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Madhumita M, Paul S. A review on methods for predicting miRNA–mRNA regulatory modules. J Integr Bioinform 2022; 19:jib-2020-0048. [PMID: 35357793 PMCID: PMC9521823 DOI: 10.1515/jib-2020-0048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 01/01/2022] [Indexed: 11/15/2022] Open
Abstract
Identification of complex interactions between miRNAs and mRNAs in a regulatory network helps better understand the underlying biological processes. Previously, identification of these interactions was based on sequence-based predicted target binding information. With the advancement in high-throughput omics technologies, miRNA and mRNA expression for the same set of samples are available. This helps develop more efficient and flexible approaches that work by integrating miRNA and mRNA expression profiles with target binding information. Since these integrative approaches of miRNA–mRNA regulatory modules (MRMs) detection is sufficiently able to capture the minute biological details, 26 such algorithms/methods/tools for MRMs identification are comprehensively reviewed in this article. The study covers the significant features underlying every method. Therefore, the methods are classified into eight groups based on mathematical approaches to understand their working and suitability for one’s study. An algorithm could be selected based on the available information with the users and the biological question under investigation.
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Affiliation(s)
- Madhumita Madhumita
- Department of Bioscience and Bioengineering, Indian Institute of Technology, Jodhpur342037, Rajasthan, India
| | - Sushmita Paul
- Department of Bioscience and Bioengineering, Indian Institute of Technology, Jodhpur342037, Rajasthan, India
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Paul S, Madhumita. Pattern Recognition Algorithms for Multi-Omics Data Analysis. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11538-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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Paul S. RFCM 3: Computational Method for Identification of miRNA-mRNA Regulatory Modules in Cervical Cancer. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1729-1740. [PMID: 30990434 DOI: 10.1109/tcbb.2019.2910851] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Cervical cancer is a leading severe malignancy throughout the world. Molecular processes and biomarkers leading to tumor progression in cervical cancer are either unknown or only partially understood. An increasing number of studies have shown that microRNAs play an important role in tumorigenesis so understanding the regulatory mechanism of miRNAs in gene-regulatory network will help elucidate the complex biological processes that occur during malignancy. Functional genomics data provides opportunities to study the aberrant microRNA-messenger RNA (miRNA-mRNA) interaction. Identification of miRNA-mRNA regulatory modules will aid deciphering aberrant transcriptional regulatory network in cervical cancer but is computationally challenging. In this regard, an algorithm, termed as relevant and functionally consistent miRNA-mRNA modules (RFCM3), is proposed. It integrates miRNA and mRNA expression data of cervical cancer for identification of potential miRNA-mRNA modules. It selects set of miRNA-mRNA modules by maximizing relation of mRNAs with miRNA and functional similarity between selected mRNAs. Later, using the knowledge of the miRNA-miRNA synergistic network different modules are fused and finally a set of modules are generated containing several miRNAs as well as mRNAs. This type of module explains the underlying biological pathways containing multiple miRNAs and mRNAs. The effectiveness of the proposed approach over other existing methods has been demonstrated on a miRNA and mRNA expression data of cervical cancer with respect to enrichment analyses and other standard metrices. The prognostic value of the genes in a module with respect to cervical cancer is also demonstrated. The approach was found to generate more robust, integrated, and functionally enriched miRNA-mRNA modules in cervical cancer.
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Geeitha S, Thangamani M. Incorporating EBO-HSIC with SVM for Gene Selection Associated with Cervical Cancer Classification. J Med Syst 2018; 42:225. [DOI: 10.1007/s10916-018-1092-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 10/01/2018] [Indexed: 12/31/2022]
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Sheena Y. Asymptotic expansion of the risk of maximum likelihood estimator with respect to α-divergence. COMMUN STAT-THEOR M 2017. [DOI: 10.1080/03610926.2017.1380828] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Yo Sheena
- Faculty of Economics and Law, Shinshu University, Nagano, Japan
- Faculty of Data Science, Shiga University, Shiga, Japan
- The Institute of Statistical Mathematics, Tokyo, Japan
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Maji P, Shah E. Significance and Functional Similarity for Identification of Disease Genes. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:1419-1433. [PMID: 28113633 DOI: 10.1109/tcbb.2016.2598163] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
One of the most significant research issues in functional genomics is insilico identification of disease related genes. In this regard, the paper presents a new gene selection algorithm, termed as SiFS, for identification of disease genes. It integrates the information obtained from interaction network of proteins and gene expression profiles. The proposed SiFS algorithm culls out a subset of genes from microarray data as disease genes by maximizing both significance and functional similarity of the selected gene subset. Based on the gene expression profiles, the significance of a gene with respect to another gene is computed using mutual information. On the other hand, a new measure of similarity is introduced to compute the functional similarity between two genes. Information derived from the protein-protein interaction network forms the basis of the proposed SiFS algorithm. The performance of the proposed gene selection algorithm and new similarity measure, is compared with that of other related methods and similarity measures, using several cancer microarray data sets.
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Nidheesh N, Abdul Nazeer KA, Ameer PM. An enhanced deterministic K-Means clustering algorithm for cancer subtype prediction from gene expression data. Comput Biol Med 2017; 91:213-221. [PMID: 29100115 DOI: 10.1016/j.compbiomed.2017.10.014] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Revised: 10/13/2017] [Accepted: 10/14/2017] [Indexed: 01/21/2023]
Abstract
BACKGROUND Clustering algorithms with steps involving randomness usually give different results on different executions for the same dataset. This non-deterministic nature of algorithms such as the K-Means clustering algorithm limits their applicability in areas such as cancer subtype prediction using gene expression data. It is hard to sensibly compare the results of such algorithms with those of other algorithms. The non-deterministic nature of K-Means is due to its random selection of data points as initial centroids. METHOD We propose an improved, density based version of K-Means, which involves a novel and systematic method for selecting initial centroids. The key idea of the algorithm is to select data points which belong to dense regions and which are adequately separated in feature space as the initial centroids. RESULTS We compared the proposed algorithm to a set of eleven widely used single clustering algorithms and a prominent ensemble clustering algorithm which is being used for cancer data classification, based on the performances on a set of datasets comprising ten cancer gene expression datasets. The proposed algorithm has shown better overall performance than the others. CONCLUSION There is a pressing need in the Biomedical domain for simple, easy-to-use and more accurate Machine Learning tools for cancer subtype prediction. The proposed algorithm is simple, easy-to-use and gives stable results. Moreover, it provides comparatively better predictions of cancer subtypes from gene expression data.
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Affiliation(s)
- N Nidheesh
- Department of Electronics and Communication Engineering, National Institute of Technology Calicut, Kerala 673601, India.
| | - K A Abdul Nazeer
- Department of Computer Science and Engineering, National Institute of Technology Calicut, Kerala 673601, India
| | - P M Ameer
- Department of Electronics and Communication Engineering, National Institute of Technology Calicut, Kerala 673601, India
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Gangeh MJ, Zarkoob H, Ghodsi A. Fast and Scalable Feature Selection for Gene Expression Data Using Hilbert-Schmidt Independence Criterion. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:167-181. [PMID: 28182548 DOI: 10.1109/tcbb.2016.2631164] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
GOAL In computational biology, selecting a small subset of informative genes from microarray data continues to be a challenge due to the presence of thousands of genes. This paper aims at quantifying the dependence between gene expression data and the response variables and to identifying a subset of the most informative genes using a fast and scalable multivariate algorithm. METHODS A novel algorithm for feature selection from gene expression data was developed. The algorithm was based on the Hilbert-Schmidt independence criterion (HSIC), and was partly motivated by singular value decomposition (SVD). RESULTS The algorithm is computationally fast and scalable to large datasets. Moreover, it can be applied to problems with any type of response variables including, biclass, multiclass, and continuous response variables. The performance of the proposed algorithm in terms of accuracy, stability of the selected genes, speed, and scalability was evaluated using both synthetic and real-world datasets. The simulation results demonstrated that the proposed algorithm effectively and efficiently extracted stable genes with high predictive capability, in particular for datasets with multiclass response variables. CONCLUSION/SIGNIFICANCE The proposed method does not require the whole microarray dataset to be stored in memory, and thus can easily be scaled to large datasets. This capability is an important attribute in big data analytics, where data can be large and massively distributed.
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Maji P, Mandal A. Multimodal Omics Data Integration Using Max Relevance--Max Significance Criterion. IEEE Trans Biomed Eng 2016; 64:1841-1851. [PMID: 27834637 DOI: 10.1109/tbme.2016.2624823] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE This paper presents a novel supervised regularized canonical correlation analysis, termed as CuRSaR, to extract relevant and significant features from multimodal high dimensional omics datasets. METHODS The proposed method extracts a new set of features from two multidimensional datasets by maximizing the relevance of extracted features with respect to sample categories and significance among them. It integrates judiciously the merits of regularized canonical correlation analysis (RCCA) and rough hypercuboid approach. An analytical formulation, based on spectral decomposition, is introduced to establish the relation between canonical correlation analysis (CCA) and RCCA. The concept of hypercuboid equivalence partition matrix of rough hypercuboid is used to compute both relevance and significance of a feature. SIGNIFICANCE The analytical formulation makes the computational complexity of the proposed algorithm significantly lower than existing methods. The equivalence partition matrix offers an efficient way to find optimum regularization parameters employed in CCA. RESULTS The superiority of the proposed algorithm over other existing methods, in terms of computational complexity and classification accuracy, is established extensively on real life data.
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Wang L, Wang Y, Chang Q. Feature selection methods for big data bioinformatics: A survey from the search perspective. Methods 2016; 111:21-31. [PMID: 27592382 DOI: 10.1016/j.ymeth.2016.08.014] [Citation(s) in RCA: 110] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2016] [Revised: 08/25/2016] [Accepted: 08/30/2016] [Indexed: 11/26/2022] Open
Abstract
This paper surveys main principles of feature selection and their recent applications in big data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and embedded approaches to feature selection, we formulate feature selection as a combinatorial optimization or search problem and categorize feature selection methods into exhaustive search, heuristic search, and hybrid methods, where heuristic search methods may further be categorized into those with or without data-distilled feature ranking measures.
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Affiliation(s)
- Lipo Wang
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.
| | - Yaoli Wang
- College of Information Engineering, Taiyuan University of Technology, Taiyuan, China.
| | - Qing Chang
- College of Information Engineering, Taiyuan University of Technology, Taiyuan, China.
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GaneshKumar P, Rani C, Devaraj D, Victoire TAA. Hybrid Ant Bee Algorithm for Fuzzy Expert System Based Sample Classification. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2014; 11:347-360. [PMID: 26355782 DOI: 10.1109/tcbb.2014.2307325] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Accuracy maximization and complexity minimization are the two main goals of a fuzzy expert system based microarray data classification. Our previous Genetic Swarm Algorithm (GSA) approach has improved the classification accuracy of the fuzzy expert system at the cost of their interpretability. The if-then rules produced by the GSA are lengthy and complex which is difficult for the physician to understand. To address this interpretability-accuracy tradeoff, the rule set is represented using integer numbers and the task of rule generation is treated as a combinatorial optimization task. Ant colony optimization (ACO) with local and global pheromone updations are applied to find out the fuzzy partition based on the gene expression values for generating simpler rule set. In order to address the formless and continuous expression values of a gene, this paper employs artificial bee colony (ABC) algorithm to evolve the points of membership function. Mutual Information is used for idenfication of informative genes. The performance of the proposed hybrid Ant Bee Algorithm (ABA) is evaluated using six gene expression data sets. From the simulation study, it is found that the proposed approach generated an accurate fuzzy system with highly interpretable and compact rules for all the data sets when compared with other approaches.
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Paul S, Maji P. Rough sets for in silico identification of differentially expressed miRNAs. Int J Nanomedicine 2013; 8 Suppl 1:63-74. [PMID: 24098080 PMCID: PMC3790281 DOI: 10.2147/ijn.s40739] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
The microRNAs, also known as miRNAs, are the class of small noncoding RNAs. They repress the expression of a gene posttranscriptionally. In effect, they regulate expression of a gene or protein. It has been observed that they play an important role in various cellular processes and thus help in carrying out normal functioning of a cell. However, dysregulation of miRNAs is found to be a major cause of a disease. Various studies have also shown the role of miRNAs in cancer and the utility of miRNAs for the diagnosis of cancer and other diseases. Unlike with mRNAs, a modest number of miRNAs might be sufficient to classify human cancers. However, the absence of a robust method to identify differentially expressed miRNAs makes this an open problem. In this regard, this paper presents a novel approach for in silico identification of differentially expressed miRNAs from microarray expression data sets. It integrates judiciously the theory of rough sets and merit of the so-called B.632+ bootstrap error estimate. While rough sets select relevant and significant miRNAs from expression data, the B.632+ error rate minimizes the variability and bias of the derived results. The effectiveness of the proposed approach, along with a comparison with other related approaches, is demonstrated on several miRNA microarray expression data sets, using the support vector machine.
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Affiliation(s)
- Sushmita Paul
- Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India
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Maji P, Garai P. On fuzzy-rough attribute selection: Criteria of Max-Dependency, Max-Relevance, Min-Redundancy, and Max-Significance. Appl Soft Comput 2013. [DOI: 10.1016/j.asoc.2012.09.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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15
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Discrimination of malignant neutrophils of chronic myelogenous leukemia from normal neutrophils by support vector machine. Comput Biol Med 2013; 43:1192-5. [DOI: 10.1016/j.compbiomed.2013.06.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2013] [Revised: 06/06/2013] [Accepted: 06/09/2013] [Indexed: 11/20/2022]
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Maji P, Das C. Relevant and Significant Supervised Gene Clusters for Microarray Cancer Classification. IEEE Trans Nanobioscience 2012; 11:161-8. [DOI: 10.1109/tnb.2012.2193590] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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PIAO HAIYAN. GENE EXPRESSION DATA ANALYSIS USING PSEUDO STANDARD DEVIATION MINIMIZATION FEATURE FUSION METHOD FOR CANCER DIAGNOSIS. J MECH MED BIOL 2012. [DOI: 10.1142/s021951941200496x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Over the past years applications of fusion technique have been growing rapidly. However, very few applications of the technique to microarray data have been reported. In this paper, we propose a new fusion method based on pseudo standard deviation minimization (PSDM) for the feature selection of microarray. This new method provides a more accurate set of features. Therefore the classification can be performed and functional meaning from the features can also be revealed. The new method is actually obtained through a combination of two different feature selection methods (FSMs). It is shown that it can explore nonperfect correlation between gene expression profile and cancer classes or feature detection algorithms. To evaluate its effectiveness, it is tested on lymphoma and leukemia microarray expression datasets and then compared with the existing methods. Self-organizing map (SOM) is used for feature classification. It can be seen through the comparison that the classification accuracy of the new fusion method is at least 2% ~ 3% higher than others.
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Affiliation(s)
- HAIYAN PIAO
- School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore
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GANESH KUMAR P, ARULDOSS ALBERT VICTOIRE T. MULTISTAGE MUTUAL INFORMATION FOR INFORMATIVE GENE SELECTION. J BIOL SYST 2012. [DOI: 10.1142/s0218339011004160] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
An important issue in the design of gene selection algorithm for microarray data analysis is the formation of suitable criterion function for measuring the relevance between different gene expressions. Mutual information (MI) is a widely used criterion function but it calculates the relevance on the entire samples only once which cannot exactly identify the informative genes. This paper proposes a novel idea of computing MI in stages. The proposed multistage mutual information (MSMI) computes MI, initially using all the samples and based on the classification performance produced by artificial neural network (ANN), MI is repeatedly calculated using only the unclassified samples until there is no improvement in the classification accuracy. The performance of the proposed approach is evaluated using ten gene expression data sets. Simulation result shows that the proposed approach helps to improve the discriminate power of the genes with regard to the target disease of a microarray sample. Statistical analysis of the test result shows that the proposed method selects highly informative genes and produces comparable classification accuracy than the other approaches reported in the literature.
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Affiliation(s)
- P. GANESH KUMAR
- Department of Information Technology, Anna University of Technology, Coimbatore, India
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Moutselos K, Maglogiannis I, Chatziioannou A. GOrevenge: a novel generic reverse engineering method for the identification of critical molecular players, through the use of ontologies. IEEE Trans Biomed Eng 2011; 58:3522-7. [PMID: 21846603 DOI: 10.1109/tbme.2011.2164794] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The ever-increasing use of ontologies in modern biological analysis and interpretation facilitates the understanding of the cellular procedures, their hierarchical organization, and their potential interactions at a system's level. Currently, the gene ontology serves as a paradigm, where through the annotation of whole genomes of certain organisms, genes subsets selected, either from high-throughput experiments or with an established pivotal role regarding the probed disease, can act as a starting point for the exploration of their underlying functional interconnections. This may also aid the elucidation of hidden regulatory mechanisms among genes. Reverse engineering the functional relevance of genes to specific cellular pathways and vice versa, through the exploitation of the inner structure of the ontological vocabularies, may help impart insight regarding the identification and prioritization of the critical role of specific genes. The proposed graph-theoretical method is showcased in a pancreatic cancer and a T-cell acute lymphoblastic leukemia gene set, incorporating edge and Resnik semantic similarity metrics, and systematically evaluated regarding its performance.
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Affiliation(s)
- Konstantinos Moutselos
- Department of Computer Science and Biomedical Informatics, University of Central Greece, Lamia 35100, Greece.
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Maji P, Paul S. Rough set based maximum relevance-maximum significance criterion and Gene selection from microarray data. Int J Approx Reason 2011. [DOI: 10.1016/j.ijar.2010.09.006] [Citation(s) in RCA: 104] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Maji P, Paul S. Rough Sets for Selection of Molecular Descriptors to Predict Biological Activity of Molecules. ACTA ACUST UNITED AC 2010. [DOI: 10.1109/tsmcc.2010.2047943] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Maji P, Pal SK. Fuzzy-rough sets for information measures and selection of relevant genes from microarray data. ACTA ACUST UNITED AC 2009; 40:741-52. [PMID: 19887323 DOI: 10.1109/tsmcb.2009.2028433] [Citation(s) in RCA: 69] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Several information measures such as entropy, mutual information, and f-information have been shown to be successful for selecting a set of relevant and nonredundant genes from a high-dimensional microarray data set. However, for continuous gene expression values, it is very difficult to find the true density functions and to perform the integrations required to compute different information measures. In this regard, the concept of the fuzzy equivalence partition matrix is presented to approximate the true marginal and joint distributions of continuous gene expression values. The fuzzy equivalence partition matrix is based on the theory of fuzzy-rough sets, where each row of the matrix represents a fuzzy equivalence partition that can automatically be derived from the given expression values. The performance of the proposed approach is compared with that of existing approaches using the class separability index and the predictive accuracy of the support vector machine. An important finding, however, is that the proposed approach is shown to be effective for selecting relevant and nonredundant continuous-valued genes from microarray data.
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Affiliation(s)
- Pradipta Maji
- Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700108, India.
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