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A hybrid multi-objective whale optimization algorithm for analyzing microarray data based on Apache Spark

PEERJ COMPUTER SCIENCE 2021年 7卷 e416页

作者： AbdelAziz, Amr Mohamed Soliman, Taysir Ghany, Kareem Kamal A. Sewisy, Adel Beni Suef Univ Fac Comp & Artificial Intelligence Bani Suwayf Egypt Assiut Univ Fac Comp & Informat Assiut Egypt Saudi Elect Univ Coll Comp & Informat Riyadh Saudi Arabia

A microarray is a revolutionary tool that generates vast volumes of data that describe the expression profiles of genes under investigation that can be qualified as Big data. Hadoop and Spark are efficient frameworks, developed to store and analyze Big data. Analyzing microarray data helps researchers to identify correlated genes. Clustering has been successfully applied to analyze microarray data by grouping genes with similar expression profiles into clusters. The complex nature of microarray data obligated clustering methods to employ multiple evaluation functions to ensure obtaining solutions with high quality. This transformed the clustering problem into a Multi-Objective Problem (MOP). A new and efficient hybrid Multi-Objective Whale Optimization Algorithm with Tabu Search (MOWOATS) was proposed to solve MOPs. In this article, MOWOATS is proposed to analyze massive microarray datasets. Three evaluation functions have been developed to ensure an effective assessment of solutions. MOWOATS has been adapted to run in parallel using Spark over Hadoop computing clusters. The quality of the generated solutions was evaluated based on different indices, such as Silhouette and Davies-Bouldin indices. The obtained clusters were very similar to the original classes. Regarding the scalability, the running time was inversely proportional to the number of computing nodes.

关键词： data mining Swarm intelligence Distributed and parallel computation microarray data

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Application of active learning in DNA microarray data for cancerous gene identification

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 177卷 114914-114914页

作者： Begum, Shemim Sarkar, Ram Chakraborty, Debasis Sen, Sagnik Maulik, Ujjwal Govt Colege Engn & Text Technol Comp Sci & Engn Dept Murshidabad W Bengal India Jadavpur Univ Comp Sci & Engn Dept Kolkata W Bengal India Asansol Engn Coll Comp Sci & Engn Dept Asansol W Bengal India

microarray technology has an important role in evaluating gene expression data with unique patterns into existence. In gene-expression based experiments, the expression level of the gene is constantly monitored in order to classify a tissue sample. In microarray technology, the expressions of the genes are altered with respect to pathogenes. The altered expression values can be identified by analyzing the genes of the tissue/cell that are affected along with the tissues/cells that are unaffected are termed as biomarkers. In the current paper, we have developed an Active Learning (AL) model by using Support Vector Machine (SVM) in association with featureselection (FS) algorithm;called Symmetrical Uncertainty (SU) for the prediction of cancer. The effectiveness of the proposed AL and SU combination is manifested and the biomarkers or cancerous genes identified by the proposed method on four gene-expression data sets are reported. In addition, the biological significance tests are performed for the cancer biomarkers obtained from the data sets.

关键词： Active learning Biomarker Cancer prediction microarray data Symmetrical uncertainty SVM

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An efficient multivariate feature ranking method for gene selection in high-dimensional microarray data

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 166卷 113971-113971页

作者： Lee, Junghye Choi, In Young Jun, Chi-Hyuck Ulsan Natl Inst Sci & Technol UNIST Dept Ind Engn 50 UNIST Gil Ulsan South Korea Catholic Univ Korea Dept Med Informat Coll Med 222 Banpo Daero Seoul South Korea Pohang Univ Sci & Technol POSTECH Dept Ind & Management Engn 77 Cheongam Ro Pohang South Korea

Classification of microarray data plays a significant role in the diagnosis and prediction of cancer. However, its high-dimensionality (>tens of thousands) compared to the number of observations (data, univariate feature ranking methods are widely-used in gene selection. However, most of them are not that accurate because they only consider the relevance of features to the target without considering the redundancy among features. In this study, we propose a novel multivariate feature ranking method to improve the quality of gene selection and ultimately to improve the accuracy of microarray data classification. The method can be efficiently applied to high-dimensional microarray data. We embedded the formal definition of relevance into a Markov blanket (MB) to create a new feature ranking method. Using a few microarray datasets, we demonstrated the practicability of MB-based feature ranking having high accuracy and good efficiency. The method outperformed commonly-used univariate ranking methods and also yielded the better result even compared with the other multivariate feature ranking method due to the advantage of data efficiency.

关键词： Multivariate feature selection Gene selection High-dimensional data microarray data Classification Multiclass Mixed-type data Markov blanket Ranking

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Theoretical and empirical analysis of filter ranking methods: Experimental study on benchmark DNA microarray data

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 169卷 114485-114485页

作者： Ghosh, Kushal Kanti Begum, Shemim Sardar, Aritra Adhikary, Sukdev Ghosh, Manosij Kumar, Munish Sarkar, Ram Jadavpur Univ Dept Comp Sci & Engn Kolkata India Govt Coll Engn & Text Technol Dept Comp Sci & Engn Berhampur India Maharaja Ranjit Singh Punjab Tech Univ Dept Computat Sci Bathinda India

DNA microarray experiments generate thousands of gene expression values that provide information about the state of cells and tissues. Though these expressive values are useful in disease classification, however, only a few genes contribute towards this classification. In this context, usage of feature selection algorithms can be beneficial, as the main goal of feature selection algorithms is to identify the relevant features (here genes) efficiently. In the recent past, many feature selection algorithms have been proposed in the literature that measure the relevancy and redundancy of the features using various evaluation criteria. An important type of feature selection techniques is feature ranking, which does not use any learning algorithm, rather assigns an important value or weight to a feature. In this paper, we provide an extensive study on 10 popularly used filter ranking methods. We have applied the methods to 10 microarray datasets (both binary class and multi-class) and tested the accuracies using three well-known classifiers namely Multi-layer Perceptron (MLP), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). We have conducted a wide variety of tests to assess the strength and weakness of various filter methods. This vast study provides a comparison amongst different filter methods helping researchers make an informed choice about selecting an appropriate filter method for their work. Three categories of filtering methods are tested, namely, Entropy based, Similarity based and Statistics based. The experiments show that out of all the methods Mutual Information (MI) gives the best results (also best among Entropy based methods). In the category of Similarity based methods ReliefF performs best and Chi-square performs best in the category of Statistics based methods. In case of bi-class datasets, Chi-square would be the better choice, while for multi-class datasets, MI gives better results.

关键词： Feature selection Filter method microarray data Cancer classification Gene expression

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Stable and Accurate Feature Selection from microarray data with Ensembled Fast Correlation Based Filter

Stable and Accurate Feature Selection from Microarray Data w...

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IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)

作者： Wang, Aiguo Liu, Huancheng Liu, Jinjun Ding, Huitong Yang, Jing Chen, Guilin Foshan Univ Sch Elect Informat Engn Foshan Peoples R China Chuzhou Univ Sch Comp & Informat Engn Chuzhou Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Peoples R China

ISBN: (纸本)9781728162157

Feature selection has been playing an important role in analyzing the high-dimension and low-sample-size gene expression profiles towards high classification performance of diseases and deep understanding of the underlying biological mechanisms. Besides classification performance, the stability of selected features is another non-ignorable factor in evaluating a feature selector, since stable feature selection results enhance the confidence of selected features for true biomarker discovery and further biological validation. In this study, we propose a novel feature selection method under the ensemble learning framework. Specifically, we take Fast Correlation Based Filter as the base feature selector to analyze subsamples of microarray data. We then present several aggregation methods to combine multiple feature subsets. Finally, two stability measures are used to quantify the robustness of feature selectors to data variations. Our comparative empirical study on publicly available datasets demonstrates the superiority of the proposed methods over its competitors in obtaining high stability scores and classification accuracy.

关键词： microarray data feature selection ensemble learning stability classification

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PD_BiBIM: Biclustering-based biomarker identification in ESCC microarray data

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JOURNAL OF BIOSCIENCES 2021年第3期46卷 56-56页

作者： Patowary, Pallabi Bhattacharyya, Dhruba K. Tezpur Univ Dept Comp Sci & Engn Tezpur Assam India

To promote diligent analysis of the progression of a disease, it is important to identify interesting biomarkers for the disease. Biclustering has already been established as an effective technique to help identify such biomarkers of high biological significance. Although in the recent past, a good number of biclustering techniques have been introduced, most of them fail to perform consistently across multiple domains or datasets. To choose a single biclustering technique that can help the accomplishment of such a critical task for multiple diseases with high precision is extremely difficult. Hence, in this study, we considered several biclustering techniques and accepted those techniques and their results which are found significant from enrichment perspective for subsequent analysis. Based on biclustering results, we constructed biological networks and carried out a topological, pathway and causal analysis on the modules extracted from the networks. Our multi-objective study enabled us to identify several biomarkers for esophageal squamous cell carcinoma (ESCC) such as IFNGR1, CLIC1, CDK4, and COPS5, after applying a ranking scheme.

关键词： Co-expression network ESCC biomarker ESCC disease microarray data primary gene secondary gene

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microarray data and pathway analyses of peripheral blood mononuclear cells from healthy subjects after a three weeks grape-rich diet

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data IN BRIEF 2020年 29卷 105278页

作者： Milella, Rosa Anna Gasparro, Marica Alagna, Fiammetta Cardone, Maria Francesca Rotunno, Silvia Ammollo, Concetta Tiziana Semeraro, Fabrizio Tullo, Apollonia Marzano, Flaviana Catalano, Domenico Antonacci, Donato Colucci, Mario D'Elia, Domenica Council Agr Res & Econ Res Ctr Viticulture & Enol I-70010 Bari Italy Univ Bari Aldo Moro Dept Biomed Sci & Human Oncol I-70124 Bari Italy CNR Inst Biomembranes Bioenerget & Mol Biotechnol I-70126 Bari Italy CNR Inst Biomed Technol I-70126 Bari Italy ENEA Italian Natl Agcy New Technol Energy & Susta Trisaia Res Ctr I-75026 Rotondella Matera Italy Univ Molise Dept Biosci & Terr I-86090 Pesche Italy

Using Human Gene Expression microarrays (Agilent) technologies, we investigated changes of the level of gene expression in peripheral blood mononuclear cells of healthy subjects after 21 days of fresh table grape-rich diet and after an additional 28-day washout. Several hundreds of genes were differentially expressed after grape intake or after washout. The functional analysis of these genes detected significant changes in key processes such as inflammation and immunity, thrombosis, DNA and protein repair, autophagy and mitochondrial biogenesis. Moreover, fresh grape intake was found to influence the expression of many long non-coding RNA genes. The data can be valuable for researchers interested in nutrigenetics and nutrigenomics studies and are related to the research article "Gene expression signature induced by grape intake in healthy subjects reveals wide-spread beneficial effects on PBMCs" [1]. (C) 2020 The Authors. Published by Elsevier Inc.

关键词： Peripheral blood mononuclear cells microarray data Pathway analyses Biological processes Grape intake Diets Human health Health benefits

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Genetic algorithm based cancerous gene identification from microarray data using ensemble of filter methods

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2019年第1期57卷 159-176页

作者： Ghosh, Manosij Adhikary, Sukdev Ghosh, Kushal Kanti Sardar, Aritra Begum, Shemim Sarkar, Ram Jadavpur Univ Dept Comp Sci & Engn Kolkata India Govt Coll Engn & Text Technol Dept Comp Sci & Engn Berhampur W Bengal India

microarray datasets play a crucial role in cancer detection. But the high dimension of these datasets makes the classification challenging due to the presence of many irrelevant and redundant features. Hence, feature selection becomes irreplaceable in this field because of its ability to remove the unrequired features from the system. As the task of selecting the optimal number of features is an NP-hard problem, hence, some meta-heuristic search technique helps to cope up with this problem. In this paper, we propose a 2-stage model for feature selection in microarray datasets. The ranking of the genes for the different filter methods are quite diverse and effectiveness of rankings is datasets dependent. First, we develop an ensemble of filter methods by considering the union and intersection of the top-n features of ReliefF, chi-square, and symmetrical uncertainty. This ensemble allows us to combine all the information of the three rankings together in a subset. In the next stage, we use genetic algorithm (GA) on the union and intersection to get the fine-tuned results, and union performs better than the latter. Our model has been shown to be classifier independent through the use of three classifiersmulti-layer perceptron (MLP), support vector machine (SVM), and K-nearest neighbor (K-NN). We have tested our model on five cancer datasetscolon, lung, leukemia, SRBCT, and prostate. Experimental results illustrate the superiority of our model in comparison to state-of-the-art methods.

关键词： Wrapper method Filter method Ensemble microarray data Cancer detection

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Partial maximum correlation information: A new feature selection method for microarray data classification

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NEUROCOMPUTING 2019年 323卷 231-243页

作者： Yuan, Mingshun Yang, Zijiang Ji, Guoli Xiamen Univ Dept Automat Xiamen 361005 Fujian Peoples R China York Univ Sch Informat Technol Toronto ON M3J 1P3 Canada Xiamen Univ Innovat Ctr Cell Signaling Network Xiamen 361102 Fujian Peoples R China

Feature (gene) selection of microarray data is a very important and challenging task. This paper proposes a new feature selection method, partial maximum correlation information (PMCI), for microarray data classification. The proposed method extracts several orthogonal components from the feature space to evaluate the importance of each feature. In PMCI method, the component extraction is based on the correlation between feature space and class coding space. Meanwhile, this paper also provides a new generic class-encoding scheme for multi-class problems. This type of encoding scheme can ensure the smooth implementation for PMCI and has a clear geometric meaning. To validate the performance of the proposed method, this paper selects six state-of-the-art algorithms for comparison based on ten widely used microarray benchmark datasets for experiment. The experimental results show that PMCI is an effective feature selection method with good performance and lower time complexity. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Feature selection microarray data SIMPIS Partial maximum correlation information (PMCI)

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A novel ECOC algorithm for multiclass microarray data classification based on data complexity analysis

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PATTERN RECOGNITION 2019年 90卷 346-362页

作者： Sun, Mengxin Liu, Kunhong Wu, Qingqiang Hong, Qingqi Wang, Beizhan Zhang, Haiying Xiamen Univ Software Sch Xiamen 361005 Fujian Peoples R China

Nowadays, a lot of new classification and clustering techniques have been proposed for microarray data analysis. However, the multiclass microarray data classification is still regarded as a tough task because of the small sample size problem and the class imbalance problem. In this paper, we propose a novel error correcting output code (ECOC) algorithm for the classification of multiclass microarray data based on the data complexity (DC) theory. In this algorithm, an ECOC coding matrix is generated based on a hierarchical partition of the class space with the aim of Minimizing data Complexity (named as ECOC-MDC). As the partition process can be mapped as a binary tree, a compact ensemble with high discrimination power is produced. The performance of ECOC-MDC is compared with some state-of-art ECOC algorithms on six multiclass microarray data sets, and it is found that the proposed algorithm can obtain better results in most cases. The correlation between DC measures and the dichotomizers' performances is checked, and the observations confirm that high complexity in data usually leads to high error rates of the connected dichotomizers. But the error correcting mechanism in the ECOC framework can effectively improve our algorithm's generalization ability. In short, ECOC-MDC can produce a compact ensemble system with high error correction capability through the application of diverse DC measures. Our Matlab code is available at: ***/MLDMXM2017/ECOC-MDC. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Error correcting output codes (ECOC) data complexity microarray data Multiclass

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