression patterns from normal state to malignant state has been recognized with respect to their nature. The effectiveness of the proposed method has been implemented on eight microarray gene expression datasets for human breast cancer patients. Moreover, we have validated the results by F-score and NCBI database which signify that the proposed model performs better in comparison with the existing methods.

关键词： microarray data Multigranulation Type-2 fuzzy sets Rough sets Classifier Support vector machine

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A comprehensive meta-analysis to identify transcriptional signatures of abiotic stress responses in barley (Hordeum vulgare)

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CEREAL RESEARCH COMMUNICATIONS 2021年第3期49卷 385-391页

作者： Zinati, Zahra Sazegari, Sima Tahmasebi, Ahmad Delavari, Azar Shiraz Univ Coll Agr & Nat Resources Darab Dept Agroecol Shiraz Iran Shiraz Univ Inst Biotechnol Shiraz Iran Shiraz Univ Coll Agr Dept Crop Prod & Plant Breeding Shiraz Iran

Barley, as one of the major cereals, possesses natural tolerance to major abiotic stresses such as drought and salinity, so that it is an outstanding model in abiotic stresses research. The study focuses on meta-analysis by combining different datasets of barley abiotic stress-related microarray data to identify stress-responsive genes and pathways. In addition to a thorough investigation to determine the up and downregulated gene sets under stress conditions, other analyses including gene ontology (GO) enrichment and protein-protein interaction network analysis were performed for a comprehensive study of differentially expressed genes under abiotic stresses. A total of 256 microarray samples from 14 different experiments were analyzed and 3723 probe sets were identified. The metabolic processes, cellular process, localization, biological regulation and regulation of biological process were the top enriched GO terms. Interestingly, the response to abiotic stress in the functional group contained the highest number of upregulated genes. In addition, the photosynthesis biological category included only downregulated genes. Fourteen genes in this category were related to photosystem II while only six genes belonged to photosystem I. Network analysis of DEGs revealed 52 and 57 genes as critical genes in down and upregulated networks, respectively;module analysis unveiled 28 and 23 clusters for up and downregulated networks. Regarding the GO analysis of modules, one upregulated cluster and two downregulated clusters exhibited a direct response to abiotic stress.

关键词： Barley Abiotic stress tolerance Meta-analysis microarray data Gene ontology (GO) enrichment Network analysis

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An Adaptive Harmony Search Approach for Gene Selection and Classification of High Dimensional Medical data

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2021年第2期33卷 195-207页

作者： Dash, Rasmita Siksha O Anusandhan Deemed Univ Dept Comp Sci & Engn Bhubaneswar 751030 Odisha India

In Bioinformatics, microarray data analysis has gained substantial attention for disease diagnosis. microarray data is represented with a huge search space which imposes the foremost difficulties in selection of most relevant facts in terms of genes. In this esteem, we have recommended a hybridised harmony search and Pareto optimization approach for feature selection in high dimensional data classification problem. In the first stage an adaptive harmony search algorithm for gene selection with probability distribution factor for optimal gene ranking is implemented. This selection is further refined applying a biobjective Pareto based feature selection technique to select optimal minimum number of top ranked genes. The importance and relevance of the selected genes are verified through a few numbers of classifiers. Experimental analysis is conducted over four well known microarray datasets. Finally statistical analysis is conducted to prove the superiority of proposed work with two other nature inspired algorithms. Simulation result reveals that the proposed hybridisation is providing high potentiality in both sample classification and feature subset prediction prospective for high dimensional databases. (C) 2018 The Author. Production and hosting by Elsevier B.V. on behalf of King Saud University.

关键词： Feature selection microarray data Filter method Wrapper method Harmony search Pareto optimal solution

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Late acceptance hill climbing aided chaotic harmony search for feature selection: An empirical analysis on medical data

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期221卷

作者： Naskar, Anurup Pramanik, Rishav Hossain, S. K. Sabbir Mirjalili, Seyedali Sarkar, Ram Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Torrens Univ Ctr Artificial Intelligence Res & Optimizat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Obuda Univ Univ Res & Innovat Ctr Budapest Hungary

In today's era of data-driven digital society, there is a huge demand for optimized solutions that essentially reduce the cost of operation, thereby aiming to increase productivity. Processing a huge amount of data, like the microarray based gene expression data, using machine learning and data mining algorithms has certain limitations in terms of memory and time requirements. This would be more concerning, when a dataset comes with redundant and non-important information. For example, many report-based medical datasets have several non-informative attributes which mislead the classification algorithms. To this end, researchers have been developing several feature selection algorithms that try to discard the redundant information from the raw datasets before feeding them to machine learning algorithms. Metaheuristic based optimization algorithms provide an excellent option to solve feature selection problems. In this paper, we propose a music-inspired harmony search (HS) algorithm based wrapper feature selection method. At the beginning, we use a chaotic mapping to initialize the population of the HS algorithm in order to better coverage of the search space. Further to complement the inferior exploitation of the HS algorithm, we integrate it with the Late Acceptance Hill Climbing (LAHC) method. Thus the combination of these two algorithms provides a good balance between the exploration and exploitation of the HS algorithm. We evaluate the proposed feature selection method on 15 UCI datasets and the obtained results are found to be better than many state-of-the-art methods both in terms of the classification accuracy and the number of features selected. To evaluate the effectiveness of our algorithm, we utilize a combination of precision, recall, F1 score, fitness value, and execution time as performance indicators. These metrics enable us to obtain a comprehensive assessment of the algorithm's abilities and limitations. We also apply our method on 3 microarray based

关键词： Feature selection microarray data Harmony search Late Acceptance Hill Climbing Metaheuristics COVID-19 data Optimization Algorithm

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Dimension Reduction and Classifier-Based Feature Selection for Oversampled Gene Expression data and Cancer Classification

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PROCESSES 2023年第7期11卷 1940页

作者： Petinrin, Olutomilayo Olayemi Saeed, Faisal Salim, Naomie Toseef, Muhammad Liu, Zhe Muyide, Ibukun Omotayo City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Birmingham City Univ Sch Comp & Digital Technol Dept Comp & Data Sci DAAI Res Grp Birmingham B4 7XG England Univ Teknol Malaysia Ibnu Sina Inst Sci & Ind Res UTM Big Data Ctr Johor Baharu 81310 Johor Malaysia Georgia Inst Technol Coll Comp Atlanta GA 30332 USA

Gene expression data are usually known for having a large number of features. Usually, some of these features are irrelevant and redundant. However, in some cases, all features, despite being numerous, show high importance and contribute to the data analysis. In a similar fashion, gene expression data sometimes have limited instances with a high rate of imbalance among the classes. This can limit the exposure of a classification model to instances of different categories, thereby influencing the performance of the model. In this study, we proposed a cancer detection approach that utilized data preprocessing techniques such as oversampling, feature selection, and classification models. The study used SVMSMOTE for the oversampling of the six examined datasets. Further, we examined different techniques for feature selection using dimension reduction methods and classifier-based feature ranking and selection. We trained six machine learning algorithms, using repeated 5-fold cross-validation on different microarray datasets. The performance of the algorithms differed based on the data and feature reduction technique used.

关键词： cancer classification gene expression machine learning microarray data sampling methods

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Optimizing microarray cancer gene selection using swarm intelligence: Recent developments and an exploratory study

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EGYPTIAN INFORMATICS JOURNAL 2023年第4期24卷

作者： Isuwa, Jeremiah Abdullahi, Mohammed Ali, Yusuf Sahabi Hassan, Ibrahim Hayatu Buba, Jesse Romeo Aliyu, Ibrahim Kim, Jinsul Oyelade, Olaide Nathaniel Fed Univ Kashere Dept Comp Sci Gombe Nigeria Ahmadu Bello Univ Dept Comp Sci Zaria Nigeria Ahmadu Bello Univ Inst Agr Res Zaria Nigeria Univ Maiduguri Dept Med Lab Sci Maiduguri Nigeria Chonnam Natl Univ Dept ICT Convergence Syst Engn Gwangju 61186 South Korea

microarray data represents a valuable tool for the identification of biomarkers associated with diseases and other biological conditions. Genes, in particular, are a type of biomarker that holds great importance for the identi-fication and understanding of various types of tumors, including brain, lung, and breast cancers. However, a significant portion of these cancer genes are not directly associated with the target disease, which can lead to challenges during analysis, such as increased computational complexity, poor generalization, and decreased classification accuracy, among others. To address this issue, a range of techniques and algorithms have been developed to optimize the selection of the most relevant subset of cancer genes. One highly effective approach to handle this challenge is the use of Swarm Intelligent (SI) algorithms, which are known for their efficiency and effectiveness as global search agents. In this paper, we present two distinct but related sections. First, we conduct a survey of current literature from 2019 to the present, on the use of SI algorithms for optimizing the selection of an optimal subset of cancer genes. Secondly, based on the analysis and findings from the first part, a presentation of an experimental study that evaluates the efficacy of four classical SI algorithms -Particle Swarm Optimization (PSO), Salp Swarm Optimization (SSA), Firefly Algorithm (FA), and Cuckoo Search (CS) - for optimizing the selection of relevant genes in three different cancer datasets. For the experimental study, we used the Chi-square, Mutual Information, and ANOVA filter methods to individually select 100, 200, and 500 relevant genes from the identified cancer datasets. We then passed these genes as input to each of the SI algorithms. The results of the study indicate that diverse filter-wrapper combinations can effectively address the challenge of selecting cancer genes across various datasets.

关键词： Swarm intelligence Filter methods microarray data Feature selection Machine learning

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Gene expression profiling of corona virus microarray datasets to identify crucial targets in COVID-19 patients

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GENE REPORTS 2021年 22卷 100980页

作者： Ramesh, Priyanka Veerappapillai, Shanthi Karuppasamy, Ramanathan Vellore Inst Technol Sch Bio Sci & Technol Dept Biotechnol Vellore Tamil Nadu India

The current outbreak of coronavirus disease (COVID-19) has been affecting millions of people and has caused devastating mortality worldwide. Moreover, it is to be noted that cytokine storm has become an important cause for the rising mortality. However, the efforts for the development of drugs, vaccines and treatment has also been intervened due to poor understanding of host's defense mechanism and also due to the development of cytokine storm against this viral infection. Thus, a deeper understanding of the mechanism behind the immune dysregulation and cytokine storm development might give us clues for the clinical management of the severe cases. Hence, we have implemented differential gene expression analysis together with protein-protein interaction and Gene Ontology (GO) studies with the help of Severe Acute respiratory syndrome coronavirus (SARS-CoV) data sets such as GSE1739 and GSE33267 to give us more knowledge on the host immune response for the pathogenic coronavirus which in turn reduces the mortality. A total of 79 differentially-expressed genes (DEGs) were identified in our data set using the filters such as P-value and log2 fold change values of less than 0.05 and 1.5 respectively. Further, network analysis and GO studies showed that differential expression of two hub genes namely ELANE and LTF which could induce higher levels of pro-inflammatory cytokines in the lungs. We are certain that differential expression of ELANE and LTF results in an excessive inflammatory reaction known as the cytokine storm and ultimately leading to death. Therefore, targeting these key drivers of cytokine storm genes appears to be the potential therapeutic targets for combating the Severe Acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection ultimately resulting in reduced mortality. Indeed, this predictive view may open new insights for designing an immune intervention for COVID-19 in the near future resulting in the mitigation of mortality rate.

关键词： SARS-CoV-2 microarray data Hub gene identification Cytokine storm STRING database

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