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Colello R, Baigorri M, Del Canto F, González J, Rogé A, van der Ploeg C, Sánchez Chopa F, Sparo M, Etcheverría A, Padola NL. Occurrence and genetic characterization of Shiga toxin-producing Escherichia coli on bovine and pork carcasses and the environment from transport trucks. World J Microbiol Biotechnol 2023; 39:174. [PMID: 37115263 DOI: 10.1007/s11274-023-03624-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 04/17/2023] [Indexed: 04/29/2023]
Abstract
Shiga toxin-producing Escherichia coli (STEC) are foodborne pathogens causing severe diseases. The ability of STEC to produce disease is associated with Shiga toxin (Stx) production. We investigated the occurrence of STEC on bovine and pork carcasses and walls of trucks where they were transported, and we characterized virulence genes and serotypes of STEC strains. We compared the whole genomic sequencing of a STEC O157:H7 strain isolated from a bovine carcass in this work and a STEC O157:H7 strain isolated from a child with HUS, both isolated in 2019. We studied the relationship between these isolates and others collected in the database. The results show a 40% of STEC and two different serogroups were identified (O130 and O157). STEC O157:H7 were isolated from bovine carcasses and harbored stx2, eae, ehxA, katP, espP, stcE, ECSP_0242/1773/2687/2870/2872/3286/3620 and were classified as lineage I/II. In STEC non-O157 isolates, three isolates were isolated from bovine carcasses and harbored the serogroup O130 and one strain isolated from pork carcasses was O-non-typeable. All STEC non-O157 harbored sxt1 gene. The analysis from the whole genome showed that both STEC O157:H7 strains belonged to the hypervirulent clade 8, ST11, phylogroup E, carried the allele tir 255 T > A T, and they were not clonal. The analysis of information allows us to conclude that the STEC strains circulate in pork and bovine carcasses arriving in transport. This situation represents a risk for the consumers and the need to implement an integrated STEC control in the food chain.
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Affiliation(s)
- Rocío Colello
- Facultad de Ciencias Veterinarias, Universidad Nacional del Centro de la Provincia de Buenos Aires (UNCPBA), Tandil, Buenos Aires, Argentina.
- Centro de Investigación Veterinaria de Tandil (CIVETAN), UNCPBA- CICPBA- CONICET, Tandil, Buenos Aires, Argentina.
| | - Manuela Baigorri
- Facultad de Ciencias Veterinarias, Universidad Nacional del Centro de la Provincia de Buenos Aires (UNCPBA), Tandil, Buenos Aires, Argentina
| | - Felipe Del Canto
- Programa de Microbiología y Micología, Instituto de Ciencias Biomédicas, Facultad de Medicina, Universidad de Chile, Santiago, Chile
| | - Juliana González
- Facultad de Ciencias Veterinarias, Universidad Nacional del Centro de la Provincia de Buenos Aires (UNCPBA), Tandil, Buenos Aires, Argentina
- Centro de Investigación Veterinaria de Tandil (CIVETAN), UNCPBA- CICPBA- CONICET, Tandil, Buenos Aires, Argentina
| | - Ariel Rogé
- Servicio Antígenos y Antisueros, Instituto Nacional de Producción de Biológicos, Administración Nacional de Laboratorios e Institutos de Salud "Dr. Carlos G. Malbrán", Buenos Aires, Argentina
| | - Claudia van der Ploeg
- Servicio Antígenos y Antisueros, Instituto Nacional de Producción de Biológicos, Administración Nacional de Laboratorios e Institutos de Salud "Dr. Carlos G. Malbrán", Buenos Aires, Argentina
| | - Federico Sánchez Chopa
- Facultad de Ciencias Veterinarias, Universidad Nacional del Centro de la Provincia de Buenos Aires (UNCPBA), Tandil, Buenos Aires, Argentina
- Centro de Investigación Veterinaria de Tandil (CIVETAN), UNCPBA- CICPBA- CONICET, Tandil, Buenos Aires, Argentina
| | - Mónica Sparo
- Laboratorio de Microbiología Clínica, Hospital Ramón Santamarina, Tandil, Buenos Aires, Argentina
| | - Analía Etcheverría
- Facultad de Ciencias Veterinarias, Universidad Nacional del Centro de la Provincia de Buenos Aires (UNCPBA), Tandil, Buenos Aires, Argentina
- Centro de Investigación Veterinaria de Tandil (CIVETAN), UNCPBA- CICPBA- CONICET, Tandil, Buenos Aires, Argentina
| | - Nora Lía Padola
- Facultad de Ciencias Veterinarias, Universidad Nacional del Centro de la Provincia de Buenos Aires (UNCPBA), Tandil, Buenos Aires, Argentina
- Centro de Investigación Veterinaria de Tandil (CIVETAN), UNCPBA- CICPBA- CONICET, Tandil, Buenos Aires, Argentina
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Nekouie N, Romoozi M, Esmaeili M. A New Evolutionary Ensemble Learning of Multimodal Feature Selection from Microarray Data. Neural Process Lett 2023. [DOI: 10.1007/s11063-023-11159-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
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Feature selection in high dimensional data: A specific preordonnances-based memetic algorithm. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2023.110420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
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Serra A, Cattelani L, Fratello M, Fortino V, Kinaret PAS, Greco D. Supervised Methods for Biomarker Detection from Microarray Experiments. Methods Mol Biol 2022; 2401:101-120. [PMID: 34902125 DOI: 10.1007/978-1-0716-1839-4_8] [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] [Indexed: 06/14/2023]
Abstract
Biomarkers are valuable indicators of the state of a biological system. Microarray technology has been extensively used to identify biomarkers and build computational predictive models for disease prognosis, drug sensitivity and toxicity evaluations. Activation biomarkers can be used to understand the underlying signaling cascades, mechanisms of action and biological cross talk. Biomarker detection from microarray data requires several considerations both from the biological and computational points of view. In this chapter, we describe the main methodology used in biomarkers discovery and predictive modeling and we address some of the related challenges. Moreover, we discuss biomarker validation and give some insights into multiomics strategies for biomarker detection.
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Affiliation(s)
- Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), University of Tampere, Tampere, Finland
| | - Luca Cattelani
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), University of Tampere, Tampere, Finland
| | - Michele Fratello
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), University of Tampere, Tampere, Finland
| | - Vittorio Fortino
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| | - Pia Anneli Sofia Kinaret
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), University of Tampere, Tampere, Finland
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
- BioMediTech Institute, Tampere University, Tampere, Finland.
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), University of Tampere, Tampere, Finland.
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland.
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Azadifar S, Ahmadi A. A graph-based gene selection method for medical diagnosis problems using a many-objective PSO algorithm. BMC Med Inform Decis Mak 2021; 21:333. [PMID: 34838034 PMCID: PMC8627636 DOI: 10.1186/s12911-021-01696-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 11/16/2021] [Indexed: 11/16/2022] Open
Abstract
Background Gene expression data play an important role in bioinformatics applications. Although there may be a large number of features in such data, they mainly tend to contain only a few samples. This can negatively impact the performance of data mining and machine learning algorithms. One of the most effective approaches to alleviate this problem is to use gene selection methods. The aim of gene selection is to reduce the dimensions (features) of gene expression data leading to eliminating irrelevant and redundant genes. Methods This paper presents a hybrid gene selection method based on graph theory and a many-objective particle swarm optimization (PSO) algorithm. To this end, a filter method is first utilized to reduce the initial space of the genes. Then, the gene space is represented as a graph to apply a graph clustering method to group the genes into several clusters. Moreover, the many-objective PSO algorithm is utilized to search an optimal subset of genes according to several criteria, which include classification error, node centrality, specificity, edge centrality, and the number of selected genes. A repair operator is proposed to cover the whole space of the genes and ensure that at least one gene is selected from each cluster. This leads to an increasement in the diversity of the selected genes. Results To evaluate the performance of the proposed method, extensive experiments are conducted based on seven datasets and two evaluation measures. In addition, three classifiers—Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)—are utilized to compare the effectiveness of the proposed gene selection method with other state-of-the-art methods. The results of these experiments demonstrate that our proposed method not only achieves more accurate classification, but also selects fewer genes than other methods. Conclusion This study shows that the proposed multi-objective PSO algorithm simultaneously removes irrelevant and redundant features using several different criteria. Also, the use of the clustering algorithm and the repair operator has improved the performance of the proposed method by covering the whole space of the problem.
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Affiliation(s)
- Saeid Azadifar
- Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran.
| | - Ali Ahmadi
- Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran
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Irum S, Naz K, Ullah N, Mustafa Z, Ali A, Arslan M, Khalid K, Andleeb S. Antimicrobial Resistance and Genomic Characterization of Six New Sequence Types in Multidrug-Resistant Pseudomonas aeruginosa Clinical Isolates from Pakistan. Antibiotics (Basel) 2021; 10:antibiotics10111386. [PMID: 34827324 PMCID: PMC8615273 DOI: 10.3390/antibiotics10111386] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 08/06/2021] [Accepted: 08/13/2021] [Indexed: 01/13/2023] Open
Abstract
Pseudomonas aeruginosa (P. aeruginosa) is a major bacterial pathogen associated with a variety of infections with high mortality rates. Most of the clinical P. aeruginosa isolates belong to a limited number of genetic subgroups characterized by multiple housekeeping genes’ sequences (usually 5–7) through the Multi-Locus Sequence Typing (MLST) scheme. The emergence and dissemination of novel multidrug-resistant (MDR) sequence types (ST) in P. aeruginosa pose serious clinical concerns. We performed whole-genome sequencing on a cohort (n = 160) of MDR P. aeruginosa isolates collected from a tertiary care hospital lab in Pakistan and found six isolates belonging to six unique MLST allelic profiles. The genomes were submitted to the PubMLST database and new ST numbers (ST3493, ST3494, ST3472, ST3489, ST3491, and ST3492) were assigned to the respective allele combinations. MLST and core-genome-based phylogenetic analysis confirmed the divergence of these isolates and positioned them in separate branches. Analysis of the resistome of the new STs isolates revealed the presence of genes blaOXA-50, blaPAO, blaPDC, blaVIM-2, aph(3′)-IIb, aac(6′)-II, aac(3)-Id, fosA, catB7, dfrB2, crpP, merP and a number of missense and frame-shift mutations in chromosomal genes conferring resistance to various antipseudomonal antibiotics. The exoS, exoT, pvdE, rhlI, rhlR, lasA, lasB, lasI, and lasR genes were the most prevalent virulence-related genes among the new ST isolates. The different genotypic features revealed the adaptation of these new clones to a variety of infections by various mutations in genes affecting antimicrobial resistance, quorum sensing and biofilm formation. Close monitoring of these antibiotic-resistant pathogens and surveillance mechanisms needs to be adopted to reduce their spread to the healthcare facilities of Pakistan. We believe that these strains can be used as reference strains for future comparative analysis of isolates belonging to the same STs.
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Affiliation(s)
- Sidra Irum
- Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences & Technology (NUST), Islamabad 44000, Pakistan; (S.I.); (K.N.); (N.U.); (Z.M.); (A.A.); (K.K.)
| | - Kanwal Naz
- Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences & Technology (NUST), Islamabad 44000, Pakistan; (S.I.); (K.N.); (N.U.); (Z.M.); (A.A.); (K.K.)
| | - Nimat Ullah
- Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences & Technology (NUST), Islamabad 44000, Pakistan; (S.I.); (K.N.); (N.U.); (Z.M.); (A.A.); (K.K.)
| | - Zeeshan Mustafa
- Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences & Technology (NUST), Islamabad 44000, Pakistan; (S.I.); (K.N.); (N.U.); (Z.M.); (A.A.); (K.K.)
| | - Amjad Ali
- Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences & Technology (NUST), Islamabad 44000, Pakistan; (S.I.); (K.N.); (N.U.); (Z.M.); (A.A.); (K.K.)
| | - Muhammad Arslan
- Pakistan Institute of Medical Sciences (PIMS), Islamabad 44000, Pakistan;
| | - Kashaf Khalid
- Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences & Technology (NUST), Islamabad 44000, Pakistan; (S.I.); (K.N.); (N.U.); (Z.M.); (A.A.); (K.K.)
| | - Saadia Andleeb
- Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences & Technology (NUST), Islamabad 44000, Pakistan; (S.I.); (K.N.); (N.U.); (Z.M.); (A.A.); (K.K.)
- Correspondence: or
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Feature Selection and Feature Stability Measurement Method for High-Dimensional Small Sample Data Based on Big Data Technology. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:3597051. [PMID: 34603430 PMCID: PMC8486514 DOI: 10.1155/2021/3597051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 09/02/2021] [Indexed: 11/17/2022]
Abstract
With the rapid development of artificial intelligence in recent years, the research on image processing, text mining, and genome informatics has gradually deepened, and the mining of large-scale databases has begun to receive more and more attention. The objects of data mining have also become more complex, and the data dimensions of mining objects have become higher and higher. Compared with the ultra-high data dimensions, the number of samples available for analysis is too small, resulting in the production of high-dimensional small sample data. High-dimensional small sample data will bring serious dimensional disasters to the mining process. Through feature selection, redundancy and noise features in high-dimensional small sample data can be effectively eliminated, avoiding dimensional disasters and improving the actual efficiency of mining algorithms. However, the existing feature selection methods emphasize the classification or clustering performance of the feature selection results and ignore the stability of the feature selection results, which will lead to unstable feature selection results, and it is difficult to obtain real and understandable features. Based on the traditional feature selection method, this paper proposes an ensemble feature selection method, Random Bits Forest Recursive Clustering Eliminate (RBF-RCE) feature selection method, combined with multiple sets of basic classifiers to carry out parallel learning and screen out the best feature classification results, optimizes the classification performance of traditional feature selection methods, and can also improve the stability of feature selection. Then, this paper analyzes the reasons for the instability of feature selection and introduces a feature selection stability measurement method, the Intersection Measurement (IM), to evaluate whether the feature selection process is stable. The effectiveness of the proposed method is verified by experiments on several groups of high-dimensional small sample data sets.
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Zheng X, Zhang C. Gene selection for microarray data classification via dual latent representation learning. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.07.047] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Gene Correlation Guided Gene Selection for Microarray Data Classification. BIOMED RESEARCH INTERNATIONAL 2021; 2021:6490118. [PMID: 34435048 PMCID: PMC8382518 DOI: 10.1155/2021/6490118] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 08/09/2021] [Indexed: 12/14/2022]
Abstract
The microarray cancer data obtained by DNA microarray technology play an important role for cancer prevention, diagnosis, and treatment. However, predicting the different types of tumors is a challenging task since the sample size in microarray data is often small but the dimensionality is very high. Gene selection, which is an effective means, is aimed at mitigating the curse of dimensionality problem and can boost the classification accuracy of microarray data. However, many of previous gene selection methods focus on model design, but neglect the correlation between different genes. In this paper, we introduce a novel unsupervised gene selection method by taking the gene correlation into consideration, named gene correlation guided gene selection (G3CS). Specifically, we calculate the covariance of different gene dimension pairs and embed it into our unsupervised gene selection model to regularize the gene selection coefficient matrix. In such a manner, redundant genes can be effectively excluded. In addition, we utilize a matrix factorization term to exploit the cluster structure of original microarray data to assist the learning process. We design an iterative updating algorithm with convergence guarantee to solve the resultant optimization problem. Experimental results on six publicly available microarray datasets are conducted to validate the efficacy of our proposed method.
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Fukushima A, Sugimoto M, Hiwa S, Hiroyasu T. Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles. BMC Bioinformatics 2021; 22:132. [PMID: 33736614 PMCID: PMC7977599 DOI: 10.1186/s12859-021-04052-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 02/28/2021] [Indexed: 12/14/2022] Open
Abstract
Background Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04052-4.
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Affiliation(s)
- Arika Fukushima
- Graduate School of Life and Medical Sciences, Doshisha University, Kyotanabe-shi, Kyoto, 610-0321, Japan
| | - Masahiro Sugimoto
- Research and Development Center for Minimally Invasive Therapies, Institute of Medical Science, Tokyo Medical University, Shinjuku, Tokyo, 160-8402, Japan.,Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, 997-0052, Japan
| | - Satoru Hiwa
- Faculty of Life and Medical Sciences, Doshisha University, Kyotanabe-shi, Kyoto, 610-0321, Japan
| | - Tomoyuki Hiroyasu
- Faculty of Life and Medical Sciences, Doshisha University, Kyotanabe-shi, Kyoto, 610-0321, Japan.
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Agrawal S, Ransom RF, Saraswathi S, Garcia-Gonzalo E, Webb A, Fernandez-Martinez JL, Popovic M, Guess AJ, Kloczkowski A, Benndorf R, Sadee W, Smoyer WE. Sulfatase 2 Is Associated with Steroid Resistance in Childhood Nephrotic Syndrome. J Clin Med 2021; 10:523. [PMID: 33540508 PMCID: PMC7867139 DOI: 10.3390/jcm10030523] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 01/20/2021] [Accepted: 01/23/2021] [Indexed: 01/17/2023] Open
Abstract
Glucocorticoid (GC) resistance complicates the treatment of ~10-20% of children with nephrotic syndrome (NS), yet the molecular basis for resistance remains unclear. We used RNAseq analysis and in silico algorithm-based approaches on peripheral blood leukocytes from 12 children both at initial NS presentation and after ~7 weeks of GC therapy to identify a 12-gene panel able to differentiate steroid resistant NS (SRNS) from steroid-sensitive NS (SSNS). Among this panel, subsequent validation and analyses of one biologically relevant candidate, sulfatase 2 (SULF2), in up to a total of 66 children, revealed that both SULF2 leukocyte expression and plasma arylsulfatase activity Post/Pre therapy ratios were greater in SSNS vs. SRNS. However, neither plasma SULF2 endosulfatase activity (measured by VEGF binding activity) nor plasma VEGF levels, distinguished SSNS from SRNS, despite VEGF's reported role as a downstream mediator of SULF2's effects in glomeruli. Experimental studies of NS-related injury in both rat glomeruli and cultured podocytes also revealed decreased SULF2 expression, which were partially reversible by GC treatment of podocytes. These findings together suggest that SULF2 levels and activity are associated with GC resistance in NS, and that SULF2 may play a protective role in NS via the modulation of downstream mediators distinct from VEGF.
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Affiliation(s)
- Shipra Agrawal
- Center for Clinical and Translational Research, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205, USA; (R.F.R.); (M.P.); (A.J.G.); (R.B.)
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43210, USA;
| | - Richard F. Ransom
- Center for Clinical and Translational Research, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205, USA; (R.F.R.); (M.P.); (A.J.G.); (R.B.)
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43210, USA;
| | - Saras Saraswathi
- Battelle Center for Mathematical Medicine at Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205, USA;
| | | | - Amy Webb
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH 43210, USA;
| | | | - Milan Popovic
- Center for Clinical and Translational Research, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205, USA; (R.F.R.); (M.P.); (A.J.G.); (R.B.)
| | - Adam J. Guess
- Center for Clinical and Translational Research, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205, USA; (R.F.R.); (M.P.); (A.J.G.); (R.B.)
| | - Andrzej Kloczkowski
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43210, USA;
- Battelle Center for Mathematical Medicine at Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205, USA;
| | - Rainer Benndorf
- Center for Clinical and Translational Research, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205, USA; (R.F.R.); (M.P.); (A.J.G.); (R.B.)
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43210, USA;
| | - Wolfgang Sadee
- Department of Cancer Biology and Genetics, Center for Pharmacogenomics, The Ohio State University College of Medicine, Columbus, OH 43210, USA;
| | - William E. Smoyer
- Center for Clinical and Translational Research, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205, USA; (R.F.R.); (M.P.); (A.J.G.); (R.B.)
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43210, USA;
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Al-Betar MA, Alomari OA, Abu-Romman SM. A TRIZ-inspired bat algorithm for gene selection in cancer classification. Genomics 2020; 112:114-126. [DOI: 10.1016/j.ygeno.2019.09.015] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Revised: 09/05/2019] [Accepted: 09/17/2019] [Indexed: 10/25/2022]
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Sharma A, Rani R. C-HMOSHSSA: Gene selection for cancer classification using multi-objective meta-heuristic and machine learning methods. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 178:219-235. [PMID: 31416551 DOI: 10.1016/j.cmpb.2019.06.029] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2019] [Revised: 06/24/2019] [Accepted: 06/27/2019] [Indexed: 05/21/2023]
Abstract
BACKGROUND AND OBJECTIVE Over the last two decades, DNA microarray technology has emerged as a powerful tool for early cancer detection and prevention. It helps to provide a detailed overview of disease complex microenvironment. Moreover, online availability of thousands of gene expression assays made microarray data classification an active research area. A common goal is to find a minimum subset of genes and maximizing the classification accuracy. METHODS In pursuit of a similar objective, we have proposed framework (C-HMOSHSSA) for gene selection using multi-objective spotted hyena optimizer (MOSHO) and salp swarm algorithm (SSA). The real-life optimization problems with more than one objective usually face the challenge to maintain convergence and diversity. Salp Swarm Algorithm (SSA) maintains diversity but, suffers from the overhead of maintaining the necessary information. On the other hand, the calculation of MOSHO requires low computational efforts hence is used for maintaining the necessary information. Therefore, the proposed algorithm is a hybrid algorithm that utilizes the features of both SSA and MOSHO to facilitate its exploration and exploitation capability. RESULTS Four different classifiers are trained on seven high-dimensional datasets using a subset of features (genes), which are obtained after applying the proposed hybrid gene selection algorithm. The results show that the proposed technique significantly outperforms existing state-of-the-art techniques. CONCLUSION It is also shown that the new sets of informative and biologically relevant genes are successfully identified by the proposed technique. The proposed approach can also be applied to other problem domains of interest which involve feature selection.
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Affiliation(s)
- Aman Sharma
- Computer Science and Engineering Department, Thapar Institute of Engineering & Technology, Patiala, Punjab, India.
| | - Rinkle Rani
- Computer Science and Engineering Department, Thapar Institute of Engineering & Technology, Patiala, Punjab, India.
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Gene selection using hybrid binary black hole algorithm and modified binary particle swarm optimization. Genomics 2019; 111:669-686. [DOI: 10.1016/j.ygeno.2018.04.004] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Revised: 04/04/2018] [Accepted: 04/09/2018] [Indexed: 12/11/2022]
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Gene selection for microarray data classification via adaptive hypergraph embedded dictionary learning. Gene 2019; 706:188-200. [DOI: 10.1016/j.gene.2019.04.060] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Revised: 04/03/2019] [Accepted: 04/22/2019] [Indexed: 01/19/2023]
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Alomari OA, Khader AT, Al-Betar MA, Awadallah MA. A novel gene selection method using modified MRMR and hybrid bat-inspired algorithm with β-hill climbing. APPL INTELL 2018. [DOI: 10.1007/s10489-018-1207-1] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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17
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Gene selection for microarray data classification via subspace learning and manifold regularization. Med Biol Eng Comput 2017; 56:1271-1284. [PMID: 29256006 DOI: 10.1007/s11517-017-1751-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Accepted: 11/03/2017] [Indexed: 10/18/2022]
Abstract
With the rapid development of DNA microarray technology, large amount of genomic data has been generated. Classification of these microarray data is a challenge task since gene expression data are often with thousands of genes but a small number of samples. In this paper, an effective gene selection method is proposed to select the best subset of genes for microarray data with the irrelevant and redundant genes removed. Compared with original data, the selected gene subset can benefit the classification task. We formulate the gene selection task as a manifold regularized subspace learning problem. In detail, a projection matrix is used to project the original high dimensional microarray data into a lower dimensional subspace, with the constraint that the original genes can be well represented by the selected genes. Meanwhile, the local manifold structure of original data is preserved by a Laplacian graph regularization term on the low-dimensional data space. The projection matrix can serve as an importance indicator of different genes. An iterative update algorithm is developed for solving the problem. Experimental results on six publicly available microarray datasets and one clinical dataset demonstrate that the proposed method performs better when compared with other state-of-the-art methods in terms of microarray data classification. Graphical Abstract The graphical abstract of this work.
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Pashaei E, Ozen M, Aydin N. Biomarker discovery based on BBHA and AdaboostM1 on microarray data for cancer classification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:3080-3083. [PMID: 28268962 DOI: 10.1109/embc.2016.7591380] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In this paper, a new approach based on Binary Black Hole Algorithm (BBHA) and Adaptive Boosting version Ml (AdaboostM1) is proposed for finding genes that can classify the group of cancers correctly. In this approach, BBHA is used to perform gene selection and AdaboostM1 with 10-fold cross validation is adopted as the classifier. Also, to find the relation between the biomarkers for biological point of view, decision tree algorithm (C4.5) is utilized. The proposed approach is tested on three benchmark microarrays. The experimental results show that our proposed method can select the most informative gene subsets by reducing the dimension of the data set and improve classification accuracy as compared to several recent studies.
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Sindhu R, Ngadiran R, Yacob YM, Zahri NAH, Hariharan M. Sine–cosine algorithm for feature selection with elitism strategy and new updating mechanism. Neural Comput Appl 2017. [DOI: 10.1007/s00521-017-2837-7] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Motieghader H, Najafi A, Sadeghi B, Masoudi-Nejad A. A hybrid gene selection algorithm for microarray cancer classification using genetic algorithm and learning automata. INFORMATICS IN MEDICINE UNLOCKED 2017. [DOI: 10.1016/j.imu.2017.10.004] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
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Zhongxin W, Gang S, Jing Z, Jia Z. Feature Selection Algorithm Based on Mutual Information and Lasso for Microarray Data. ACTA ACUST UNITED AC 2016. [DOI: 10.2174/1874070701610010278] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
With the development of microarray technology, massive microarray data is produced by gene expression experiments, and it provides a new approach for the study of human disease. Due to the characteristics of high dimensionality, much noise and data redundancy for microarray data, it is difficult to my knowledge from microarray data profoundly and accurately,and it also brings enormous difficulty for information genes selection. Therefore, a new feature selection algorithm for high dimensional microarray data is proposed in this paper, which mainly involves two steps. In the first step, mutual information method is used to calculate all genes, and according to the mutual information value, information genes is selected as candidate genes subset and irrelevant genes are filtered. In the second step, an improved method based on Lasso is used to select information genes from candidate genes subset, which aims to remove the redundant genes. Experimental results show that the proposed algorithm can select fewer genes, and it has better classification ability, stable performance and strong generalization ability. It is an effective genes feature selection algorithm.
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Feature Subset Selection for Cancer Classification Using Weight Local Modularity. Sci Rep 2016; 6:34759. [PMID: 27703256 PMCID: PMC5050509 DOI: 10.1038/srep34759] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Accepted: 09/19/2016] [Indexed: 11/27/2022] Open
Abstract
Microarray is recently becoming an important tool for profiling the global gene expression patterns of tissues. Gene selection is a popular technology for cancer classification that aims to identify a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers to obtain a high predictive accuracy. This technique has been extensively studied in recent years. This study develops a novel feature selection (FS) method for gene subset selection by utilizing the Weight Local Modularity (WLM) in a complex network, called the WLMGS. In the proposed method, the discriminative power of gene subset is evaluated by using the weight local modularity of a weighted sample graph in the gene subset where the intra-class distance is small and the inter-class distance is large. A higher local modularity of the gene subset corresponds to a greater discriminative of the gene subset. With the use of forward search strategy, a more informative gene subset as a group can be selected for the classification process. Computational experiments show that the proposed algorithm can select a small subset of the predictive gene as a group while preserving classification accuracy.
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Abstract
BACKGROUND Development of biologically relevant models from gene expression data notably, microarray data has become a topic of great interest in the field of bioinformatics and clinical genetics and oncology. Only a small number of gene expression data compared to the total number of genes explored possess a significant correlation with a certain phenotype. Gene selection enables researchers to obtain substantial insight into the genetic nature of the disease and the mechanisms responsible for it. Besides improvement of the performance of cancer classification, it can also cut down the time and cost of medical diagnoses. METHODS This study presents a modified Artificial Bee Colony Algorithm (ABC) to select minimum number of genes that are deemed to be significant for cancer along with improvement of predictive accuracy. The search equation of ABC is believed to be good at exploration but poor at exploitation. To overcome this limitation we have modified the ABC algorithm by incorporating the concept of pheromones which is one of the major components of Ant Colony Optimization (ACO) algorithm and a new operation in which successive bees communicate to share their findings. RESULTS The proposed algorithm is evaluated using a suite of ten publicly available datasets after the parameters are tuned scientifically with one of the datasets. Obtained results are compared to other works that used the same datasets. The performance of the proposed method is proved to be superior. CONCLUSION The method presented in this paper can provide subset of genes leading to more accurate classification results while the number of selected genes is smaller. Additionally, the proposed modified Artificial Bee Colony Algorithm could conceivably be applied to problems in other areas as well.
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Affiliation(s)
| | - Rameen Shakur
- Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Mohammad Kaykobad
- A ℓEDA Group, Department of CSE, BUET, Dhaka-1205, Dhaka, Bangladesh
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A Feature Selection Method for Large-Scale Network Traffic Classification Based on Spark. INFORMATION 2016. [DOI: 10.3390/info7010006] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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Sachnev V, Saraswathi S, Niaz R, Kloczkowski A, Suresh S. Multi-class BCGA-ELM based classifier that identifies biomarkers associated with hallmarks of cancer. BMC Bioinformatics 2015; 16:166. [PMID: 25986937 PMCID: PMC4448565 DOI: 10.1186/s12859-015-0565-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2015] [Accepted: 03/31/2015] [Indexed: 12/05/2022] Open
Abstract
Background Traditional cancer treatments have centered on cytotoxic drugs and general purpose chemotherapy that may not be tailored to treat specific cancers. Identification of molecular markers that are related to different types of cancers might lead to discovery of drugs that are patient and disease specific. This study aims to use microarray gene expression cancer data to identify biomarkers that are indicative of different types of cancers. Our aim is to provide a multi-class cancer classifier that can simultaneously differentiate between cancers and identify type-specific biomarkers, through the application of the Binary Coded Genetic Algorithm (BCGA) and a neural network based Extreme Learning Machine (ELM) algorithm. Results BCGA and ELM are combined and used to select a subset of genes that are present in the Global Cancer Mapping (GCM) data set. This set of candidate genes contains over 52 biomarkers that are related to multiple cancers, according to the literature. They include APOA1, VEGFC, YWHAZ, B2M, EIF2S1, CCR9 and many other genes that have been associated with the hallmarks of cancer. BCGA-ELM is tested on several cancer data sets and the results are compared to other classification methods. BCGA-ELM compares or exceeds other algorithms in terms of accuracy. We were also able to show that over 50% of genes selected by BCGA-ELM on GCM data are cancer related biomarkers. Conclusions We were able to simultaneously differentiate between 14 different types of cancers, using only 92 genes, to achieve a multi-class classification accuracy of 95.4% which is between 21.6% and 38% higher than other results in the literature for multi-class cancer classification. Our findings suggest that computational algorithms such as BCGA-ELM can facilitate biomarker-driven integrated cancer research that can lead to a detailed understanding of the complexities of cancer. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0565-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Vasily Sachnev
- Department of Information, Communication and Electronics Engineering, Catholic University of Korea, Bucheon, Republic of Korea.
| | - Saras Saraswathi
- Battelle Center for Mathematical Medicine at The Research Institute at Nationwide Children's Hospital; currently at Sidra, Medical and Research Center, Doha, Qatar.
| | - Rashid Niaz
- Department of Medical Informatics, Sidra Medical and Research Center, Doha, Qatar.
| | - Andrzej Kloczkowski
- Battelle Center for Mathematical Medicine at The Research Institute at Nationwide Children's Hospital; Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, USA.
| | - Sundaram Suresh
- School of Computer Science, Nanyang Technological University, Nanyang, Singapore.
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Chuang LY, Lane HY, Lin YD, Lin MT, Yang CH, Chang HW. Identification of SNP barcode biomarkers for genes associated with facial emotion perception using particle swarm optimization algorithm. Ann Gen Psychiatry 2014; 13:15. [PMID: 24955105 PMCID: PMC4050220 DOI: 10.1186/1744-859x-13-15] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2014] [Accepted: 04/23/2014] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Facial emotion perception (FEP) can affect social function. We previously reported that parts of five tested single-nucleotide polymorphisms (SNPs) in the MET and AKT1 genes may individually affect FEP performance. However, the effects of SNP-SNP interactions on FEP performance remain unclear. METHODS This study compared patients with high and low FEP performances (n = 89 and 93, respectively). A particle swarm optimization (PSO) algorithm was used to identify the best SNP barcodes (i.e., the SNP combinations and genotypes that revealed the largest differences between the high and low FEP groups). RESULTS The analyses of individual SNPs showed no significant differences between the high and low FEP groups. However, comparisons of multiple SNP-SNP interactions involving different combinations of two to five SNPs showed that the best PSO-generated SNP barcodes were significantly associated with high FEP score. The analyses of the joint effects of the best SNP barcodes for two to five interacting SNPs also showed that the best SNP barcodes had significantly higher odds ratios (2.119 to 3.138; P < 0.05) compared to other SNP barcodes. In conclusion, the proposed PSO algorithm effectively identifies the best SNP barcodes that have the strongest associations with FEP performance. CONCLUSIONS This study also proposes a computational methodology for analyzing complex SNP-SNP interactions in social cognition domains such as recognition of facial emotion.
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Affiliation(s)
- Li-Yeh Chuang
- Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung 84001, Taiwan
| | - Hsien-Yuan Lane
- Institute of Clinical Medical Science, China Medical University, Taichung 40402, Taiwan ; Department of Psychiatry, China Medical University Hospital, Taichung 40402, Taiwan
| | - Yu-Da Lin
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 80778, Taiwan
| | - Ming-Teng Lin
- Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung 84001, Taiwan ; Department of Psychiatry, Taipei Veterans General Hospital, Hsinchu Branch, Hsinchu 31064, Taiwan
| | - Cheng-Hong Yang
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 80778, Taiwan
| | - Hsueh-Wei Chang
- Cancer Center, Translational Research Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan ; Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung 80424, Taiwan ; Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
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Torrente A, López-Pintado S, Romo J. DepthTools: an R package for a robust analysis of gene expression data. BMC Bioinformatics 2013; 14:237. [PMID: 23885712 PMCID: PMC3750619 DOI: 10.1186/1471-2105-14-237] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2013] [Accepted: 07/17/2013] [Indexed: 11/18/2022] Open
Abstract
Background The use of DNA microarrays and oligonucleotide chips of high density in modern biomedical research provides complex, high dimensional data which have been proven to convey crucial information about gene expression levels and to play an important role in disease diagnosis. Therefore, there is a need for developing new, robust statistical techniques to analyze these data. Results depthTools is an R package for a robust statistical analysis of gene expression data, based on an efficient implementation of a feasible notion of depth, the Modified Band Depth. This software includes several visualization and inference tools successfully applied to high dimensional gene expression data. A user-friendly interface is also provided via an R-commander plugin. Conclusion We illustrate the utility of the depthTools package, that could be used, for instance, to achieve a better understanding of genome-level variation between tumors and to facilitate the development of personalized treatments.
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Affiliation(s)
- Aurora Torrente
- Functional Genomics Team, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK.
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Mohamad MS, Omatu S, Deris S, Yoshioka M, Abdullah A, Ibrahim Z. An enhancement of binary particle swarm optimization for gene selection in classifying cancer classes. Algorithms Mol Biol 2013; 8:15. [PMID: 23617960 PMCID: PMC3847130 DOI: 10.1186/1748-7188-8-15] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2012] [Accepted: 04/16/2013] [Indexed: 11/10/2022] Open
Abstract
Background Gene expression data could likely be a momentous help in the progress of proficient cancer diagnoses and classification platforms. Lately, many researchers analyze gene expression data using diverse computational intelligence methods, for selecting a small subset of informative genes from the data for cancer classification. Many computational methods face difficulties in selecting small subsets due to the small number of samples compared to the huge number of genes (high-dimension), irrelevant genes, and noisy genes. Methods We propose an enhanced binary particle swarm optimization to perform the selection of small subsets of informative genes which is significant for cancer classification. Particle speed, rule, and modified sigmoid function are introduced in this proposed method to increase the probability of the bits in a particle’s position to be zero. The method was empirically applied to a suite of ten well-known benchmark gene expression data sets. Results The performance of the proposed method proved to be superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also requires lower computational time compared to BPSO.
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