Deeper Integrative Neural Network Analysis for Multi-level Omics Data
2
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
Recently, various machine learning methods have emerged for analyzing and interpreting the ever-expanding genetic data. In addition, new analytical tools for machine learning using statistical models, are being developed. Lim et al. [1] proposed the integrative deep learning to find the differentially expressed (DE) biomarkers using deep neural network with a single hidden layer. This method consists of the input layer, a hidden layer, the consolidation layer, and the output layer. They found that integrative deep learning method is stable and robust for analysis of the variation in the simulation datasets. In this study, we expanded the integrative deep learning method by including an additional hidden layer. The present expanded method consists of the input layer, two hidden layers, the consolidation layer and the output layer. The purpose of this study is to investigate the effect of the additional hidden layers on the performance of the previous method (integrative deep learning). We conducted a simulation study and compared the results with those from deep neural network with one hidden layer.
Abstract
Recently, various machine learning methods have emerged for analyzing and interpreting the ever-expanding genetic data. In addition, new analytical tools for machine learning using statistical models, are being developed. Lim et al. [1] proposed the integrative deep learning to find the differentially expressed (DE) biomarkers using deep neural network with a single hidden layer. This method consists of the input layer, a hidden layer, the consolidation layer, and the output layer. They found that integrative deep learning method is stable and robust for analysis of the variation in the simulation datasets. In this study, we expanded the integrative deep learning method by including an additional hidden layer. The present expanded method consists of the input layer, two hidden layers, the consolidation layer and the output layer. The purpose of this study is to investigate the effect of the additional hidden layers on the performance of the previous method (integrative deep learning). We conducted a simulation study and compared the results with those from deep neural network with one hidden layer.
참고문헌 (29)
- Lim J, "Integrative deep learning for identifying differentially expressed (DE) biomarkers", Comput Math Method M, 2019, pp.10~ (2019)
- Kim J., "Integrative deep learning for identifying differentially expressed(DE) biomarkers", Master’s
- Yao X, "Evolving artificial neural networks", P IEEE, 87(9), pp.1423~1447 (1999)
- Hasin Y, "Multi-omics approaches to disease", Genome Bio, 18, pp.83~ (2017)
- Bebek G, "Network biology methods integrating biological data for translational science", Brief Bioinformatics, 13, pp.446~459 (2012)
- Xu L, "Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data", Bioinformatics, 21, pp.3905~3911 (2015)
- Kim S, "Integrative clustering of multi-level omics data for disease subtype discovery using sequential double regularization", Bioinformatics, 18, pp.165~179 (2017)
- SungHwan Kim, "Meta-analytic support vector machine for integrating multiple omics data", BioData Mining, 10(1) (2017), Springer Science and Business Media LLC[DOI]
- Ma T, "MetaOmics:Analysis pipeline and browser-based software suite for transcriptomic meta-analysis", Bioinformatics, 35, pp.1597~1599 (2019)
- Kim S, "MetaKTSP: A meta-analytic top scoring pair method for robust cross-study validation of omics prediction analysis", Bioinformatics, 32, pp.1966~1973 (2016)
- Kim S, "Meta-analytic principal component analysis in integrative omics application", Bioinformatics, 34, pp.1321~1328 (2018)
- Swan AL, "Application of machine learning to proteomics data: Classification and biomarker identification in postgenomics biology", Omics, 17, pp.595~610 (2013)
- Min-Seok Kwon, "Integrative analysis of multi-omics data for identifying multi-markers for diagnosing pancreatic cancer", BMC Genomics, 16(Suppl 9), pp.S4~ (2015), Springer Science and Business Media LLC[DOI]
- 김상철, "Identification of Differentially Expressed Genes Using Tests Based on Multiple Imputations", Quantitative Bio-Science, 36(1), pp.23~31 (2017), 자연과학연구소[DOI]
- 김동순, "국내외 애널리스트들의 투자의견 및 목표주가 변경이 주가에 미치는 영향력 분석", Asia-Pacific Journal of Financial Studies, 35(2), pp.75~108 (2006), 한국증권학회
- 김동순, "베스트 애널리스트가 이익예측능력이 우수하고 주가영향력이 더 큰가?", 대한경영학회지, 22(1), pp.27~48 (2009), 대한경영학회
- 박진우, "애널리스트 활동수준에 따른 애널리스트보고서의 정보력과 투자주체별 정보비대칭", 한국증권학회지, 41(4), pp.547~588 (2012), 한국증권학회
- 서윤범, "Predicting Bitcoin Market Trend with Deep Learning Models", Quantitative Bio-Science, 37(1), pp.65~71 (2018), 자연과학연구소[DOI]
- Saaty TL, "Decision making for leaders", IEEE Trans Syst Man Cybern Syst, 3, pp.450~452 (1985)
- Saaty TL, "Decision-making with the AHP: Why is the principal eigenvector necessary", Eur J Oper Res, 145, pp.85~91 (2003)
- Ngai EWT, "Evaluation of knowledge management tools using AHP", Expert Syst Appl, 29, pp.889~899 (2005)
- Nagesha N, "Barriers to energy efficiency in small industry clusters: multi-criteria-based prioritization using the analytic hierarchy process", Energy, 31, pp.1969~1983 (2006)
- Bottero M, "The use of the analytic hierarchy process for the comparison between microtunnelling and trench excavation", Tunn Undergr Sp Tech, 20, pp.501~513 (2005)
- Vaidya OS, "Analytic hierarchy process: An overview of applications", Eur J Oper Res, 169, pp.1~29 (2006)
- Erensal YC, "Determining key capabilities in technology management using fuzzy analytic hierarchy process: A case study of Turkey", Inform Sciences, 176, pp.2755~2770 (2006)
- Lee SS, "A commercial paper evaluation model based on the AHP", KORMS, 5, pp.97~115 (1998)
- 정현순, "기업신용평가시스템을 위한 AHP 모형의 개발", 경영과학, 20(2), pp.165~177 (2003), 한국경영과학회
- Kurttila M, "Utilizing the analytic hierarchy process (AHP) in SWOT analysis - a hybrid method and its application to a forest-certification case", For Policy Econ, 1, pp.41~52 (2000)
- Zeidan R, "Developing a sustainability credit score system", J Bus Ethics, 127, pp.283~296 (2015)