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Bhutia S, Patra B, Ray M. A hybrid approach for cancer classification based on squirrel search. JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES 2022. [DOI: 10.1080/02522667.2022.2091095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Santosini Bhutia
- Department of Computer Science & Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
| | - Bichitrananda Patra
- Department of Computer Application, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
| | - Mitrabinda Ray
- Department of Computer Application, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
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Prabhakar SK, Ryu S, Jeong IC, Won DO. A Dual Level Analysis with Evolutionary Computing and Swarm Models for Classification of Leukemia. BIOMED RESEARCH INTERNATIONAL 2022; 2022:2052061. [PMID: 35663047 PMCID: PMC9162867 DOI: 10.1155/2022/2052061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 03/17/2022] [Accepted: 03/28/2022] [Indexed: 11/17/2022]
Abstract
One of the major reasons of mortality in human beings is cancer, and there is an absolute necessity for doctors to identify and treat a person suffering from it. Leukemia is a group of blood cancers that usually originates in the bone marrow and results in very high number of abnormal cells. For the diagnosis of cancer, microarray data serves as an important clinical application and serves as a great aid to the entire medical community. The dimensionality of the microarray data is too high, and so selection of suitable genes is quite an important step for the improvement of data classification. Therefore, for the prediction and diagnosis of cancer, there is an utmost necessity to select the most informative genes. In this work, Minimum Redundancy Maximum Relevance (MRMR), Signal to Noise Ratio (SNR), Multivariate Error Weight Uncorrelated Shrunken Centroid (EWUSC), and multivariate correlation-based feature selection (CFS) are chosen as initial feature selection techniques. Then, to select the most informative genes, five different kinds of evolutionary optimization techniques too are incorporated here such as African Buffalo Optimization (ABO), Artificial Bee Colony Optimization (ABCO), Cockroach Swarm Optimization (CSO), Imperialist Competitive Optimization (ICO), and Social Spider Optimization (SSO). Finally, the optimized values are fed through classification process and the best results are obtained when multivariate CFS with SSO is utilized and classified with Probabilistic Neural Network (PNN), and a high classification accuracy of 95.70% is obtained.
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Affiliation(s)
- Sunil Kumar Prabhakar
- Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, 24252 Gangwon, Republic of Korea
| | - Semin Ryu
- Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, 24252 Gangwon, Republic of Korea
| | - In cheol Jeong
- Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, 24252 Gangwon, Republic of Korea
| | - Dong-Ok Won
- Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, 24252 Gangwon, Republic of Korea
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3
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Lai CM, Huang HP. A gene selection algorithm using simplified swarm optimization with multi-filter ensemble technique. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2020.106994] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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4
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Feng CM, Xu Y, Liu JX, Gao YL, Zheng CH. Supervised Discriminative Sparse PCA for Com-Characteristic Gene Selection and Tumor Classification on Multiview Biological Data. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2019; 30:2926-2937. [PMID: 30802874 DOI: 10.1109/tnnls.2019.2893190] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Principal component analysis (PCA) has been used to study the pathogenesis of diseases. To enhance the interpretability of classical PCA, various improved PCA methods have been proposed to date. Among these, a typical method is the so-called sparse PCA, which focuses on seeking sparse loadings. However, the performance of these methods is still far from satisfactory due to their limitation of using unsupervised learning methods; moreover, the class ambiguity within the sample is high. To overcome this problem, this paper developed a new PCA method, which is named the supervised discriminative sparse PCA (SDSPCA). The main innovation of this method is the incorporation of discriminative information and sparsity into the PCA model. Specifically, in contrast to the traditional sparse PCA, which imposes sparsity on the loadings, here, sparse components are obtained to represent the data. Furthermore, via the linear transformation, the sparse components approximate the given label information. On the one hand, sparse components improve interpretability over the traditional PCA, while on the other hand, they are have discriminative abilities suitable for classification purposes. A simple algorithm is developed, and its convergence proof is provided. SDSPCA has been applied to the common-characteristic gene selection and tumor classification on multiview biological data. The sparsity and classification performance of SDSPCA are empirically verified via abundant, reasonable, and effective experiments, and the obtained results demonstrate that SDSPCA outperforms other state-of-the-art methods.
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5
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Affiliation(s)
- Yong Wang
- Department of Statistics, University of Auckland, Auckland, New Zealand
| | - Xuxu Wang
- Department of Statistics, University of Auckland, Auckland, New Zealand
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6
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Hong CF, Chen YC, Chen WC, Tu KC, Tsai MH, Chan YK, Yu SS. Construction of diagnosis system and gene regulatory networks based on microarray analysis. J Biomed Inform 2018; 81:61-73. [PMID: 29550394 DOI: 10.1016/j.jbi.2018.03.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 01/30/2018] [Accepted: 03/12/2018] [Indexed: 01/02/2023]
Abstract
A microarray analysis generally contains expression data of thousands of genes, but most of them are irrelevant to the disease of interest, making analyzing the genes concerning specific diseases complicated. Therefore, filtering out a few essential genes as well as their regulatory networks is critical, and a disease can be easily diagnosed just depending on the expression profiles of a few critical genes. In this study, a target gene screening (TGS) system, which is a microarray-based information system that integrates F-statistics, pattern recognition matching, a two-layer K-means classifier, a Parameter Detection Genetic Algorithm (PDGA), a genetic-based gene selector (GBG selector) and the association rule, was developed to screen out a small subset of genes that can discriminate malignant stages of cancers. During the first stage, F-statistic, pattern recognition matching, and a two-layer K-means classifier were applied in the system to filter out the 20 critical genes most relevant to ovarian cancer from 9600 genes, and the PDGA was used to decide the fittest values of the parameters for these critical genes. Among the 20 critical genes, 15 are associated with cancer progression. In the second stage, we further employed a GBG selector and the association rule to screen out seven target gene sets, each with only four to six genes, and each of which can precisely identify the malignancy stage of ovarian cancer based on their expression profiles. We further deduced the gene regulatory networks of the 20 critical genes by applying the Pearson correlation coefficient to evaluate the correlationship between the expression of each gene at the same stages and at different stages. Correlationships between gene pairs were calculated, and then, three regulatory networks were deduced. Their correlationships were further confirmed by the Ingenuity pathway analysis. The prognostic significances of the genes identified via regulatory networks were examined using online tools, and most represented biomarker candidates. In summary, our proposed system provides a new strategy to identify critical genes or biomarkers, as well as their regulatory networks, from microarray data.
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Affiliation(s)
- Chun-Fu Hong
- Department of Long-Term Care, National Quemoy University, Kinmen County 892, Taiwan, ROC
| | - Ying-Chen Chen
- Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung City 402, Taiwan, ROC
| | - Wei-Chun Chen
- Department of Management Information System, National Chung Hsing University, Taichung City 402, Taiwan, ROC
| | - Keng-Chang Tu
- Deparment of Computer Science and Engineering, National Chung Hsing University, Taichung City 402, Taiwan, ROC
| | - Meng-Hsiun Tsai
- Department of Management Information System, National Chung Hsing University, Taichung City 402, Taiwan, ROC.
| | - Yung-Kuan Chan
- Department of Management Information System, National Chung Hsing University, Taichung City 402, Taiwan, ROC.
| | - Shyr Shen Yu
- Deparment of Computer Science and Engineering, National Chung Hsing University, Taichung City 402, Taiwan, ROC
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7
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Lai CM, Yeh WC, Chang CY. Gene selection using information gain and improved simplified swarm optimization. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.08.089] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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8
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Prediction of Early Recurrence of Liver Cancer by a Novel Discrete Bayes Decision Rule for Personalized Medicine. BIOMED RESEARCH INTERNATIONAL 2016; 2016:8567479. [PMID: 27800494 PMCID: PMC5075355 DOI: 10.1155/2016/8567479] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2016] [Revised: 08/10/2016] [Accepted: 09/08/2016] [Indexed: 02/07/2023]
Abstract
We discuss a novel diagnostic method for predicting the early recurrence of liver cancer with high accuracy for personalized medicine. The difficulty with cancer treatment is that even if the types of cancer are the same, the cancers vary depending on the patient. Thus, remarkable attention has been paid to personalized medicine. Unfortunately, although the Tokyo Score, the Modified JIS, and the TNM classification have been proposed as liver scoring systems, none of these scoring systems have met the needs of clinical practice. In this paper, we convert continuous and discrete data to categorical data and keep the natively categorical data as is. Then, we propose a discrete Bayes decision rule that can deal with the categorical data. This may lead to its use with various types of laboratory data. Experimental results show that the proposed method produced a sensitivity of 0.86 and a specificity of 0.49 for the test samples. This suggests that our method may be superior to the well-known Tokyo Score, the Modified JIS, and the TNM classification in terms of sensitivity. Additional comparative study shows that if the numbers of test samples in two classes are the same, this method works well in terms of the F1 measure compared to the existing scoring methods.
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Dubey AK, Gupta U, Jain S. Breast cancer statistics and prediction methodology: a systematic review and analysis. Asian Pac J Cancer Prev 2016; 16:4237-45. [PMID: 26028079 DOI: 10.7314/apjcp.2015.16.10.4237] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Breast cancer is a menacing cancer, primarily affecting women. Continuous research is going on for detecting breast cancer in the early stage as the possibility of cure in early stages is bright. There are two main objectives of this current study, first establish statistics for breast cancer and second to find methodologies which can be helpful in the early stage detection of the breast cancer based on previous studies. The breast cancer statistics for incidence and mortality of the UK, US, India and Egypt were considered for this study. The finding of this study proved that the overall mortality rates of the UK and US have been improved because of awareness, improved medical technology and screening, but in case of India and Egypt the condition is less positive because of lack of awareness. The methodological findings of this study suggest a combined framework based on data mining and evolutionary algorithms. It provides a strong bridge in improving the classification and detection accuracy of breast cancer data.
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Affiliation(s)
- Ashutosh Kumar Dubey
- Department of Computer Science and Engineering, JK Lakshmipat University, Jaipur, India E-mail :
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Guo NL, Wan YW. Network-based identification of biomarkers coexpressed with multiple pathways. Cancer Inform 2014; 13:37-47. [PMID: 25392692 PMCID: PMC4218687 DOI: 10.4137/cin.s14054] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2014] [Revised: 06/25/2014] [Accepted: 06/29/2014] [Indexed: 02/07/2023] Open
Abstract
Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson’s correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson’s correlation networks when evaluated with MSigDB database.
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Affiliation(s)
- Nancy Lan Guo
- Mary Babb Randolph Cancer Center/School of Public Health, West Virginia University, Morgantown, WV, USA
| | - Ying-Wooi Wan
- Mary Babb Randolph Cancer Center/School of Public Health, West Virginia University, Morgantown, WV, USA
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11
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A comparative analysis of swarm intelligence techniques for feature selection in cancer classification. ScientificWorldJournal 2014; 2014:693831. [PMID: 25157377 PMCID: PMC4137534 DOI: 10.1155/2014/693831] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2014] [Accepted: 06/18/2014] [Indexed: 11/17/2022] Open
Abstract
Feature selection in cancer classification is a central area of research in the field of bioinformatics and used to select the informative genes from thousands of genes of the microarray. The genes are ranked based on T-statistics, signal-to-noise ratio (SNR), and F-test values. The swarm intelligence (SI) technique finds the informative genes from the top-m ranked genes. These selected genes are used for classification. In this paper the shuffled frog leaping with Lévy flight (SFLLF) is proposed for feature selection. In SFLLF, the Lévy flight is included to avoid premature convergence of shuffled frog leaping (SFL) algorithm. The SI techniques such as particle swarm optimization (PSO), cuckoo search (CS), SFL, and SFLLF are used for feature selection which identifies informative genes for classification. The k-nearest neighbour (k-NN) technique is used to classify the samples. The proposed work is applied on 10 different benchmark datasets and examined with SI techniques. The experimental results show that the results obtained from k-NN classifier through SFLLF feature selection method outperform PSO, CS, and SFL.
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Mekler A, Schwarz D. Quality assessment of data discrimination using self-organizing maps. J Biomed Inform 2014; 51:210-8. [PMID: 24924268 DOI: 10.1016/j.jbi.2014.06.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2013] [Revised: 05/17/2014] [Accepted: 06/01/2014] [Indexed: 10/25/2022]
Abstract
MOTIVATION One of the important aspects of the data classification problem lies in making the most appropriate selection of features. The set of variables should be small and, at the same time, should provide reliable discrimination of the classes. The method for the discriminating power evaluation that enables a comparison between different sets of variables will be useful in the search for the set of variables. RESULTS A new approach to feature selection is presented. Two methods of evaluation of the data discriminating power of a feature set are suggested. Both of the methods implement self-organizing maps (SOMs) and the newly introduced exponents of the degree of data clusterization on the SOM. The first method is based on the comparison of intraclass and interclass distances on the map. Another method concerns the evaluation of the relative number of best matching unit's (BMUs) nearest neighbors of the same class. Both methods make it possible to evaluate the discriminating power of a feature set in cases when this set provides nonlinear discrimination of the classes. AVAILABILITY Current algorithms in program code can be downloaded for free at http://mekler.narod.ru/Science/Articles_support.html, as well as the supporting data files.
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Affiliation(s)
- Alexey Mekler
- The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, 61, Moika, 191186 Saint-Petersburg, Russia.
| | - Dmitri Schwarz
- The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, 61, Moika, 191186 Saint-Petersburg, Russia
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Wang X. Identification of Marker Genes for Cancer Based on Microarrays Using a Computational Biology Approach. Curr Bioinform 2014; 9:140-146. [PMID: 24683388 DOI: 10.2174/1574893608999140109115649] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Rapid advances in gene expression microarray technology have enabled to discover molecular markers used for cancer diagnosis, prognosis, and prediction. One computational challenge with using microarray data analysis to create cancer classifiers is how to effectively deal with microarray data which are composed of high-dimensional attributes (p) and low-dimensional instances (n). Gene selection and classifier construction are two key issues concerned with this topics. In this article, we reviewed major methods for computational identification of cancer marker genes. We concluded that simple methods should be preferred to complicated ones for their interpretability and applicability.
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Affiliation(s)
- Xiaosheng Wang
- Biometric Research Branch, National Cancer Institute, National Institutes of Health, Rockville, MD 20852, U.S.A
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Piao Y, Piao M, Park K, Ryu KH. An ensemble correlation-based gene selection algorithm for cancer classification with gene expression data. Bioinformatics 2012; 28:3306-15. [PMID: 23060613 DOI: 10.1093/bioinformatics/bts602] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Gene selection for cancer classification is one of the most important topics in the biomedical field. However, microarray data pose a severe challenge for computational techniques. We need dimension reduction techniques that identify a small set of genes to achieve better learning performance. From the perspective of machine learning, the selection of genes can be considered to be a feature selection problem that aims to find a small subset of features that has the most discriminative information for the target. RESULTS In this article, we proposed an Ensemble Correlation-Based Gene Selection algorithm based on symmetrical uncertainty and Support Vector Machine. In our method, symmetrical uncertainty was used to analyze the relevance of the genes, the different starting points of the relevant subset were used to generate the gene subsets and the Support Vector Machine was used as an evaluation criterion of the wrapper. The efficiency and effectiveness of our method were demonstrated through comparisons with other feature selection techniques, and the results show that our method outperformed other methods published in the literature.
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Affiliation(s)
- Yongjun Piao
- Department of Electrical and Computer Engineering, Chungbuk National University, Chungbuk, Korea
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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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16
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Affiliation(s)
- Nancy Lan Guo
- Mary Babb Randolph Cancer Center/Department of Community Medicine, School of Medicine, West Virginia University, Morgantown, WV 26506-9300
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TANG KAILIN, YAO WEIJIA, LI TONGHUA, LI YIXUE, CAO ZHIWEI. CANCER CLASSIFICATION FROM THE GENE EXPRESSION PROFILES BY DISCRIMINANT KERNEL-PLS. J Bioinform Comput Biol 2011; 8 Suppl 1:147-60. [DOI: 10.1142/s0219720010005130] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2010] [Revised: 08/25/2010] [Accepted: 09/10/2010] [Indexed: 11/18/2022]
Abstract
Cancer diagnosis depending on microarray technology has drawn more and more attention in the past few years. Accurate and fast diagnosis results make gene expression profiling produced from microarray widely used by a large range of researchers. Much research work highlights the importance of gene selection and gains good results. However, the minimum sets of genes derived from different methods are seldom overlapping and often inconsistent even for the same set of data, partially because of the complexity of cancer disease. In this paper, cancer classification was attempted in an alternative way of the whole gene expression profile for all samples instead of partial gene sets. Here, the three common sets of data were tested by NIPALS-KPLS method for acute leukemia, prostate cancer and lung cancer respectively. Compared to other conventional methods, the results showed wide improvement in classification accuracy. This paper indicates that sample profile of gene expression may be explored as a better indicator for cancer classification, which deserves further investigation.
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Affiliation(s)
- KAI-LIN TANG
- Shanghai Center for Bioinformation and Technology, 100 Qinzhou Road, Shanghai 200235, P. R. China
| | - WEI-JIA YAO
- Department of Chemistry, Tongji University, Shanghai 200092, P. R. China
| | - TONG-HUA LI
- Department of Chemistry, Tongji University, Shanghai 200092, P. R. China
| | - YI-XUE LI
- Shanghai Center for Bioinformation and Technology, 100 Qinzhou Road, Shanghai 200235, P. R. China
- Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Graduate School of the Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, P. R. China
| | - ZHI-WEI CAO
- Shanghai Center for Bioinformation and Technology, 100 Qinzhou Road, Shanghai 200235, P. R. China
- College of Life Science and Biotechnology, Tongji University, Shanghai 200092, P. R. China
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FLAOUNAS ILIASN, IAKOVIDIS DIMITRISK, MAROULIS DIMITRISE. CASCADING SVMS AS A TOOL FOR MEDICAL DIAGNOSIS USING MULTI-CLASS GENE EXPRESSION DATA. INT J ARTIF INTELL T 2011. [DOI: 10.1142/s0218213006002709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this paper we propose a novel Support Vector Machines-based architecture for medical diagnosis using multi-class gene expression data. It consists of a pre-processing unit and N-1 sequentially ordered blocks capable of classifying N classes in a cascading manner. Each block embodies both a gene selection and a classification module. It offers the flexibility of constructing block-specific gene expression spaces and hypersurfaces for the discrimination of the different classes. The proposed architecture was applied for medical diagnostic tasks including prostate and lung cancer diagnosis. Its performance was evaluated by using a leave-one-out cross validation approach which avoids the bias introduced by the gene selection process. The results show that it provides high accuracy which in most cases exceeds the accuracy achieved by the popular one-vs-one and one-vs-all SVM combination schemes and Nearest-Neighbor classifiers. The cascading SVMs can be successfully applied as a medical diagnostic tool.
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Affiliation(s)
- ILIAS N. FLAOUNAS
- Department of Informatics and Telecommunications, National and Kapodestrian Univ. of Athens, Panepistimiopolis, Ilissia, Athens, 15784, Greece
| | - DIMITRIS K. IAKOVIDIS
- Department of Informatics and Telecommunications, National and Kapodestrian Univ. of Athens, Panepistimiopolis, Ilissia, Athens, 15784, Greece
| | - DIMITRIS E. MAROULIS
- Department of Informatics and Telecommunications, National and Kapodestrian Univ. of Athens, Panepistimiopolis, Ilissia, Athens, 15784, Greece
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Habermann JK, Bündgen NK, Gemoll T, Hautaniemi S, Lundgren C, Wangsa D, Doering J, Bruch HP, Nordstroem B, Roblick UJ, Jörnvall H, Auer G, Ried T. Genomic instability influences the transcriptome and proteome in endometrial cancer subtypes. Mol Cancer 2011; 10:132. [PMID: 22040021 PMCID: PMC3261822 DOI: 10.1186/1476-4598-10-132] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2011] [Accepted: 10/31/2011] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In addition to clinical characteristics, DNA aneuploidy has been identified as a prognostic factor in epithelial malignancies in general and in endometrial cancers in particular. We mapped ploidy-associated chromosomal aberrations and identified corresponding gene and protein expression changes in endometrial cancers of different prognostic subgroups. METHODS DNA image cytometry classified 25 endometrioid cancers to be either diploid (n = 16) or aneuploid (n = 9), and all uterine papillary serous cancers (UPSC) to be aneuploid (n = 8). All samples were subjected to comparative genomic hybridization and gene expression profiling. Identified genes were subjected to Ingenuity pathway analysis (IPA) and were correlated to protein expression changes. RESULTS Comparative genomic hybridization revealed ploidy-associated specific, recurrent genomic imbalances. Gene expression analysis identified 54 genes between diploid and aneuploid endometrioid carcinomas, 39 genes between aneuploid endometrioid cancer and UPSC, and 76 genes between diploid endometrioid and aneuploid UPSC to be differentially expressed. Protein profiling identified AKR7A2 and ANXA2 to show translational alterations consistent with the transcriptional changes. The majority of differentially expressed genes and proteins belonged to identical molecular functions, foremost Cancer, Cell Death, and Cellular Assembly and Organization. CONCLUSIONS We conclude that the grade of genomic instability rather than the histopathological subtype correlates with specific gene and protein expression changes. The identified genes and proteins might be useful as molecular targets for improved diagnostic and therapeutic intervention and merit prospective validation.
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Affiliation(s)
- Jens K Habermann
- Laboratory for Surgical Research, Department of Surgery, University of Lübeck, Germany.
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Chow CK, Zhu HL, Lacy J, Kuo WP. Error margin analysis for feature gene extraction. BMC Bioinformatics 2010; 11:241. [PMID: 20459827 PMCID: PMC2885372 DOI: 10.1186/1471-2105-11-241] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2009] [Accepted: 05/11/2010] [Indexed: 11/25/2022] Open
Abstract
Background Feature gene extraction is a fundamental issue in microarray-based biomarker discovery. It is normally treated as an optimization problem of finding the best predictive feature genes that can effectively and stably discriminate distinct types of disease conditions, e.g. tumors and normals. Since gene microarray data normally involves thousands of genes at, tens or hundreds of samples, the gene extraction process may fall into local optimums if the gene set is optimized according to the maximization of classification accuracy of the classifier built from it. Results In this paper, we propose a novel gene extraction method of error margin analysis to optimize the feature genes. The proposed algorithm has been tested upon one synthetic dataset and two real microarray datasets. Meanwhile, it has been compared with five existing gene extraction algorithms on each dataset. On the synthetic dataset, the results show that the feature set extracted by our algorithm is the closest to the actual gene set. For the two real datasets, our algorithm is superior in terms of balancing the size and the validation accuracy of the resultant gene set when comparing to other algorithms. Conclusion Because of its distinct features, error margin analysis method can stably extract the relevant feature genes from microarray data for high-performance classification.
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Affiliation(s)
- Chi Kin Chow
- Research institute of Innovative Products and Technologies, The Hong Kong Polytechnic University, Hong Kong SAR, PR China
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A method of tumor classification based on wavelet packet transforms and neighborhood rough set. Comput Biol Med 2010; 40:430-7. [PMID: 20227068 DOI: 10.1016/j.compbiomed.2010.02.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2008] [Revised: 01/08/2010] [Accepted: 02/15/2010] [Indexed: 11/20/2022]
Abstract
Tumor classification is an important application domain of gene expression data. Because of its characteristics of high dimensionality and small sample size (SSS), and a great number of redundant genes not related to tumor phenotypes, various feature extraction or gene selection methods have been applied to gene expression data analysis. Wavelet packet transforms (WPT) and neighborhood rough sets (NRS) are effective tools to extract and select features. In this paper, a novel approach of tumor classification is proposed based on WPT and NRS. First the classification features are extracted by WPT and the decision tables are formed, then the attributes of the decision tables are reduced by NRS. Thirdly, a feature subset with few attributes and high classification ability is obtained. The experimental results on three gene expression datasets demonstrate that the proposed method is effective and feasible.
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Mary-Huard T, Robin S. Tailored aggregation for classification. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2009; 31:2098-2105. [PMID: 19762936 DOI: 10.1109/tpami.2009.55] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Compression and variable selection are two classical strategies to deal with large-dimension data sets in classification. We propose an alternative strategy, called aggregation, which consists of a clustering step of redundant variables and a compression step within each group. We develop a statistical framework to define tailored aggregation methods that can be combined with selection methods to build reliable classifiers that benefit from the information contained in redundant variables. Two algorithms are proposed for ordered and nonordered variables, respectively. Applications to the kNN and CART algorithms are presented.
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Anand A, Suganthan PN. Multiclass cancer classification by support vector machines with class-wise optimized genes and probability estimates. J Theor Biol 2009; 259:533-40. [PMID: 19406131 DOI: 10.1016/j.jtbi.2009.04.013] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2008] [Revised: 02/11/2009] [Accepted: 04/20/2009] [Indexed: 11/15/2022]
Abstract
We investigate the multiclass classification of cancer microarray samples. In contrast to classification of two cancer types from gene expression data, multiclass classification of more than two cancer types are relatively hard and less studied problem. We used class-wise optimized genes with corresponding one-versus-all support vector machine (OVA-SVM) classifier to maximize the utilization of selected genes. Final prediction was made by using probability scores from all classifiers. We used three different methods of estimating probability from decision value. Among the three probability methods, Platt's approach was more consistent, whereas, isotonic approach performed better for datasets with unequal proportion of samples in different classes. Probability based decision does not only gives true and fair comparison between different one-versus-all (OVA) classifiers but also gives the possibility of using them for any post analysis. Several ensemble experiments, an example of post analysis, of the three probability methods were implemented to study their effect in improving the classification accuracy. We observe that ensemble did help in improving the predictive accuracy of cancer data sets especially involving unbalanced samples. Four-fold external stratified cross-validation experiment was performed on the six multiclass cancer datasets to obtain unbiased estimates of prediction accuracies. Analysis of class-wise frequently selected genes on two cancer datasets demonstrated that the approach was able to select important and relevant genes consistent to literature. This study demonstrates successful implementation of the framework of class-wise feature selection and multiclass classification for prediction of cancer subtypes on six datasets.
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Affiliation(s)
- Ashish Anand
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, S2-B2a-21, Singapore 639798, Singapore
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Habermann JK, Doering J, Hautaniemi S, Roblick UJ, Bündgen NK, Nicorici D, Kronenwett U, Rathnagiriswaran S, Mettu RKR, Ma Y, Krüger S, Bruch HP, Auer G, Guo NL, Ried T. The gene expression signature of genomic instability in breast cancer is an independent predictor of clinical outcome. Int J Cancer 2009; 124:1552-64. [PMID: 19101988 DOI: 10.1002/ijc.24017] [Citation(s) in RCA: 92] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Recently, expression profiling of breast carcinomas has revealed gene signatures that predict clinical outcome, and discerned prognostically relevant breast cancer subtypes. Measurement of the degree of genomic instability provides a very similar stratification of prognostic groups. We therefore hypothesized that these features are linked. We used gene expression profiling of 48 breast cancer specimens that profoundly differed in their degree of genomic instability and identified a set of 12 genes that defines the 2 groups. The biological and prognostic significance of this gene set was established through survival prediction in published datasets from patients with breast cancer. Of note, the gene expression signatures that define specific prognostic subtypes in other breast cancer datasets, such as luminal A and B, basal, normal-like, and ERBB2+, and prognostic signatures including MammaPrint and Oncotype DX, predicted genomic instability in our samples. This remarkable congruence suggests a biological interdependence of poor-prognosis gene signatures, breast cancer subtypes, genomic instability, and clinical outcome.
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Affiliation(s)
- Jens K Habermann
- Genetics Branch, National Cancer Institute, NIH, Bethesda, MD 20892-8010, USA
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Zhu Y, Shen X, Pan W. Network-based support vector machine for classification of microarray samples. BMC Bioinformatics 2009; 10 Suppl 1:S21. [PMID: 19208121 PMCID: PMC2648796 DOI: 10.1186/1471-2105-10-s1-s21] [Citation(s) in RCA: 73] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Background The importance of network-based approach to identifying biological markers for diagnostic classification and prognostic assessment in the context of microarray data has been increasingly recognized. To our knowledge, there have been few, if any, statistical tools that explicitly incorporate the prior information of gene networks into classifier building. The main idea of this paper is to take full advantage of the biological observation that neighboring genes in a network tend to function together in biological processes and to embed this information into a formal statistical framework. Results We propose a network-based support vector machine for binary classification problems by constructing a penalty term from the F∞-norm being applied to pairwise gene neighbors with the hope to improve predictive performance and gene selection. Simulation studies in both low- and high-dimensional data settings as well as two real microarray applications indicate that the proposed method is able to identify more clinically relevant genes while maintaining a sparse model with either similar or higher prediction accuracy compared with the standard and the L1 penalized support vector machines. Conclusion The proposed network-based support vector machine has the potential to be a practically useful classification tool for microarrays and other high-dimensional data.
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Affiliation(s)
- Yanni Zhu
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455, USA.
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Zhang XW, Yap YL, Wei D, Chen F, Danchin A. Molecular diagnosis of human cancer type by gene expression profiles and independent component analysis. Eur J Hum Genet 2008; 13:1303-11. [PMID: 16205741 DOI: 10.1038/sj.ejhg.5201495] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
The precise diagnosis of cancer type based on microarray data is of particular importance and is also a challenging task. We have devised a novel pattern recognition procedure based on independent component analysis (ICA). Different from the conventional cancer classification methods, which are limited in their clinical applicability of cancer diagnosis, our method extracts explicitly, by ICA algorithm, a set of specific diagnostic patterns of normal and tumor tissues corresponding to a set of biomarkers for clinical use. We validated our procedure with the colon and prostate cancer data sets and achieved good diagnosis (>90%) on the data sets studied here. This technique is also suitable for the identification of diagnostic expression patterns for other human cancers and demonstrates the feasibility of simple and accurate molecular cancer diagnostics for clinical implementation.
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Affiliation(s)
- Xue Wu Zhang
- College of Light Industry and Food Sciences, South China University of Technology, Guangzhou, China.
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Cho H, Dhillon IS. Coclustering of human cancer microarrays using Minimum Sum-Squared Residue coclustering. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2008; 5:385-400. [PMID: 18670042 DOI: 10.1109/tcbb.2007.70268] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
It is a consensus in microarray analysis that identifying potential local patterns, characterized by coherent groups of genes and conditions, may shed light on the discovery of previously undetectable biological cellular processes of genes as well as macroscopic phenotypes of related samples. In order to simultaneously cluster genes and conditions, we have previously developed a fast co-clustering algorithm, Minimum Sum-Squared Residue Co-clustering (MSSRCC), which employs an alternating minimization scheme and generates what we call co-clusters in a checkerboard structure. In this paper, we propose specific strategies that enable MSSRCC to escape poor local minima and resolve the degeneracy problem in partitional clustering algorithms. The strategies include binormalization, deterministic spectral initialization, and incremental local search. We assess the effects of various strategies on both synthetic gene expression datasets and real human cancer microarrays and provide empirical evidence that MSSRCC with the proposed strategies performs better than existing co-clustering and clustering algorithms. In particular, the combination of all the three strategies leads to the best performance. Furthermore, we illustrate coherence of the resulting co-clusters in a checkerboard structure, where genes in a co-cluster manifest the phenotype structure of corresponding specific samples, and evaluate the enrichment of functional annotations in Gene Ontology (GO).
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Affiliation(s)
- Hyuk Cho
- Department of Computer Science, The University of Texas at Austin, 1 University Station C0500, Austin, TX 78712, USA.
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Zhu M, Martinez AM. Using the information embedded in the testing sample to break the limits caused by the small sample size in microarray-based classification. BMC Bioinformatics 2008; 9:280. [PMID: 18554411 PMCID: PMC2443146 DOI: 10.1186/1471-2105-9-280] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2007] [Accepted: 06/14/2008] [Indexed: 12/25/2022] Open
Abstract
Background Microarray-based tumor classification is characterized by a very large number of features (genes) and small number of samples. In such cases, statistical techniques cannot determine which genes are correlated to each tumor type. A popular solution is the use of a subset of pre-specified genes. However, molecular variations are generally correlated to a large number of genes. A gene that is not correlated to some disease may, by combination with other genes, express itself. Results In this paper, we propose a new classiification strategy that can reduce the effect of over-fitting without the need to pre-select a small subset of genes. Our solution works by taking advantage of the information embedded in the testing samples. We note that a well-defined classification algorithm works best when the data is properly labeled. Hence, our classification algorithm will discriminate all samples best when the testing sample is assumed to belong to the correct class. We compare our solution with several well-known alternatives for tumor classification on a variety of publicly available data-sets. Our approach consistently leads to better classification results. Conclusion Studies indicate that thousands of samples may be required to extract useful statistical information from microarray data. Herein, it is shown that this problem can be circumvented by using the information embedded in the testing samples.
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Affiliation(s)
- Manli Zhu
- Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA.
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30
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Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data. Comput Biol Chem 2008; 32:52-9. [DOI: 10.1016/j.compbiolchem.2007.10.001] [Citation(s) in RCA: 93] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2007] [Revised: 10/10/2007] [Accepted: 10/14/2007] [Indexed: 01/24/2023]
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31
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Sanden SV, Lin D, Burzykowski T. Performance of Gene Selection and Classification Methods in a Microarray Setting: A Simulation Study. COMMUN STAT-SIMUL C 2008. [DOI: 10.1080/03610910701792554] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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32
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Yoo C, Gernaey KV. Classification and Diagnostic Output Prediction of Cancer Using Gene Expression Profiling and Supervised Machine Learning Algorithms. JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 2008. [DOI: 10.1252/jcej.08we042] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Changkyoo Yoo
- College of Environment and Applied Chemistry, Green Energy Center/Center for Environmental Studies, Kyung Hee University
| | - Krist V. Gernaey
- Department of Chemical Engineering, Technical University of Denmark
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Gubskaya AV, Kholodovych V, Knight D, Kohn J, Welsh WJ. Prediction of Fibrinogen Adsorption for Biodegradable Polymers: Integration of Molecular Dynamics and Surrogate Modeling. POLYMER 2007; 48:5788-5801. [PMID: 19568328 PMCID: PMC2703561 DOI: 10.1016/j.polymer.2007.07.007] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
This work is a part of a series of publications devoted to the development of surrogate (semi-empirical) models for the prediction of fibrinogen adsorption onto polymer surfaces. Since fibrinogen is one of the key proteins involved in platelet activation and the formation of thrombosis, the modeling of fibrinogen adsorption on the surface of blood contacting medical devices is of high theoretical and practical significance. We report here, for the first time, on the incorporation three-dimensional structures of polymers obtained from atomistic simulations into conventional mesoscopic-scale calculations. Low energy conformations derived from Molecular Dynamics simulations for 45 representatives of a combinatorial library of polyarylates were used in an improved modeling procedure (referred to as "3D surrogate model") instead of simplistic two-dimensional representations of polymer structures, which were used in several previous models (collectively referred to as "2D surrogate models"). In the framework of this 3D model we created 12 model sets of polymers to account for their chirality, conformational diversity and the structural influence of a solvent. For each polymer set, three-dimensional molecular descriptors were generated and then ranked with respect to the experimental fibrinogen adsorption data by means of a Monte Carlo Decision Tree. The most significant descriptors identified by Decision Tree and the experimental dataset were utilized to predict fibrinogen adsorption using an Artificial Neural Network (ANN). The best prediction achieved by the 3D surrogate model demonstrated a noticeable improvement in the predictive quality as compared to the previously used 2D model (as evidenced by the increase in the average Pearson correlation coefficient from 0.67+/-0.13 to 0.54+/-0.12). The predictive quality of the 3D surrogate model compares favorably with the best results previously reported for extended 2D model that combines an ANN with Partial Least Squares (PLS) regression and principal component (PC) analysis. The significance of the newly developed 3D model is that it allows high accuracy prediction of fibrinogen adsorption without the need for experimentally derived descriptors and it has better predictive quality than the original 2D surrogate model due to utilization of realistic polymer representations.
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Affiliation(s)
- Anna V. Gubskaya
- New Jersey Center for Biomaterials, Rutgers, The State University of New Jersey, Piscataway, NJ 08854
| | - Vladyslav Kholodovych
- Department of Pharmacology, University of Medicine and Dentistry of New Jersey (UMDNJ)-Robert Wood Johnson Medical School (RWJMS), Piscataway, NJ 08854
| | - Doyle Knight
- Department of Mechanical and Aerospace Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854
| | - Joachim Kohn
- New Jersey Center for Biomaterials, Rutgers, The State University of New Jersey, Piscataway, NJ 08854
| | - William J. Welsh
- Department of Pharmacology, University of Medicine and Dentistry of New Jersey (UMDNJ)-Robert Wood Johnson Medical School (RWJMS), Piscataway, NJ 08854
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Perez-Diez A, Morgun A, Shulzhenko N. Microarrays for Cancer Diagnosis and Classification. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2007; 593:74-85. [PMID: 17265718 DOI: 10.1007/978-0-387-39978-2_8] [Citation(s) in RCA: 54] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Microarray analysis has yet to be widely accepted for diagnosis and classification of human cancers, despite the exponential increase in microarray studies reported in the literature. Among several methods available, a few refined approaches have evolved for the analysis of microarray data for cancer diagnosis. These include class comparison, class prediction and class discovery. Using as examples some of the major experimental contributions recently provided in the field of both hematological and solid tumors, we discuss the steps required to utilize microarray data to obtain general and reliable gene profiles that could be universally used in clinical laboratories. As we show, microarray technology is not only a new tool for the clinical lab but it can also improve the accuracy of the classical diagnostic techniques by suggesting novel tumor-specific markers. We then highlight the importance of publicly available microarray data and the development of their integrated analysis that may fulfill the promise that this new technology holds for cancer diagnosis and classification.
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Affiliation(s)
- Ainhoa Perez-Diez
- Ghost Lab, Laboratory of Cellular and Molecular Immunology, NIAID, NIH, USA.
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Zhang X, Wei D, Yap Y, Li L, Guo S, Chen F. Mass spectrometry-based "omics" technologies in cancer diagnostics. MASS SPECTROMETRY REVIEWS 2007; 26:403-31. [PMID: 17405143 DOI: 10.1002/mas.20132] [Citation(s) in RCA: 53] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Many "omics" techniques have been developed for one goal: biomarker discovery and early diagnosis of human cancers. A comprehensive review of mass spectrometry-based "omics" approaches performed on various biological samples for molecular diagnosis of human cancers is presented in this article. Furthermore, the existing and potential problems/solutions (both de facto experimental and bioinformatic challenges), and future prospects have been extensively discussed. Although the use of present omic methods as diagnostic tools are still in their infant stage and consequently not ready for immediate clinical use, it can be envisaged that the "omics"-based cancer diagnostics will gradually enter into the clinic in next 10 years as an important supplement to current clinical diagnostics.
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Affiliation(s)
- Xuewu Zhang
- College of Light Industry and Food Sciences, South China University of Technology, Guangzhou, China.
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36
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Mary-Huard T, Robin S, Daudin JJ. A penalized criterion for variable selection in classification. J MULTIVARIATE ANAL 2007. [DOI: 10.1016/j.jmva.2006.06.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Guillot G, Olsson M, Benson M, Rudemo M. Discrimination and scoring using small sets of genes for two-sample microarray data. Math Biosci 2006; 205:195-203. [PMID: 17087979 DOI: 10.1016/j.mbs.2006.08.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2006] [Revised: 06/01/2006] [Accepted: 08/07/2006] [Indexed: 10/24/2022]
Abstract
Comparison of gene expression for two groups of individuals form an important subclass of microarray experiments. We study multivariate procedures, in particular use of Hotelling's T2 for discrimination between the groups with a special emphasis on methods based on few genes only. We apply the methods to data from an experiment with a group of atopic dermatitis patients compared with a control group. We also compare our methodology to other recently proposed methods on publicly available datasets. It is found that (i) use of several genes gives a much improved discrimination of the groups as compared to one gene only, (ii) the genes that play the most important role in the multivariate analysis are not necessarily those that rank first in univariate comparisons of the groups, (iii) Linear Discriminant Analysis carried out with sets of 2-5 genes selected according to their Hotelling T2 give results comparable to state-of-the-art methods using many more genes, a feature of our method which might be crucial in clinical applications. Finding groups of genes that together give optimal multivariate discrimination (given the size of the group) can identify crucial pathways and networks of genes responsible for a disease. The computer code that we developed to make computations is available as an R package.
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Lai C, Reinders MJ, Wessels L. Random subspace method for multivariate feature selection. Pattern Recognit Lett 2006. [DOI: 10.1016/j.patrec.2005.12.018] [Citation(s) in RCA: 86] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Vinciotti V, Tucker A, Kellam P, Liu X. Robust Selection of Predictive Genes via a Simple Classifier. ACTA ACUST UNITED AC 2006; 5:1-11. [PMID: 16539532 DOI: 10.2165/00822942-200605010-00001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Identifying genes that direct the mechanism of a disease from expression data is extremely useful in understanding how that mechanism works. This in turn may lead to better diagnoses and potentially could lead to a cure for that disease. This task becomes extremely challenging when the data are characterised by only a small number of samples and a high number of dimensions, as is often the case with gene expression data. Motivated by this challenge, we present a general framework that focuses on simplicity and data perturbation. These are the keys for robust identification of the most predictive features in such data. Within this framework, we propose a simple selective naive Bayes classifier discovered using a global search technique, and combine it with data perturbation to increase its robustness for small sample sizes. An extensive validation of the method was carried out using two applied datasets from the field of microarrays and a simulated dataset, all confounded by small sample sizes and high dimensionality. The method has been shown to be capable of selecting genes known to be associated with prostate cancer and viral infections.
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Affiliation(s)
- Veronica Vinciotti
- School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, UK.
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Lai C, Reinders MJT, van't Veer LJ, Wessels LFA. A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets. BMC Bioinformatics 2006; 7:235. [PMID: 16670007 PMCID: PMC1569875 DOI: 10.1186/1471-2105-7-235] [Citation(s) in RCA: 87] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2005] [Accepted: 05/02/2006] [Indexed: 11/25/2022] Open
Abstract
Background Gene selection is an important step when building predictors of disease state based on gene expression data. Gene selection generally improves performance and identifies a relevant subset of genes. Many univariate and multivariate gene selection approaches have been proposed. Frequently the claim is made that genes are co-regulated (due to pathway dependencies) and that multivariate approaches are therefore per definition more desirable than univariate selection approaches. Based on the published performances of all these approaches a fair comparison of the available results can not be made. This mainly stems from two factors. First, the results are often biased, since the validation set is in one way or another involved in training the predictor, resulting in optimistically biased performance estimates. Second, the published results are often based on a small number of relatively simple datasets. Consequently no generally applicable conclusions can be drawn. Results In this study we adopted an unbiased protocol to perform a fair comparison of frequently used multivariate and univariate gene selection techniques, in combination with a ränge of classifiers. Our conclusions are based on seven gene expression datasets, across several cancer types. Conclusion Our experiments illustrate that, contrary to several previous studies, in five of the seven datasets univariate selection approaches yield consistently better results than multivariate approaches. The simplest multivariate selection approach, the Top Scoring method, achieves the best results on the remaining two datasets. We conclude that the correlation structures, if present, are difficult to extract due to the small number of samples, and that consequently, overly-complex gene selection algorithms that attempt to extract these structures are prone to overtraining.
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Affiliation(s)
- Carmen Lai
- Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands
| | - Marcel JT Reinders
- Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands
| | | | - Lodewyk FA Wessels
- Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands
- The Netherland's Cancer Institute, Amsterdam, The Netherlands
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Hong JH, Cho SB. The classification of cancer based on DNA microarray data that uses diverse ensemble genetic programming. Artif Intell Med 2006; 36:43-58. [PMID: 16102956 DOI: 10.1016/j.artmed.2005.06.002] [Citation(s) in RCA: 57] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2004] [Revised: 05/11/2005] [Accepted: 06/17/2005] [Indexed: 11/23/2022]
Abstract
OBJECT The classification of cancer based on gene expression data is one of the most important procedures in bioinformatics. In order to obtain highly accurate results, ensemble approaches have been applied when classifying DNA microarray data. Diversity is very important in these ensemble approaches, but it is difficult to apply conventional diversity measures when there are only a few training samples available. Key issues that need to be addressed under such circumstances are the development of a new ensemble approach that can enhance the successful classification of these datasets. MATERIALS AND METHODS An effective ensemble approach that does use diversity in genetic programming is proposed. This diversity is measured by comparing the structure of the classification rules instead of output-based diversity estimating. RESULTS Experiments performed on common gene expression datasets (such as lymphoma cancer dataset, lung cancer dataset and ovarian cancer dataset) demonstrate the performance of the proposed method in relation to the conventional approaches. CONCLUSION Diversity measured by comparing the structure of the classification rules obtained by genetic programming is useful to improve the performance of the ensemble classifier.
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Affiliation(s)
- Jin-Hyuk Hong
- Department of Computer Science, Yonsei University, 134 Sinchon-dong, Sudaemoon-ku, Seoul 120-749, Republic of Korea
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Liu X, Krishnan A, Mondry A. An entropy-based gene selection method for cancer classification using microarray data. BMC Bioinformatics 2005; 6:76. [PMID: 15790388 PMCID: PMC1087831 DOI: 10.1186/1471-2105-6-76] [Citation(s) in RCA: 95] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2004] [Accepted: 03/24/2005] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND Accurate diagnosis of cancer subtypes remains a challenging problem. Building classifiers based on gene expression data is a promising approach; yet the selection of non-redundant but relevant genes is difficult. The selected gene set should be small enough to allow diagnosis even in regular clinical laboratories and ideally identify genes involved in cancer-specific regulatory pathways. Here an entropy-based method is proposed that selects genes related to the different cancer classes while at the same time reducing the redundancy among the genes. RESULTS The present study identifies a subset of features by maximizing the relevance and minimizing the redundancy of the selected genes. A merit called normalized mutual information is employed to measure the relevance and the redundancy of the genes. In order to find a more representative subset of features, an iterative procedure is adopted that incorporates an initial clustering followed by data partitioning and the application of the algorithm to each of the partitions. A leave-one-out approach then selects the most commonly selected genes across all the different runs and the gene selection algorithm is applied again to pare down the list of selected genes until a minimal subset is obtained that gives a satisfactory accuracy of classification. The algorithm was applied to three different data sets and the results obtained were compared to work done by others using the same data sets. CONCLUSION This study presents an entropy-based iterative algorithm for selecting genes from microarray data that are able to classify various cancer sub-types with high accuracy. In addition, the feature set obtained is very compact, that is, the redundancy between genes is reduced to a large extent. This implies that classifiers can be built with a smaller subset of genes.
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Affiliation(s)
- Xiaoxing Liu
- Bioinformatics Institute, 30, Biopolis Street, #07-01, (S) 138671, Singapore
| | - Arun Krishnan
- Bioinformatics Institute, 30, Biopolis Street, #07-01, (S) 138671, Singapore
| | - Adrian Mondry
- Bioinformatics Institute, 30, Biopolis Street, #07-01, (S) 138671, Singapore
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Fu LM, Fu-Liu CS. Evaluation of gene importance in microarray data based upon probability of selection. BMC Bioinformatics 2005; 6:67. [PMID: 15784140 PMCID: PMC1274261 DOI: 10.1186/1471-2105-6-67] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2004] [Accepted: 03/22/2005] [Indexed: 01/30/2023] Open
Abstract
Background Microarray devices permit a genome-scale evaluation of gene function. This technology has catalyzed biomedical research and development in recent years. As many important diseases can be traced down to the gene level, a long-standing research problem is to identify specific gene expression patterns linking to metabolic characteristics that contribute to disease development and progression. The microarray approach offers an expedited solution to this problem. However, it has posed a challenging issue to recognize disease-related genes expression patterns embedded in the microarray data. In selecting a small set of biologically significant genes for classifier design, the nature of high data dimensionality inherent in this problem creates substantial amount of uncertainty. Results Here we present a model for probability analysis of selected genes in order to determine their importance. Our contribution is that we show how to derive the P value of each selected gene in multiple gene selection trials based on different combinations of data samples and how to conduct a reliability analysis accordingly. The importance of a gene is indicated by its associated P value in that a smaller value implies higher information content from information theory. On the microarray data concerning the subtype classification of small round blue cell tumors, we demonstrate that the method is capable of finding the smallest set of genes (19 genes) with optimal classification performance, compared with results reported in the literature. Conclusion In classifier design based on microarray data, the probability value derived from gene selection based on multiple combinations of data samples enables an effective mechanism for reducing the tendency of fitting local data particularities.
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MESH Headings
- Algorithms
- Artificial Intelligence
- Cell Line, Tumor
- Colonic Neoplasms/diagnosis
- Colonic Neoplasms/genetics
- Data Interpretation, Statistical
- Databases, Genetic
- Databases, Protein
- Evaluation Studies as Topic
- Gene Expression Profiling/methods
- Gene Expression Regulation, Neoplastic
- Genome, Human
- Humans
- Leukemia/diagnosis
- Leukemia/genetics
- Models, Biological
- Models, Statistical
- Oligonucleotide Array Sequence Analysis/methods
- Pattern Recognition, Automated
- Probability
- Protein Structure, Tertiary
- Reproducibility of Results
- Selection, Genetic
- Sensitivity and Specificity
- Sequence Analysis, DNA
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Affiliation(s)
- Li M Fu
- Pacific Tuberculosis and Cancer Research Organization, Pasadena, California, USA
- University of Florida, Gainesville, Florida, USA
| | - Casey S Fu-Liu
- Pacific Tuberculosis and Cancer Research Organization, Pasadena, California, USA
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Tsai CA, Chen CH, Lee TC, Ho IC, Yang UC, Chen JJ. Gene Selection for Sample Classifications in Microarray Experiments. DNA Cell Biol 2004; 23:607-14. [PMID: 15585118 DOI: 10.1089/dna.2004.23.607] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
DNA microarray technology provides useful tools for profiling global gene expression patterns in different cell/tissue samples. One major challenge is the large number of genes relative to the number of samples. The use of all genes can suppress or reduce the performance of a classification rule due to the noise of nondiscriminatory genes. Selection of an optimal subset from the original gene set becomes an important prestep in sample classification. In this study, we propose a family-wise error (FWE) rate approach to selection of discriminatory genes for two-sample or multiple-sample classification. The FWE approach controls the probability of the number of one or more false positives at a prespecified level. A public colon cancer data set is used to evaluate the performance of the proposed approach for the two classification methods: k nearest neighbors (k-NN) and support vector machine (SVM). The selected gene sets from the proposed procedure appears to perform better than or comparable to several results reported in the literature using the univariate analysis without performing multivariate search. In addition, we apply the FWE approach to a toxicogenomic data set with nine treatments (a control and eight metals, As, Cd, Ni, Cr, Sb, Pb, Cu, and AsV) for a total of 55 samples for a multisample classification. Two gene sets are considered: the gene set omegaF formed by the ANOVA F-test, and a gene set omegaT formed by the union of one-versus-all t-tests. The predicted accuracies are evaluated using the internal and external crossvalidation. Using the SVM classification, the overall accuracies to predict 55 samples into one of the nine treatments are above 80% for internal crossvalidation. OmegaF has slightly higher accuracy rates than omegaT. The overall predicted accuracies are above 70% for the external crossvalidation; the two gene sets omegaT and omegaF performed equally well.
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Affiliation(s)
- Chen-An Tsai
- Division of Biometry and Risk Assessment, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas 72079, USA
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Krishnapuram B, Carin L, Hartemink AJ. Joint classifier and feature optimization for comprehensive cancer diagnosis using gene expression data. J Comput Biol 2004; 11:227-42. [PMID: 15285890 DOI: 10.1089/1066527041410463] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Recent research has demonstrated quite convincingly that accurate cancer diagnosis can be achieved by constructing classifiers that are designed to compare the gene expression profile of a tissue of unknown cancer status to a database of stored expression profiles from tissues of known cancer status. This paper introduces the JCFO, a novel algorithm that uses a sparse Bayesian approach to jointly identify both the optimal nonlinear classifier for diagnosis and the optimal set of genes on which to base that diagnosis. We show that the diagnostic classification accuracy of the proposed algorithm is superior to a number of current state-of-the-art methods in a full leave-one-out cross-validation study of five widely used benchmark datasets. In addition to its superior classification accuracy, the algorithm is designed to automatically identify a small subset of genes (typically around twenty in our experiments) that are capable of providing complete discriminatory information for diagnosis. Focusing attention on a small subset of genes is useful not only because it produces a classifier with good generalization capacity, but also because this set of genes may provide insights into the mechanisms responsible for the disease itself. A number of the genes identified by the JCFO in our experiments are already in use as clinical markers for cancer diagnosis; some of the remaining genes may be excellent candidates for further clinical investigation. If it is possible to identify a small set of genes that is indeed capable of providing complete discrimination, inexpensive diagnostic assays might be widely deployable in clinical settings.
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Affiliation(s)
- Balaji Krishnapuram
- Department of Electrical Engineering, Duke University, Durham, NC 27708-0291, USA
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Li W, Sun F, Grosse I. Extreme value distribution based gene selection criteria for discriminant microarray data analysis using logistic regression. J Comput Biol 2004; 11:215-26. [PMID: 15285889 DOI: 10.1089/1066527041410445] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
One important issue commonly encountered in the analysis of microarray data is to decide which and how many genes should be selected for further studies. For discriminant microarray data analyses based on statistical models, such as the logistic regression models, gene selection can be accomplished by a comparison of the maximum likelihood of the model given the real data, L(D|M), and the expected maximum likelihood of the model given an ensemble of surrogate data with randomly permuted label, L(D(0)|M). Typically, the computational burden for obtaining L(D(0)M) is immense, often exceeding the limits of available computing resources by orders of magnitude. Here, we propose an approach that circumvents such heavy computations by mapping the simulation problem to an extreme-value problem. We present the derivation of an asymptotic distribution of the extreme-value as well as its mean, median, and variance. Using this distribution, we propose two gene selection criteria, and we apply them to two microarray datasets and three classification tasks for illustration.
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Affiliation(s)
- Wentian Li
- The Robert S. Boas Center for Genomics and Human Genetics, North Shore LIJ Research Institute, 350 Community Drive, Manhasset, NY 11030, USA.
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Jiang H, Deng Y, Chen HS, Tao L, Sha Q, Chen J, Tsai CJ, Zhang S. Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes. BMC Bioinformatics 2004; 5:81. [PMID: 15217521 PMCID: PMC476733 DOI: 10.1186/1471-2105-5-81] [Citation(s) in RCA: 194] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2004] [Accepted: 06/24/2004] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Due to the high cost and low reproducibility of many microarray experiments, it is not surprising to find a limited number of patient samples in each study, and very few common identified marker genes among different studies involving patients with the same disease. Therefore, it is of great interest and challenge to merge data sets from multiple studies to increase the sample size, which may in turn increase the power of statistical inferences. In this study, we combined two lung cancer studies using microarray GeneChip, employed two gene shaving methods and a two-step survival test to identify genes with expression patterns that can distinguish diseased from normal samples, and to indicate patient survival, respectively. RESULTS In addition to common data transformation and normalization procedures, we applied a distribution transformation method to integrate the two data sets. Gene shaving (GS) methods based on Random Forests (RF) and Fisher's Linear Discrimination (FLD) were then applied separately to the joint data set for cancer gene selection. The two methods discovered 13 and 10 marker genes (5 in common), respectively, with expression patterns differentiating diseased from normal samples. Among these marker genes, 8 and 7 were found to be cancer-related in other published reports. Furthermore, based on these marker genes, the classifiers we built from one data set predicted the other data set with more than 98% accuracy. Using the univariate Cox proportional hazard regression model, the expression patterns of 36 genes were found to be significantly correlated with patient survival (p < 0.05). Twenty-six of these 36 genes were reported as survival-related genes from the literature, including 7 known tumor-suppressor genes and 9 oncogenes. Additional principal component regression analysis further reduced the gene list from 36 to 16. CONCLUSION This study provided a valuable method of integrating microarray data sets with different origins, and new methods of selecting a minimum number of marker genes to aid in cancer diagnosis. After careful data integration, the classification method developed from one data set can be applied to the other with high prediction accuracy.
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Affiliation(s)
- Hongying Jiang
- Plant Biotechnology Research Center, School of Forest Resources & Environmental Science, Michigan Technological University, 1400 Townsend Dr., Houghton, MI 49931, USA
| | - Youping Deng
- Division of Biology, Kansas State University, Manhattan, KS 66506, USA
| | - Huann-Sheng Chen
- Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Dr., Houghton, MI 49931, USA
| | - Lin Tao
- Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Dr., Houghton, MI 49931, USA
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Dr., Houghton, MI 49931, USA
| | - Jun Chen
- Division of Biology, Kansas State University, Manhattan, KS 66506, USA
| | - Chung-Jui Tsai
- Plant Biotechnology Research Center, School of Forest Resources & Environmental Science, Michigan Technological University, 1400 Townsend Dr., Houghton, MI 49931, USA
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Dr., Houghton, MI 49931, USA
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Fu LM, Fu-Liu CS. Multi-class cancer subtype classification based on gene expression signatures with reliability analysis. FEBS Lett 2004; 561:186-90. [PMID: 15013775 DOI: 10.1016/s0014-5793(04)00175-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2004] [Accepted: 01/28/2004] [Indexed: 10/26/2022]
Abstract
Differential diagnosis among a group of histologically similar cancers poses a challenging problem in clinical medicine. Constructing a classifier based on gene expression signatures comprising multiple discriminatory molecular markers derived from microarray data analysis is an emerging trend for cancer diagnosis. To identify the best genes for classification using a small number of samples relative to the genome size remains the bottleneck of this approach, despite its promise. We have devised a new method of gene selection with reliability analysis, and demonstrated that this method can identify a more compact set of genes than other methods for constructing a classifier with optimum predictive performance for both small round blue cell tumors and leukemia. High consensus between our result and the results produced by methods based on artificial neural networks and statistical techniques confers additional evidence of the validity of our method. This study suggests a way for implementing a reliable molecular cancer classifier based on gene expression signatures.
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Affiliation(s)
- Li M Fu
- Pacific Tuberculosis and Cancer Research Organization, Los Angeles, CA, USA.
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