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Yang Y, Wu R, Chen D, Fei C, Li D, Yang Y. An improved Fourier Ptychography algorithm for ultrasonic array imaging. Comput Biol Med 2023; 163:107157. [PMID: 37352636 DOI: 10.1016/j.compbiomed.2023.107157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 06/03/2023] [Accepted: 06/07/2023] [Indexed: 06/25/2023]
Abstract
Inspired by the optical imaging algorithm, the Fourier Ptychography (FP) algorithm is adopted to improve the resolution of ultrasonic array imaging. In the FP algorithm, the steady-state spectrum is utilized to recover the high-resolution ultrasonic images. Meanwhile, the parameters of FP algorithm are empirical, which can affect the imaging quality of ultrasonic array. Then the particle swarm optimization (PSO) algorithm is used to optimize the parameters of FP algorithm to further improve the imaging quality of ultrasonic array. The tungsten imaging experiments and pig eye imaging experiments are conducted to demonstrate the feasibility and effectiveness of the developed algorithm. In addition, the proposed algorithm and the coherent wave superposition (CWS) algorithm are both based on single plane wave (SPW) algorithms and they are then compared. The results show that the CWS algorithm and FP algorithm have good longitudinal and lateral resolutions, respectively. The particle swarm optimization-based FP (PSOFP) imaging algorithm has both excellent lateral and longitudinal resolutions. The average lateral resolution of PSOFP imaging algorithm is improved by 34.47% compared with CWS imaging algorithm in the tungsten wires experiments, and the lateral boundary structure width of the lens is improved by 49.48% in the pig eye experiments. The proposed algorithm can effectively improve the ultrasonic imaging quality for medical application.
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Affiliation(s)
- Yaoyao Yang
- School of Microelectronics, Xidian University, Xi'an, 710071, China
| | - Runcong Wu
- School of Microelectronics, Xidian University, Xi'an, 710071, China
| | - Dongdong Chen
- School of Microelectronics, Xidian University, Xi'an, 710071, China.
| | - Chunlong Fei
- School of Microelectronics, Xidian University, Xi'an, 710071, China; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Di Li
- School of Microelectronics, Xidian University, Xi'an, 710071, China
| | - Yintang Yang
- School of Microelectronics, Xidian University, Xi'an, 710071, China
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An ensemble framework for microarray data classification based on feature subspace partitioning. Comput Biol Med 2022; 148:105820. [PMID: 35872409 DOI: 10.1016/j.compbiomed.2022.105820] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 06/05/2022] [Accepted: 07/03/2022] [Indexed: 12/14/2022]
Abstract
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.
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Liu WB, Liang SN, Qin XW. A novel dimension reduction algorithm based on weighted kernel principal analysis for gene expression data. PLoS One 2021; 16:e0258326. [PMID: 34644329 PMCID: PMC8513872 DOI: 10.1371/journal.pone.0258326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 09/26/2021] [Indexed: 11/19/2022] Open
Abstract
Gene expression data has the characteristics of high dimensionality and a small sample size and contains a large number of redundant genes unrelated to a disease. The direct application of machine learning to classify this type of data will not only incur a great time cost but will also sometimes fail to improved classification performance. To counter this problem, this paper proposes a dimension-reduction algorithm based on weighted kernel principal component analysis (WKPCA), constructs kernel function weights according to kernel matrix eigenvalues, and combines multiple kernel functions to reduce the feature dimensions. To further improve the dimensional reduction efficiency of WKPCA, t-class kernel functions are constructed, and corresponding theoretical proofs are given. Moreover, the cumulative optimal performance rate is constructed to measure the overall performance of WKPCA combined with machine learning algorithms. Naive Bayes, K-nearest neighbour, random forest, iterative random forest and support vector machine approaches are used in classifiers to analyse 6 real gene expression dataset. Compared with the all-variable model, linear principal component dimension reduction and single kernel function dimension reduction, the results show that the classification performance of the 5 machine learning methods mentioned above can be improved effectively by WKPCA dimension reduction.
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Affiliation(s)
- Wen Bo Liu
- School of Mathematics and Statistics, Qiannan Normal University for Nationalities, Duyun, Guizhou, China
- Key Laboratory of Complex Systems and Intelligent Computing, Qiannan Normal College of Nationalities, Duyun, Guizhou, China
| | - Sheng Nan Liang
- School of Mathematics and Statistics, Qiannan Normal University for Nationalities, Duyun, Guizhou, China
- Key Laboratory of Complex Systems and Intelligent Computing, Qiannan Normal College of Nationalities, Duyun, Guizhou, China
| | - Xi Wen Qin
- School of Mathematics and Statistics, Changchun University of Technology, Changchun, Jilin, China
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Alharthi AM, Lee MH, Algamal ZY. Gene selection and classification of microarray gene expression data based on a new adaptive L1-norm elastic net penalty. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100622] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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Patil S, Naik G, Pai R, Gad R. Stacked Autoencoder for classification of glioma grade III and grade IV. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.07.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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6
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Documenting and predicting topic changes in Computers in Biology and Medicine: A bibliometric keyword analysis from 1990 to 2017. INFORMATICS IN MEDICINE UNLOCKED 2018. [DOI: 10.1016/j.imu.2018.03.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
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7
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Zhang L, Zhou W, Wang B, Zhang Z, Li F. Applying 1-norm SVM with squared loss to gene selection for cancer classification. APPL INTELL 2017. [DOI: 10.1007/s10489-017-1056-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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8
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Aziz R, Verma C, Srivastava N. Dimension reduction methods for microarray data: a review. AIMS BIOENGINEERING 2017. [DOI: 10.3934/bioeng.2017.2.179] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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9
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Aziz R, Verma C, Srivastava N. Dimension reduction methods for microarray data: a review. AIMS BIOENGINEERING 2017. [DOI: 10.3934/bioeng.2017.1.179] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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10
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Ang JC, Mirzal A, Haron H, Hamed HNA. Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2016; 13:971-989. [PMID: 26390495 DOI: 10.1109/tcbb.2015.2478454] [Citation(s) in RCA: 186] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Recently, feature selection and dimensionality reduction have become fundamental tools for many data mining tasks, especially for processing high-dimensional data such as gene expression microarray data. Gene expression microarray data comprises up to hundreds of thousands of features with relatively small sample size. Because learning algorithms usually do not work well with this kind of data, a challenge to reduce the data dimensionality arises. A huge number of gene selection are applied to select a subset of relevant features for model construction and to seek for better cancer classification performance. This paper presents the basic taxonomy of feature selection, and also reviews the state-of-the-art gene selection methods by grouping the literatures into three categories: supervised, unsupervised, and semi-supervised. The comparison of experimental results on top 5 representative gene expression datasets indicates that the classification accuracy of unsupervised and semi-supervised feature selection is competitive with supervised feature selection.
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Mundra PA, Rajapakse JC. Gene and sample selection using T-score with sample selection. J Biomed Inform 2016; 59:31-41. [DOI: 10.1016/j.jbi.2015.11.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Revised: 10/13/2015] [Accepted: 11/04/2015] [Indexed: 10/22/2022]
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12
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Mollaee M, Moattar MH. A novel feature extraction approach based on ensemble feature selection and modified discriminant independent component analysis for microarray data classification. Biocybern Biomed Eng 2016. [DOI: 10.1016/j.bbe.2016.05.001] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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13
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Similarity-balanced discriminant neighbor embedding and its application to cancer classification based on gene expression data. Comput Biol Med 2015; 64:236-45. [DOI: 10.1016/j.compbiomed.2015.07.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2014] [Revised: 07/08/2015] [Accepted: 07/10/2015] [Indexed: 11/21/2022]
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14
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New feature selection for gene expression classification based on degree of class overlap in principal dimensions. Comput Biol Med 2015; 64:292-8. [PMID: 25712072 DOI: 10.1016/j.compbiomed.2015.01.022] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2014] [Revised: 01/29/2015] [Accepted: 01/30/2015] [Indexed: 11/21/2022]
Abstract
Micro-array data are typically characterized by high dimensional features with a small number of samples. Several problems in identifying genes causing diseases from micro-array data can be transformed into the problem of classifying the features extracted from gene expression in micro-array data. However, too many features can cause low prediction accuracy as well as high computational complexity. Dimensional reduction is a method to eliminate irrelevant features to improve the prediction accuracy. Typically, the eigenvalues or dimensional data variance from principal component analysis are used as criteria to select relevant features. This approach is simple but not efficient since it does not concern the degree of data overlap in each dimension in the feature space. A new method to select relevant features based on degree of dimensional data overlap with proper feature selection was introduced. Furthermore, our study concentrated on small sized data sets which usually occur in reality. The experimental results signified that this new approach can achieve substantially higher prediction accuracy when compared with other methods.
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15
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Cui Y, Zheng CH, Yang J, Sha W. Sparse maximum margin discriminant analysis for feature extraction and gene selection on gene expression data. Comput Biol Med 2013; 43:933-41. [DOI: 10.1016/j.compbiomed.2013.04.018] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2011] [Revised: 04/25/2013] [Accepted: 04/26/2013] [Indexed: 10/26/2022]
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Hassanien AE, Al-Shammari ET, Ghali NI. Computational intelligence techniques in bioinformatics. Comput Biol Chem 2013; 47:37-47. [PMID: 23891719 DOI: 10.1016/j.compbiolchem.2013.04.007] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2013] [Revised: 04/06/2013] [Accepted: 04/24/2013] [Indexed: 10/26/2022]
Abstract
Computational intelligence (CI) is a well-established paradigm with current systems having many of the characteristics of biological computers and capable of performing a variety of tasks that are difficult to do using conventional techniques. It is a methodology involving adaptive mechanisms and/or an ability to learn that facilitate intelligent behavior in complex and changing environments, such that the system is perceived to possess one or more attributes of reason, such as generalization, discovery, association and abstraction. The objective of this article is to present to the CI and bioinformatics research communities some of the state-of-the-art in CI applications to bioinformatics and motivate research in new trend-setting directions. In this article, we present an overview of the CI techniques in bioinformatics. We will show how CI techniques including neural networks, restricted Boltzmann machine, deep belief network, fuzzy logic, rough sets, evolutionary algorithms (EA), genetic algorithms (GA), swarm intelligence, artificial immune systems and support vector machines, could be successfully employed to tackle various problems such as gene expression clustering and classification, protein sequence classification, gene selection, DNA fragment assembly, multiple sequence alignment, and protein function prediction and its structure. We discuss some representative methods to provide inspiring examples to illustrate how CI can be utilized to address these problems and how bioinformatics data can be characterized by CI. Challenges to be addressed and future directions of research are also presented and an extensive bibliography is included.
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Affiliation(s)
- Aboul Ella Hassanien
- Faculty of Computers and Information, Cairo University, 5 Ahmed Zewal Street, Orman, Giza, Egypt; Scientific Research Group in Egypt (SRGE), Egypt(1).
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Du G, Jiang Z, Diao X, Yao Y. Intelligent ensemble T-S fuzzy neural networks with RCDPSO_DM optimization for effective handling of complex clinical pathway variances. Comput Biol Med 2013; 43:613-34. [PMID: 23668338 DOI: 10.1016/j.compbiomed.2013.02.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2012] [Revised: 02/05/2013] [Accepted: 02/07/2013] [Indexed: 10/27/2022]
Abstract
Takagi-Sugeno (T-S) fuzzy neural networks (FNNs) can be used to handle complex, fuzzy, uncertain clinical pathway (CP) variances. However, there are many drawbacks, such as slow training rate, propensity to become trapped in a local minimum and poor ability to perform a global search. In order to improve overall performance of variance handling by T-S FNNs, a new CP variance handling method is proposed in this study. It is based on random cooperative decomposing particle swarm optimization with double mutation mechanism (RCDPSO_DM) for T-S FNNs. Moreover, the proposed integrated learning algorithm, combining the RCDPSO_DM algorithm with a Kalman filtering algorithm, is applied to optimize antecedent and consequent parameters of constructed T-S FNNs. Then, a multi-swarm cooperative immigrating particle swarm algorithm ensemble method is used for intelligent ensemble T-S FNNs with RCDPSO_DM optimization to further improve stability and accuracy of CP variance handling. Finally, two case studies on liver and kidney poisoning variances in osteosarcoma preoperative chemotherapy are used to validate the proposed method. The result demonstrates that intelligent ensemble T-S FNNs based on the RCDPSO_DM achieves superior performances, in terms of stability, efficiency, precision and generalizability, over PSO ensemble of all T-S FNNs with RCDPSO_DM optimization, single T-S FNNs with RCDPSO_DM optimization, standard T-S FNNs, standard Mamdani FNNs and T-S FNNs based on other algorithms (cooperative particle swarm optimization and particle swarm optimization) for CP variance handling. Therefore, it makes CP variance handling more effective.
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Affiliation(s)
- Gang Du
- Business School, East China Normal University, 500 Dong chuan Road, Shanghai 200241, China.
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Yu H, Ni J, Zhao J. ACOSampling: An ant colony optimization-based undersampling method for classifying imbalanced DNA microarray data. Neurocomputing 2013. [DOI: 10.1016/j.neucom.2012.08.018] [Citation(s) in RCA: 118] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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19
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Chuang LY, Yang CH, Wu KC, Yang CH. A hybrid feature selection method for DNA microarray data. Comput Biol Med 2011; 41:228-37. [DOI: 10.1016/j.compbiomed.2011.02.004] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2010] [Revised: 01/01/2011] [Accepted: 02/08/2011] [Indexed: 12/27/2022]
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20
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Feature selection for a cooperative coevolutionary classifier in liver fibrosis diagnosis. Comput Biol Med 2011; 41:238-46. [DOI: 10.1016/j.compbiomed.2011.02.006] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2009] [Revised: 09/12/2010] [Accepted: 02/16/2011] [Indexed: 12/28/2022]
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21
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Li B, Zheng CH, Huang DS, Zhang L, Han K. Gene expression data classification using locally linear discriminant embedding. Comput Biol Med 2010; 40:802-10. [PMID: 20864095 DOI: 10.1016/j.compbiomed.2010.08.003] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2009] [Revised: 06/12/2010] [Accepted: 08/17/2010] [Indexed: 11/16/2022]
Abstract
Gene expression data collected from DNA microarray are characterized by a large amount of variables (genes), but with only a small amount of observations (experiments). In this paper, manifold learning method is proposed to map the gene expression data to a low dimensional space, and then explore the intrinsic structure of the features so as to classify the microarray data more accurately. The proposed algorithm can project the gene expression data into a subspace with high intra-class compactness and inter-class separability. Experimental results on six DNA microarray datasets demonstrated that our method is efficient for discriminant feature extraction and gene expression data classification. This work is a meaningful attempt to analyze microarray data using manifold learning method; there should be much room for the application of manifold learning to bioinformatics due to its performance.
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Affiliation(s)
- Bo Li
- Intelligent Computing Laboratory, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, China
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