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Abo Alchamlat S, Farnir F. Aggregation of experts: an application in the field of "interactomics" (detection of interactions on the basis of genomic data). BMC Bioinformatics 2018; 19:445. [PMID: 30497383 PMCID: PMC6267805 DOI: 10.1186/s12859-018-2447-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Accepted: 10/25/2018] [Indexed: 12/03/2022] Open
Abstract
Background Despite the successful mapping of genes involved in the determinism of numerous traits, a large part of the genetic variation remains unexplained. A possible explanation is that the simple models used in many studies might not properly fit the actual underlying situations. Consequently, various methods have attempted to deal with the simultaneous mapping of genomic regions, assuming that these regions might interact, leading to a complex determinism for various traits. Despite some successes, no gold standard methodology has emerged. Actually, combining several interaction mapping methods might be a better strategy, leading to positive results over a larger set of situations. Our work is a step in that direction. Results We first have demonstrated why aggregating results from several distinct methods might increase the statistical power while controlling the type I error. We have illustrated the approach using 6 existing methods (namely: MDR, Boost, BHIT, KNN-MDR, MegaSNPHunter and AntEpiSeeker) on simulated and real data sets. We have used a very simple aggregation strategy: a majority vote across the best loci combinations identified by the individual methods. In order to assess the performances of our aggregation approach in problems where most individual methods tend to fail, we have simulated difficult situations where no marginal effects of individual genes exist and where genetic heterogeneity is present. we have also demonstrated the use of the strategy on real data, using a WTCCC dataset on rheumatoid arthritis. Since we have been using simplistic assumptions to infer the expected power of the aggregation method, the actual power we estimated from our simulations has turned out to be a bit smaller than theoretically expected. Results nevertheless have shown that grouping the results of several methods is advantageous in terms of power, accuracy and type I error control. Furthermore, as more methods should become available in the future, using a grouping strategy will become more advantageous since adding more methods seems to improve the performances of the aggregated method. Conclusions The aggregation of methods as a tool to detect genetic interactions is a potentially useful addition to the arsenal used in complex traits analyses. Electronic supplementary material The online version of this article (10.1186/s12859-018-2447-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sinan Abo Alchamlat
- Department of Biostatistics, Faculty of Veterinary Medicine, University of Liège, Sart Tilman B43, 4000, Liege, Belgium
| | - Frédéric Farnir
- Department of Biostatistics, Faculty of Veterinary Medicine, University of Liège, Sart Tilman B43, 4000, Liege, Belgium.
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Wang J, Sun T, Gao N, Menon DD, Luo Y, Gao Q, Li X, Wang W, Zhu H, Lv P, Liang Z, Tao L, Liu X, Guo X. Contourlet textual features: improving the diagnosis of solitary pulmonary nodules in two dimensional CT images. PLoS One 2014; 9:e108465. [PMID: 25250576 PMCID: PMC4177406 DOI: 10.1371/journal.pone.0108465] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2013] [Accepted: 08/29/2014] [Indexed: 01/04/2023] Open
Abstract
Objective To determine the value of contourlet textural features obtained from solitary pulmonary nodules in two dimensional CT images used in diagnoses of lung cancer. Materials and Methods A total of 6,299 CT images were acquired from 336 patients, with 1,454 benign pulmonary nodule images from 84 patients (50 male, 34 female) and 4,845 malignant from 252 patients (150 male, 102 female). Further to this, nineteen patient information categories, which included seven demographic parameters and twelve morphological features, were also collected. A contourlet was used to extract fourteen types of textural features. These were then used to establish three support vector machine models. One comprised a database constructed of nineteen collected patient information categories, another included contourlet textural features and the third one contained both sets of information. Ten-fold cross-validation was used to evaluate the diagnosis results for the three databases, with sensitivity, specificity, accuracy, the area under the curve (AUC), precision, Youden index, and F-measure were used as the assessment criteria. In addition, the synthetic minority over-sampling technique (SMOTE) was used to preprocess the unbalanced data. Results Using a database containing textural features and patient information, sensitivity, specificity, accuracy, AUC, precision, Youden index, and F-measure were: 0.95, 0.71, 0.89, 0.89, 0.92, 0.66, and 0.93 respectively. These results were higher than results derived using the database without textural features (0.82, 0.47, 0.74, 0.67, 0.84, 0.29, and 0.83 respectively) as well as the database comprising only textural features (0.81, 0.64, 0.67, 0.72, 0.88, 0.44, and 0.85 respectively). Using the SMOTE as a pre-processing procedure, new balanced database generated, including observations of 5,816 benign ROIs and 5,815 malignant ROIs, and accuracy was 0.93. Conclusion Our results indicate that the combined contourlet textural features of solitary pulmonary nodules in CT images with patient profile information could potentially improve the diagnosis of lung cancer.
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Affiliation(s)
- Jingjing Wang
- School of Public Health, Capital Medical University, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Tao Sun
- School of Public Health, Capital Medical University, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Ni Gao
- School of Public Health, Capital Medical University, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Desmond Dev Menon
- School of Medical Sciences, Edith Cowan University, Perth, Australia
- School of Exercise and Health Sciences, Edith Cowan University, Perth, Australia
| | - Yanxia Luo
- School of Public Health, Capital Medical University, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Qi Gao
- School of Public Health, Capital Medical University, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Xia Li
- School of Public Health, Capital Medical University, Beijing, China
- Department of Epidemiology & Public Health, University College Cork, Cork, Ireland
| | - Wei Wang
- School of Public Health, Capital Medical University, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
- School of Medical Sciences, Edith Cowan University, Perth, Australia
| | - Huiping Zhu
- School of Public Health, Capital Medical University, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Pingxin Lv
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Zhigang Liang
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Lixin Tao
- School of Public Health, Capital Medical University, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Xiangtong Liu
- School of Public Health, Capital Medical University, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Xiuhua Guo
- School of Public Health, Capital Medical University, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
- * E-mail:
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