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Geetha A, Gomathi N. A robust grey wolf-based deep learning for brain tumour detection in MR images. ACTA ACUST UNITED AC 2020; 65:191-207. [DOI: 10.1515/bmt-2018-0244] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Accepted: 08/06/2019] [Indexed: 11/15/2022]
Abstract
AbstractIn recent times, the detection of brain tumours has become more common. Generally, a brain tumour is an abnormal mass of tissue where the cells grow uncontrollably and are apparently unregulated by the mechanisms that control cells. A number of techniques have been developed thus far; however, the time needed in a detecting brain tumour is still a challenge in the field of image processing. This article proposes a new accurate detection model. The model includes certain processes such as preprocessing, segmentation, feature extraction and classification. Particularly, two extreme processes such as contrast enhancement and skull stripping are processed under the initial phase. In the segmentation process, we used the fuzzy means clustering (FCM) algorithm. Both the grey co-occurrence matrix (GLCM) as well as the grey-level run-length matrix (GRLM) features were extracted in the feature extraction phase. Moreover, this paper uses a deep belief network (DBN) for classification. The optimized DBN concept is used here, for which grey wolf optimisation (GWO) is used. The proposed model is termed the GW-DBN model. The proposed model compares its performance over other conventional methods in terms of accuracy, specificity, sensitivity, precision, negative predictive value (NPV), the F1Score and Matthews correlation coefficient (MCC), false negative rate (FNR), false positive rate (FPR) and false discovery rate (FDR), and proves the superiority of the proposed work.
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Affiliation(s)
- A. Geetha
- VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Velachery, Chennai 600042, Tamil Nadu, India
| | - N. Gomathi
- VelTech Dr. Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai 600062, Tamil Nadu, India
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Hung FH, Chiu HW. Cancer subtype prediction from a pathway-level perspective by using a support vector machine based on integrated gene expression and protein network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 141:27-34. [PMID: 28241965 DOI: 10.1016/j.cmpb.2017.01.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2016] [Revised: 01/06/2017] [Accepted: 01/16/2017] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE Distinguishing cancer subtypes is critical for selecting the appropriate treatment strategy. Bioinformatics approaches have gradually taken the place of clinical observations and pathological experiments. However, these approaches are typically only used in gene expression profiling. Previous studies have primarily focused on the gene level or specific diseases, and thus pathway-level factors have not been considered. Therefore, a computational method that integrates gene expression and pathway is necessary. METHODS This study presented an approach to determine potential fragments of activated pathways around protein networks in different stages of disease. We used a scored equation that integrates genomic and proteomic information and determined the intensity of the pathway link change. A support vector machine (SVM) was used to train and test subtype-predicted models. RESULTS The performance of the proposed method was evaluated by calculating prediction accuracy. The average prediction accuracy was 67.64% for three subtypes in tumors of neuroepithelial tissues. The results demonstrate that the proposed method applies fewer features than gene expression methods used to obtain similar results CONCLUSIONS: This study suggests a method to implement a cancer subtype classifier based on an SVM from a pathway-level perspective.
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Affiliation(s)
- Fei-Hung Hung
- Graduate Institute of Biomedical Informatics, Taipei Medical University, 250 Wu-Hsing Street, Taipei 11031, Taiwan
| | - Hung-Wen Chiu
- Graduate Institute of Biomedical Informatics, Taipei Medical University, 250 Wu-Hsing Street, Taipei 11031, Taiwan.
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Bilkey J, Tata A, McKee TD, Porcari AM, Bluemke E, Woolman M, Ventura M, Eberlin MN, Zarrine-Afsar A. Variations in the Abundance of Lipid Biomarker Ions in Mass Spectrometry Images Correlate to Tissue Density. Anal Chem 2016; 88:12099-12107. [PMID: 28193010 DOI: 10.1021/acs.analchem.6b02767] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
While mass spectrometry (MS) imaging is widely used to investigate the molecular composition of ex vivo slices of cancerous tumors, little is known about how variations in the cellular properties of cancer tissue can influence cancer biomarker ion images. To better understand the basis for variations in the abundances of cancer biomarker ions seen in MS images of relatively homogeneous ex vivo tumor samples, sections of snap frozen human breast cancer murine xenografts were subjected to desorption electrospray ionization mass spectrometry (DESI-MS) imaging. Serial sections were then stained with hematoxylin and eosin (H&E) and subjected to detailed morphometric cellular analysis, using a commercial digital pathology platform augmented with custom-tailored image analysis algorithms developed in-house. Gross morphological heterogeneities due to stroma, vasculature, and noncancer cells were mapped in the tumor and found to not correlate with the areas of suppressed cancer biomarker abundance. Instead, the ion abundances of major breast cancer biomarkers were found to correlate with the cytoplasmic area of cancer cells that comprised the tumor tissue. Therefore, detailed cellular analyses can be used to rationalize subtle heterogeneities in ion abundance in MS images, not explained by the presence of gross morphological heterogeneities such as stroma.
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Affiliation(s)
- Jade Bilkey
- STTARR Innovation Centre, Princess Margaret Cancer Centre, 101 College Street, Toronto, Ontario M5G 1L7, Canada
| | - Alessandra Tata
- Techna Institute for the Advancement of Technology for Health, University Health Network , Toronto, Ontario M5G-1P5, Canada
| | - Trevor D McKee
- STTARR Innovation Centre, Princess Margaret Cancer Centre, 101 College Street, Toronto, Ontario M5G 1L7, Canada
| | - Andreia M Porcari
- ThoMSon Mass Spectrometry Laboratory, Institute of Chemistry, University of Campinas , Campinas, SP Brazil
| | - Emma Bluemke
- Techna Institute for the Advancement of Technology for Health, University Health Network , Toronto, Ontario M5G-1P5, Canada
| | - Michael Woolman
- Techna Institute for the Advancement of Technology for Health, University Health Network , Toronto, Ontario M5G-1P5, Canada
| | - Manuela Ventura
- Techna Institute for the Advancement of Technology for Health, University Health Network , Toronto, Ontario M5G-1P5, Canada
| | - Marcos N Eberlin
- ThoMSon Mass Spectrometry Laboratory, Institute of Chemistry, University of Campinas , Campinas, SP Brazil
| | - Arash Zarrine-Afsar
- Techna Institute for the Advancement of Technology for Health, University Health Network , Toronto, Ontario M5G-1P5, Canada.,Department of Medical Biophysics, University of Toronto ,101 College Street Suite 15-701, Toronto, Ontario M5G 1L7, Canada.,Department of Surgery, University of Toronto , 149 College Street, Toronto, Ontario M5T-1P5, Canada.,Keenan Research Centre for Biomedical Science, Li Ka-Shing Knowledge Institute, St. Michael's Hospital , 30 Bond Street, Toronto, Ontario M5B-1W8, Canada
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Du YM, Hu Y, Xia Y, Ouyang Z. Power Normalization for Mass Spectrometry Data Analysis and Analytical Method Assessment. Anal Chem 2016; 88:3156-63. [PMID: 26882462 PMCID: PMC8135100 DOI: 10.1021/acs.analchem.5b04418] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Biomarker profiling using mass spectrometry plays an essential role in biological studies and is highly dependent on the data analysis for sample classification. In this study, we introduced power nomination of the mass spectra as a method for systematically altering the weights of peaks at different intensity levels. In combination with the use of support vector machine method (SVM), the impact on the sample classification has been characterized using data in four studies previously reported, including the distinctions of anomeric configurations of sugars, types of bacteria, stages of melanoma, and the types of breast cancer. Comprehensive analysis of the data with normalization at different power normalization index (PNI) was developed and analysis tools, including error-PNI plots, reference profiles, and error source profiles, were used to assess the potential of the analytical methods as well as to find the proper approaches to classify the samples.
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Affiliation(s)
- Y. Melodie Du
- Weldon School of Biomedical Engineering, Purdue University, 206 South Martin Jischke Drive, West Lafayette, Indiana 47907, United States
| | - Ye Hu
- Department of Nanomedicine, Houston Methodist Research Institute, 6565 Fannin Street, Houston, Texas 77030, United States
| | - Yu Xia
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Zheng Ouyang
- Weldon School of Biomedical Engineering, Purdue University, 206 South Martin Jischke Drive, West Lafayette, Indiana 47907, United States
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
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