참고문헌 (36)

1. 1. Zhang J Chiodini R Badr A Zhang G The impact of next-generation sequencing on genomics J Genet Genom 2011 38 3 95 109 

2. 2. Meldrum C Doyle MA Tothill RW Next-generation sequencing for cancer diagnostics: a practical perspective Clin Biochem Rev 2011 32 4 177 195 22147957 

   상세보기

3. 3. Brennan P Wild CP Genomics of cancer and a new era for cancer prevention PLoS Genet 2015 11 11 

4. 4. Nakagawa H Fujita M Whole genome sequencing analysis for cancer genomics and precision medicine Cancer Sci 2018 109 3 513 522 29345757 

   상세보기

5. 5. Xu C A review of somatic single nucleotide variant calling algorithms for next-generation sequencing data Comput Struct Biotechnol J 2018 16 15 24 29552334 

   상세보기

6. 6. Greenman C Stephens P Smith R Dalgliesh GL Hunter C Bignell G Davies H Teague J Butler A Stevens C Patterns of somatic mutation in human cancer genomes Nature 2007 446 7132 153 158 17344846 

   상세보기

7. 7. Martincorena I Campbell PJ Somatic mutation in cancer and normal cells Science 2015 349 6255 1483 1489 26404825 

   상세보기

8. 8. Miraflor AP de Abreu FB Peterson JD Turner SA Amos CI Tsongalis GJ Yan S Somatic mutation analysis in melanoma using targeted next generation sequencing Exp Mol Pathol 2017 103 2 172 177 28822769 

   상세보기

9. 9. Krøigard AB Larsen MJ Lænkholm AV Knoop AS Jensen JD Bak M Mollenhauer J Thomassen M Kruse TA Identification of metastasis driver genes by massive parallel sequencing of successive steps of breast cancer progression PLoS ONE 2018 13 1 

10. 10. Cannataro VL Gaffney SG Townsend JP Effect sizes of somatic mutations in cancer J Nat Cancer Inst 2018 110 11 1171 1177 30365005 

11. 11. Xie T Cho YB Wang K Huang D Hong HK Choi YL Ko YH Nam DH Jin J Yang H Patterns of somatic alterations between matched primary and metastatic colorectal tumors characterized by whole-genome sequencing Genomics 2014 104 4 234 241 25066378 

    상세보기

12. 12. Girish C Ferat S A survey on feature selection methods Comput Electr Eng 2014 40 1 16 28 

    상세보기

13. 13. Saeys Y Inza I Larranaga P A review of feature selection techniques in bioinformatics Bioinformatics 2007 23 19 2507 2517 17720704 

    상세보기

14. 14. Erzurumluoglu AM Rodriguez S Shihab HA Baird D Richardson TG Day IN Gaunt TR Identifying highly penetrant disease causal mutations using next generation sequencing: guide to whole process BioMed Res Int 2015 2015 923491 26106619 

    상세보기

15. 15. Jovi A, Brki K, Bogunovi N. A review of feature selection methods with applications. In: 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2015;1200？1205. IEEE. 

16. 16. Alikovi E Subasi A Breast cancer diagnosis using GA feature selection and rotation forest Neural Comput Appl 2017 28 4 753 763 

17. 17. Xi M Sun J Liu L Fan F Wu X Cancer feature selection and classification using a binary quantum-behaved particle swarm optimization and support vector machine Comput Math Methods Med 2016 2016 3572705 27642363 

18. 18. Zhao G Wu Y Feature subset selection for cancer classification using weight local modularity Sci Rep 2016 6 34759 34774 27703256 

    상세보기

19. 19. Liu S Xu C Zhang Y Liu J Yu B Liu X Dehmer M Feature selection of gene expression data for cancer classification using double RBF-kernels BMC Bioinform 2018 19 1 396 409 

20. 20. Sakoparnig T Fried P Beerenwinkel N Identification of constrained cancer driver genes based on mutation timing PLoS Comput Biol 2015 11 1 

21. 21. Behravan H Hartikainen JM Tengstrom M Pylkas K Winqvist R Kosma VM Mannermaa A Machine learning identifies interacting genetic variants contributing to breast cancer risk: a case study in Finnish cases and controls Sci Rep 2018 8 1 13149 13161 30177847 

22. 22. Ahsen ME Boren TP Singh NK Misganaw B Mutch DG Moore KN Backes FJ McCourt CK Lea JS Miller DS Sparse feature selection for classification and prediction of metastasis in endometrial cancer BMC Genom 2017 18 3 233 244 

23. 23. Daniel CK Qunyuan Z David EL Dong S Michael DM Ling L Christopher AM Elaine RM Li D Richard KW VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing Genome Res 2012 22 3 568 576 22300766 

    상세보기

24. 24. Wang K Li M Hakonarson H ANNOVAR functional annotation of genetic variants from high-throughput sequencing data Nucleic Acids Res 2010 38 16 164 

25. 25. Forbes S Bindal N Bamford S Cole C Yin Kok C Beare D Jia M Shepherd R Leung K Menzies A COSMIC: mining complete cancer genomes in the catalogue of somatic mutations in cancer Nucleic Acids Res 2010 39 1 945 950 

26. 26. Cortes C Vapnik V Support-vector networks Mach Learn 1995 20 3 273 297 

    상세보기

27. 27. Manolio TA Genomewide association studies and assessment of the risk of disease N Engl J Med 2010 363 2 166 176 20647212 

    상세보기

28. 28. Kohler S Carmody L Vasilevsky N Jacobsen JOB Danis D Gourdine J-P Gargano M Harris NL Matentzoglu N McMurry JA Expansion of the human phenotype ontology (HPO) knowledge base and resources Nucleic Acids Res 2018 47 D1 1018 1027 

29. 29. Kanehisa M Goto S KEGG: kyoto encyclopedia of genes and genomes Nucleic Acids Res 2000 28 1 27 30 10592173 

    상세보기

30. 30. David EL Christopher CH Ken C Daniel CK Travis EA David JD Timothy JL Elaine RM Richard KW Li D SomaticSniper: identification of somatic point mutations in whole genome sequencing data Bioinformatics 2011 28 3 311 317 22155872 

31. 31. Zhang F Lupski JR Non-coding genetic variants in human disease Hum Mol Genet 2015 24 R1 102 110 

32. 32. Zhu Y Tazearslan C Suh Y Challenges and progress in interpretation of non-coding genetic variants associated with human disease Exp Biol Med 2017 242 13 1325 1334 

33. 33. Andrew Hall M. Correlation-based feature selection for machine learning. PhD thesis, The University of Waikato, Department of Computer Science, 1999. 

34. 34. Eberhart R, Kennedy J. A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995;39？43. IEEE. 

35. 35. Lee S Soak S Oh S Pedrycz W Jeon M Modified binary particle swarm optimization Prog Nat Sci 2008 18 9 1161 1166 

    상세보기

36. 36. Kennedy J, Eberhart RC. A discrete binary version of the particle swarm algorithm. In: IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, 1997;5:4104？4108. IEEE. 

활용도 Top5 논문

더보기

해당 논문의 주제분야에서 활용도가 높은 상위 5개 콘텐츠를 보여줍니다.
더보기 버튼을 클릭하시면 더 많은 관련자료를 살펴볼 수 있습니다.