基于多组学数据的流行病学研究策略及其在乳腺癌研究中的应用_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

 李佳圆 , 郝宇 , 吴雪瑶

四川大学华西公共卫生学院/四川大学华西第四医院（成都 610041）;

Corresponding author：

李佳圆, Email: lijiayuan@scu.edu.cn

Keywords：

DOI：

10.7507/1007-9424.202009072

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Citation： 李佳圆, 郝宇, 吴雪瑶. 基于多组学数据的流行病学研究策略及其在乳腺癌研究中的应用. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2020, 27(11): 1344-1347. doi: 10.7507/1007-9424.202009072 Copy

1.	Taubes G. Epidemiology faces its limits. Science, 1995, 269(5221): 164-169.
2.	Haring R, Wallaschofski H. Diving through the “-omics”: the case for deep phenotyping and systems epidemiology. Omics, 2012, 16(5): 231-234.
3.	Hu FB. Metabolic profiling of diabetes: from black-box epidemiology to systems epidemiology. Clin Chem, 2011, 57(9): 1224-1226.
4.	Dammann O, Gray P, Gressens P, et al. Systems epidemiology: What’s in a name? Online J Public Health Inform, 2014, 6(3): e198.
5.	黄涛, 李立明. 系统流行病学. 中华流行病学杂志, 2018, 39(5): 694-699.
6.	Claussnitzer M, Dankel SN, Kim KH, et al. FTO obesity variant circuitry and adipocyte browning in humans. N Engl J Med, 2015, 373(10): 895-907.
7.	Paul H. Data-driven hypotheses. EMBO Reports, 2013, 14(2): 104.
8.	Pinu FR, Beale DJ, Paten AM, et al. Systems biology and multi-omics integration: viewpoints from the metabolomics research community. Metabolites, 2019, 9(4): 76.
9.	Ebbels T, Cavill R. Bioinformatic methods in NMR-based metabolic profiling. Prog Nucl Magn Reson Spectrosc, 2009, 55(4): 361-374.
10.	Lund E, Dumeaux V. Systems epidemiology in cancer. Cancer Epidemiol Biomarkers Prev, 2008, 17(11): 2954-2957.
11.	周滔, 李静宜, 马毅, 等. 基于组学数据库整合工具的代谢通路分析应用. 国际药学研究杂志, 2015, 42(5): 587-592, 600.
12.	Kim M, Tagkopoulos I. Data integration and predictive modeling methods for multi-omics datasets. Mol Omics, 2018, 14(1): 8-25.
13.	Ang JC, Mirzal A, Haron H, et al. Supervised, unsupervised, and semi-supervised feature selection: a review on gene selection. IEEE/ACM Trans Comput Biol Bioinform, 2016, 13(5): 971-989.
14.	Gheyas IA, Smith LS. Feature subset selection in large dimensionality domains. Pattern Recognition, 2010, 43(1): 5-13.
15.	Momeni Z, Hassanzadeh E, Saniee Abadeh M, et al. A survey on single and multi omics data mining methods in cancer data classification. J Biomed Inform, 2020, 107: 103466.
16.	Ritchie MD, Holzinger ER, Li R, et al. Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev Genet, 2015, 16(2): 85-97.
17.	Holzinger ER, Ritchie MD. Integrating heterogeneous high-throughput data for meta-dimensional pharmacogenomics and disease-related studies. Pharmacogenomics, 2012, 13(2): 213-222.
18.	Holzinger ER, Dudek SM, Frase AT, et al. ATHENA: the analysis tool for heritable and environmental network associations. Bioinformatics, 2014, 30(5): 698-705.
19.	Kim D, Li R, Dudek SM, et al. ATHENA: Identifying interactions between different levels of genomic data associated with cancer clinical outcomes using grammatical evolution neural network. BioData Min, 2013, 6(1): 23.
20.	Atabaki-Pasdar N, Ohlsson M, Viuela A, et al. Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts. PLoS Med, 2020, 17(6): e1003149.
21.	Peng C, Li A, Wang M. Discovery of bladder cancer-related genes using integrative heterogeneous network modeling of multi-omics data. Sci Rep, 2017, 7(1): 15639.
22.	Tao M, Song T, Du W, et al. Classifying breast cancer subtypes using multiple kernel learning based on omics data. Genes (Basel), 2019, 10(3): 200.
23.	Akavia UD, Litvin O, Kim J, et al. An integrated approach to uncover drivers of cancer. Cell, 2010, 143(6): 1005-1017.
24.	Pauling JK, Christensen AG, Batra R, et al. Elucidation of epithelial-mesenchymal transition-related pathways in a triple-negative breast cancer cell line model by multi-omics interactome analysis. Integr Biol (Camb), 2014, 6(11): 1058-1068.
25.	Wang J, Li S, Lin S, et al. B-cell lymphoma 2 family genes show a molecular pattern of spatiotemporal heterogeneity in gynaecologic and breast cancer. Cell Prolif, 2020, 53(6): e12826.
26.	Kim SY, Kim TR, Jeong HH, et al. Integrative pathway-based survival prediction utilizing the interaction between gene expression and DNA methylation in breast cancer. BMC Med Genomics, 2018, 11(Suppl 3): 68.
27.	Mihaylov I, Kańduła M, Krachunov M, et al. A novel framework for horizontal and vertical data integration in cancer studies with application to survival time prediction models. Biol Direct, 2019, 14(1): 22.
28.	Ankney JA, Xie L, Wrobel JA, et al. Novel secretome-to-transcriptome integrated or secreto-transcriptomic approach to reveal liquid biopsy biomarkers for predicting individualized prognosis of breast cancer patients. BMC Med Genomics, 2019, 12(1): 78.

1. Taubes G. Epidemiology faces its limits. Science, 1995, 269(5221): 164-169.
2. Haring R, Wallaschofski H. Diving through the “-omics”: the case for deep phenotyping and systems epidemiology. Omics, 2012, 16(5): 231-234.
3. Hu FB. Metabolic profiling of diabetes: from black-box epidemiology to systems epidemiology. Clin Chem, 2011, 57(9): 1224-1226.
4. Dammann O, Gray P, Gressens P, et al. Systems epidemiology: What’s in a name? Online J Public Health Inform, 2014, 6(3): e198.
5. 黄涛, 李立明. 系统流行病学. 中华流行病学杂志, 2018, 39(5): 694-699.
6. Claussnitzer M, Dankel SN, Kim KH, et al. FTO obesity variant circuitry and adipocyte browning in humans. N Engl J Med, 2015, 373(10): 895-907.
7. Paul H. Data-driven hypotheses. EMBO Reports, 2013, 14(2): 104.
8. Pinu FR, Beale DJ, Paten AM, et al. Systems biology and multi-omics integration: viewpoints from the metabolomics research community. Metabolites, 2019, 9(4): 76.
9. Ebbels T, Cavill R. Bioinformatic methods in NMR-based metabolic profiling. Prog Nucl Magn Reson Spectrosc, 2009, 55(4): 361-374.
10. Lund E, Dumeaux V. Systems epidemiology in cancer. Cancer Epidemiol Biomarkers Prev, 2008, 17(11): 2954-2957.
11. 周滔, 李静宜, 马毅, 等. 基于组学数据库整合工具的代谢通路分析应用. 国际药学研究杂志, 2015, 42(5): 587-592, 600.
12. Kim M, Tagkopoulos I. Data integration and predictive modeling methods for multi-omics datasets. Mol Omics, 2018, 14(1): 8-25.
13. Ang JC, Mirzal A, Haron H, et al. Supervised, unsupervised, and semi-supervised feature selection: a review on gene selection. IEEE/ACM Trans Comput Biol Bioinform, 2016, 13(5): 971-989.
14. Gheyas IA, Smith LS. Feature subset selection in large dimensionality domains. Pattern Recognition, 2010, 43(1): 5-13.
15. Momeni Z, Hassanzadeh E, Saniee Abadeh M, et al. A survey on single and multi omics data mining methods in cancer data classification. J Biomed Inform, 2020, 107: 103466.
16. Ritchie MD, Holzinger ER, Li R, et al. Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev Genet, 2015, 16(2): 85-97.
17. Holzinger ER, Ritchie MD. Integrating heterogeneous high-throughput data for meta-dimensional pharmacogenomics and disease-related studies. Pharmacogenomics, 2012, 13(2): 213-222.
18. Holzinger ER, Dudek SM, Frase AT, et al. ATHENA: the analysis tool for heritable and environmental network associations. Bioinformatics, 2014, 30(5): 698-705.
19. Kim D, Li R, Dudek SM, et al. ATHENA: Identifying interactions between different levels of genomic data associated with cancer clinical outcomes using grammatical evolution neural network. BioData Min, 2013, 6(1): 23.
20. Atabaki-Pasdar N, Ohlsson M, Viuela A, et al. Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts. PLoS Med, 2020, 17(6): e1003149.
21. Peng C, Li A, Wang M. Discovery of bladder cancer-related genes using integrative heterogeneous network modeling of multi-omics data. Sci Rep, 2017, 7(1): 15639.
22. Tao M, Song T, Du W, et al. Classifying breast cancer subtypes using multiple kernel learning based on omics data. Genes (Basel), 2019, 10(3): 200.
23. Akavia UD, Litvin O, Kim J, et al. An integrated approach to uncover drivers of cancer. Cell, 2010, 143(6): 1005-1017.
24. Pauling JK, Christensen AG, Batra R, et al. Elucidation of epithelial-mesenchymal transition-related pathways in a triple-negative breast cancer cell line model by multi-omics interactome analysis. Integr Biol (Camb), 2014, 6(11): 1058-1068.
25. Wang J, Li S, Lin S, et al. B-cell lymphoma 2 family genes show a molecular pattern of spatiotemporal heterogeneity in gynaecologic and breast cancer. Cell Prolif, 2020, 53(6): e12826.
26. Kim SY, Kim TR, Jeong HH, et al. Integrative pathway-based survival prediction utilizing the interaction between gene expression and DNA methylation in breast cancer. BMC Med Genomics, 2018, 11(Suppl 3): 68.
27. Mihaylov I, Kańduła M, Krachunov M, et al. A novel framework for horizontal and vertical data integration in cancer studies with application to survival time prediction models. Biol Direct, 2019, 14(1): 22.
28. Ankney JA, Xie L, Wrobel JA, et al. Novel secretome-to-transcriptome integrated or secreto-transcriptomic approach to reveal liquid biopsy biomarkers for predicting individualized prognosis of breast cancer patients. BMC Med Genomics, 2019, 12(1): 78.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

基于多组学数据的流行病学研究策略及其在乳腺癌研究中的应用

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