The following recommendations are made for future research studies:
1) It is recommended to implement AAE model on different bigger data-sets with more hidden layers and optimise the hyper-parameters accordingly.
2) Instead of using grid search technique, AAE architecture in conjunction with a genetic algorithm can be implemented to optimise its hyper-parameters more effi- ciently.
3) Better hardware with higher GPU cluster is recommended for training deeper AAE.
4) Cross cancer biomarker can be identified if AAE can be applied to heterogeneous cancer data.
5) Mutation detection pipeline can be applied to different genomic data to identify the mutation. Also, based on the need of the researchers, a different biological plan can be implemented on this pipelines using this methylated DNA dataset.
It is expected that this thesis provides a strong basis for ongoing research in deep learning and bioinformatics. In this thesis, a new architecture for feature extraction and pipeline for mutation detection are developed. Then the proposed method is compared with traditional methods, and its effectiveness is established by comparing their results.
Reproducibility
My work in this thesis is fully reproducible, please follow the link below. https://bit.ly/2x3FHZt
https://github.com/Nano-Neuro-Research-Lab/latent-space-discovery https://github.com/Nano-Neuro-Research-Lab/ngs-variant-analysis
Presentation
1. R.K. Mondol. Machine learning approach to characterize breast cancer sub- types using gene expression profile. Presented Poster in AMSI BioinfoSummer 2017 at Monash University.
REFERENCES
[1] K. Wetterstrand. (2017) Dna sequencing costs: Data from the nhgri genome sequencing program (gsp). Website. National Human Genome Research Institute. [Online]. Available: www.genome.gov/sequencingcostsdata
[2] C. Cao, F. Liu, H. Tan, D. Song, W. Shu, W. Li, Y. Zhou, X. Bo, and Z. Xie, “Deep learning and its applications in biomedicine,” Genomics, Proteomics & Bioinformatics, vol. 16, no. 1, pp. 17–32, feb 2018.
[3] C. E. Cook, M. T. Bergman, G. Cochrane, R. Apweiler, and E. Birney, “The european bioinformatics institute in 2017: data coordination and integration,” Nucleic Acids Research, vol. 46, no. D1, pp. D21–D29, nov 2017.
[4] T. H. Shen, C. S. Carlson, and P. Tarczy-Hornoch, “SNPit: A federated data integration system for the purpose of functional SNP annotation,” Computer Methods and Programs in Biomedicine, vol. 95, no. 2, pp. 181–189, aug 2009. [5] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no.
7553, pp. 436–444, may 2015.
[6] S. Min, B. Lee, and S. Yoon, “Deep learning in bioinformatics,” Briefings in Bioinformatics, p. bbw068, jul 2016.
[7] R. M. F. James D. Miller. (2017) Mastering predic- tive analytics with r - second edition. [Online]. Avail- able: https://subscription.packtpub.com/book/big data and business intelligence/9781787121393/5/ch05lvl1sec38/the-biological-neuron
[8] A. Singh, N. Thakur, and A. Sharma, “A review of supervised machine learning algorithms,” in 2016 3rd International Conference on Computing for Sustain- able Global Development (INDIACom), March 2016, pp. 1310–1315.
[9] Z. Ghahramani, “Unsupervised learning,” in Summer School on Machine Learn- ing. Springer, 2003, pp. 72–112.
[10] R. Gentleman and V. J. Carey, “Unsupervised machine learning,” in Biocon- ductor Case Studies. Springer New York, 2008, pp. 137–157.
[11] K. J. Cios, R. W. Swiniarski, W. Pedrycz, and L. A. Kurgan, “Unsupervised learning: Association rules,” in Data Mining. Springer US, pp. 289–306. [12] X. Zhu, “Semi-supervised learning,” in Encyclopedia of Machine Learning and
Data Mining. Springer US, 2017, pp. 1142–1147.
[13] N. Castle. (2018, Feb.) What is semi-supervised learning? [Online]. Available: https://www.datascience.com/blog/what-is-semi-supervised-learning
[14] “1.17. neural network models (supervised).” [Online]. Available: https: //scikit-learn.org/stable/modules/neural networks supervised.html
[15] Neural network models (supervised). Sickit-Learn. [Online]. Available: http://scikit-learn.org/stable/modules/neural networks supervised.html [16] E. Fouch. Neural-based outlier discovery. [Online]. Available: https:
//edouardfouche.com/Neural-based-Outlier-Discovery/
[17] P. Werbos, “Backpropagation through time: what it does and how to do it,” Proceedings of the IEEE, vol. 78, no. 10, pp. 1550–1560, 1990.
[18] G. Hinton and T. Sejnowski, “A theoretical framework for back-propagation.” [19] R. Rojas, “The backpropagation algorithm,” in Neural networks. Springer,
1996, pp. 149–182.
[20] “A gentle introduction to mini-batch gradient descent and how to configure batch size,” Apr 2018. [Online]. Available: https://machinelearningmastery. com/gentle-introduction-mini-batch-gradient-descent-configure-batch-size/ [21] L. Bottou, “Large-scale machine learning with stochastic gradient descent,” in
Proceedings of COMPSTAT’2010. Springer, 2010, pp. 177–186.
[22] S. Ruder, “An overview of gradient descent optimization algorithms,” CoRR, vol. abs/1609.04747, 2016. [Online]. Available: http://arxiv.org/abs/1609. 04747
[23] S. Roy, W. A. LaFramboise, Y. E. Nikiforov, M. N. Nikiforova, M. J. Routbort, J. Pfeifer, R. Nagarajan, A. B. Carter, and L. Pantanowitz, “Next-generation sequencing informatics: Challenges and strategies for implementation in a clini- cal environment,” Archives of Pathology & Laboratory Medicine, vol. 140, no. 9, pp. 958–975, sep 2016.
[24] “Mitochondrial dna - genetics home reference - nih.” [Online]. Available: https://ghr.nlm.nih.gov/mitochondrial-dna
[25] R. E. H. Geoffrey M. Cooper, The Cell: A Molecular Approach. SINAUER ASSOC, 2015.
[26] P. J. Russell, iGenetics: A Molecular Approach. CUMMINGS, 2009. [Online]. Available: https://www.ebook.de/de/product/8045932/ peter j russell igenetics a molecular approach.html
[27] S. C. . W. Brown. (2008) Translation: Dna to mrna to pro- tein. [Online]. Available: https://www.nature.com/scitable/topicpage/ translation-dna-to-mrna-to-protein-393
[28] Gene expression. [Online]. Available: https://www.wikiwand.com/en/Gene expression
[29] K. R. Kukurba and S. B. Montgomery, “RNA sequencing and analysis,” Cold Spring Harbor Protocols, vol. 2015, no. 11, p. pdb.top084970, apr 2015.
[30] Y. W. Hari Krishna Yalamanchili and Z. Liu, “Data analysis pipeline for rnaseq experiments: From differential expression to cryptic splicing,” Current Protocols in Bioinformatics, vol. 59, pp. 11.15.1–11.15.21, 2018.
[31] Rna-seq analysis. Vanderbilt Technologies for Advanced Genomics Analysis and Research Design (VANGARD). [Online]. Available: http://bioinfo. vanderbilt.edu/vangard/services-rnaseq.html
[32] J. L. Causey, C. Ashby, K. Walker, Z. P. Wang, M. Yang, Y. Guan, J. H. Moore, and X. Huang, “DNAp: A pipeline for DNA-seq data analysis,” Scientific Re- ports, vol. 8, no. 1, may 2018.
[33] Dna-seq analysis. Vanderbilt Technologies for Advanced Genomics Analysis and Research Design (VANGARD). [Online]. Available: http://bioinfo. vanderbilt.edu/vangard/services-dnaseq.html
[34] M. Ashburner, C. A. Ball, J. A. Blake, D. Botstein, H. Butler, J. M. Cherry, A. P. Davis, K. Dolinski, S. S. Dwight, J. T. Eppig, M. A. Harris, D. P. Hill, L. Issel-Tarver, A. Kasarskis, S. Lewis, J. C. Matese, J. E. Richardson, M. Ring- wald, G. M. Rubin, and G. Sherlock, “Gene ontology: tool for the unification of biology,” Nature Genetics, vol. 25, no. 1, pp. 25–29, may 2000.
[35] C. Australia, “Breast cancer statistics,” Jan 2013. [On- line]. Available: https://canceraustralia.gov.au/affected-cancer/cancer-types/ breast-cancer/breast-cancer-statistics
[36] E. S. McDonald, A. S. Clark, J. Tchou, P. Zhang, and G. M. Freedman, “Clinical diagnosis and management of breast cancer,” Journal of Nuclear Medicine, vol. 57, no. Supplement 1, pp. 9S–16S, feb 2016.
[37] P. Danaee, R. Ghaeini, and D. A. Hendrix, “A Deep Learning Approach For Cancer Detection And Relevant Gene Identification,” in Biocomputing 2017. World Scientific, nov 2016.
[38] H. R. Macdonald, Breast Cancer Screening. Cham: Springer International Publishing, 2017, pp. 1–8.
[39] J. C. Marioni, C. E. Mason, S. M. Mane, M. Stephens, and Y. Gilad, “RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays,” Genome Research, vol. 18, no. 9, pp. 1509–1517, jul 2008.
[40] J.-G. Lee, S. Jun, Y.-W. Cho, H. Lee, G. B. Kim, J. B. Seo, and N. Kim, “Deep learning in medical imaging: General overview,” Korean Journal of Radiology, vol. 18, no. 4, p. 570, 2017.
[41] S. Badve, D. J. Dabbs, S. J. Schnitt, F. L. Baehner, T. Decker, V. Eusebi, S. B. Fox, S. Ichihara, J. Jacquemier, S. R. Lakhani, and et al., “Basal-like and triple-negative breast cancers: a critical review with an emphasis on the implications for pathologists and oncologists,” Modern Pathology, vol. 24, no. 2, p. 157167, Dec 2010.
[42] R. R. Bhat, V. Viswanath, and X. Li, “Deepcancer: Detecting cancer through gene expressions via deep generative learning,” CoRR, vol. abs/1612.03211, 2016.
[43] A. Gupta, H. Wang, and M. Ganapathiraju, “Learning structure in gene ex- pression data using deep architectures, with an application to gene clustering,” nov 2015.
[44] G. I. Salama, M. B. Abdelhalim, and M. A. e. Zeid, “Experimental comparison of classifiers for breast cancer diagnosis,” in 2012 Seventh International Con- ference on Computer Engineering Systems (ICCES), Nov 2012, pp. 180–185. [45] S. Vural, X. Wang, and C. Guda, “Classification of breast cancer patients using
somatic mutation profiles and machine learning approaches,” BMC Systems Biology, vol. 10, no. S3, aug 2016.
[46] A. M. Abdel-Zaher and A. M. Eldeib, “Breast cancer classification using deep belief networks,” Expert Systems with Applications, vol. 46, pp. 139–144, mar 2016.
[47] E. M. Karabulut and T. Ibrikci, “Discriminative deep belief networks for mi- croarray based cancer classification.” Biomedical Research, vol. 25, no. 3, pp. 1016–1024, 2017.
[48] K. Kourou, T. P. Exarchos, K. P. Exarchos, M. V. Karamouzis, and D. I. Fotiadis, “Machine learning applications in cancer prognosis and prediction,” Computational and Structural Biotechnology Journal, vol. 13, pp. 8–17, 2015. [49] A. Makhzani, J. Shlens, N. Jaitly, and I. J. Goodfellow, “Adversarial autoen-
coders,” CoRR, vol. abs/1511.05644, 2015.
[50] R. Longadge and S. Dongre, “Class imbalance problem in data mining review,” CoRR, vol. abs/1305.1707, 2013. [Online]. Available: http: //arxiv.org/abs/1305.1707
[51] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Ma- chine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
[52] R. S. Olson, R. J. Urbanowicz, P. C. Andrews, N. A. Lavender, L. C. Kidd, and J. H. Moore, Applications of Evolutionary Computation: 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30 – April 1, 2016, Proceedings, Part I. Springer International Publishing, 2016, ch. Automating Biomedical Data Science Through Tree-Based Pipeline Optimization, pp. 123–137. [Online]. Available: http://dx.doi.org/10.1007/978-3-319-31204-0 9 [53] R. Poplin, P.-C. Chang, D. Alexander, S. Schwartz, T. Colthurst, A. Ku,
D. Newburger, J. Dijamco, N. Nguyen, P. T. Afshar, S. S. Gross, L. Dorfman, C. Y. McLean, and M. A. DePristo, “A universal snp and small-indel variant caller using deep neural networks,” Nature Biotechnology, vol. 36, p. 983, Sep. 2018. [Online]. Available: https://doi.org/10.1038/nbt.4235
[54] G. P. Way and C. S. Greene, “Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders,” in Biocomputing 2018. WORLD SCIENTIFIC, nov 2017.
[55] P. DANAEE, R. GHAEINI, and D. A. HENDRIX, “A DEEP LEARNING APPROACH FOR CANCER DETECTION AND RELEVANT GENE IDEN- TIFICATION,” in Biocomputing 2017. WORLD SCIENTIFIC, nov 2016.
[56] R. Xie, A. Quitadamo, J. Cheng, and X. Shi, “A predictive model of gene expression using a deep learning framework,” in 2016 IEEE International Con- ference on Bioinformatics and Biomedicine (BIBM). IEEE, dec 2016.
[57] J. Wang, H. Cao, J. Z. H. Zhang, and Y. Qi, “Computational protein design with deep learning neural networks,” Scientific Reports, vol. 8, no. 1, p. 6349, Apr. 2018. [Online]. Available: https://doi.org/10.1038/s41598-018-24760-x [58] K. Baek, “Learning deep architectures for protein structure prediction,” Pro-
ceedings of the 7th International Conference on Bioinformatics and Computa- tional Biology, BICOB 2015, pp. 137–142, 01 2015.
[59] K. Chandni, P. M. Pandya, and D. S. Jardosh, “Deep learning approaches for protein structure prediction,” International Journal of Engineering & Technol- ogy, vol. 7, no. 4.5, pp. 168–170, sep 2018.
[60] F. Emmert-Streib and M. Dehmer, “A machine learning perspective on person- alized medicine: An automized, comprehensive knowledge base with ontology for pattern recognition,” Machine Learning and Knowledge Extraction, vol. 1, no. 1, pp. 149–156, sep 2018.
[61] G. P. Way and C. S. Greene, “Evaluating deep variational autoencoders trained on pan-cancer gene expression.”
[62] T. Ching, D. S. Himmelstein, B. K. Beaulieu-Jones, A. A. Kalinin, B. T. Do, G. P. Way, E. Ferrero, P.-M. Agapow, M. Zietz, M. M. Hoffman, W. Xie, G. L. Rosen, B. J. Lengerich, J. Israeli, J. Lanchantin, S. Woloszynek, A. E. Carpenter, A. Shrikumar, J. Xu, E. M. Cofer, C. A. Lavender, S. C. Turaga, A. M. Alexandari, Z. Lu, D. J. Harris, D. DeCaprio, Y. Qi, A. Kundaje, Y. Peng, L. K. Wiley, M. H. S. Segler, S. M. Boca, S. J. Swamidass, A. Huang, A. Gitter, and C. S. Greene, “Opportunities and obstacles for deep learning in biology and medicine,” Journal of The Royal Society Interface, vol. 15, no. 141, p. 20170387, apr 2018.
[63] D. Erhan, P.-A. Manzagol, Y. Bengio, S. Bengio, and P. Vincent, “The difficulty of training deep architectures and the effect of unsupervised pre-training,” in Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), 2009, pp. 153–160. [Online]. Available: http: //jmlr.csail.mit.edu/proceedings/papers/v5/erhan09a/erhan09a.pdf
[64] U. Raj, I. Aier, R. Semwal, and P. K. Varadwaj, “Identification of novel dys- regulated key genes in breast cancer through high throughput ChIP-seq data analysis,” Scientific Reports, vol. 7, no. 1, jun 2017.
[65] D. W. Huang, B. T. Sherman, and R. A. Lempicki, “Systematic and integra- tive analysis of large gene lists using DAVID bioinformatics resources,” Nature Protocols, vol. 4, no. 1, pp. 44–57, jan 2009.
[66] ——, “Bioinformatics enrichment tools: paths toward the comprehensive func- tional analysis of large gene lists,” Nucleic Acids Research, vol. 37, no. 1, pp. 1–13, nov 2008.
[67] A. Makhzani, J. Shlens, N. Jaitly, and I. J. Goodfellow, “Adversarial autoencoders,” CoRR, vol. abs/1511.05644, 2015. [Online]. Available: http://arxiv.org/abs/1511.05644
[68] L. Buitinck, G. Louppe, M. Blondel, F. Pedregosa, A. Mueller, O. Grisel, V. Niculae, P. Prettenhofer, A. Gramfort, J. Grobler, R. Layton, J. Vander- Plas, A. Joly, B. Holt, and G. Varoquaux, “API design for machine learning software: experiences from the scikit-learn project,” in ECML PKDD Work- shop: Languages for Data Mining and Machine Learning, 2013, pp. 108–122. [69] F. Chollet et al., “Keras,” https://keras.io, 2015.
[70] M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Man´e, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Vi´egas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from tensorflow.org. [Online]. Available: https://www.tensorflow.org/
[71] L. Weber, D. Maßberg, C. Becker, J. Altmller, B. Ubrig, G. Bonatz, G. Wlk, S. Philippou, A. Tannapfel, H. Hatt, and G. Gisselmann, “Olfactory receptors as biomarkers in human breast carcinoma tissues,” Frontiers in Oncology, vol. 8, feb 2018.
[72] A. Burger, Y. Amemiya, R. Kitching, and A. K. Seth, “Novel RING e3 ubiquitin ligases in breast cancer,” Neoplasia, vol. 8, no. 8, pp. 689–695, aug 2006. [73] C. Cui, R. Merritt, L. Fu, and Z. Pan, “Targeting calcium signaling in cancer
therapy,” Acta Pharmaceutica Sinica B, vol. 7, no. 1, pp. 3–17, jan 2017. [74] C. L. So, J. M. Saunus, S. J. Roberts-Thomson, and G. R. Monteith, “Calcium
signalling and breast cancer,” Seminars in Cell & Developmental Biology, nov 2018.
[75] E. Cannizzaro, A. J. Bannister, N. Han, A. Alendar, and T. Kouzarides, “DDX3x RNA helicase affects breast cancer cell cycle progression by regulating expression of KLF4,” FEBS Letters, vol. 592, no. 13, pp. 2308–2322, jun 2018. [76] J. R. Delaney and D. G. Stupack, “Whole genome pathway analysis identifies an association of cadmium response gene loss with copy number variation in mutant p53 bearing uterine endometrial carcinomas,” PLOS ONE, vol. 11, no. 7, p. e0159114, jul 2016.
[77] J. A. McElroy, R. L. Kruse, J. Guthrie, R. E. Gangnon, and J. D. Robert- son, “Cadmium exposure and endometrial cancer risk: A large midwestern u.s. population-based case-control study,” PLOS ONE, vol. 12, no. 7, p. e0179360, jul 2017.
[78] E. S. Jaffe, Pathology and Genetics: Tumours of Haematopoietic and Lymphoid Tissues (World Health Organization Classification of Tumours). Intl Agency for Research on Cancer, 2003. [Online]. Available: https://www. amazon.com/Pathology-Genetics-Haematopoietic-Organization-Classification/ dp/9283224116?SubscriptionId=AKIAIOBINVZYXZQZ2U3A&tag=
chimbori05-20&linkCode=xm2&camp=2025&creative=165953& creativeASIN=9283224116
[79] S. Swerdlow, E. Campo, N. L. Harris, E. S. Jaffe, S. A. Pileri, H. Stein, J. Thiele, D. Arber, R. Hasserjian, and M. L. Beau, WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues (Medicine). World Health Organization, 2017. [Online]. Available: https://www.amazon.com/ Classification-Tumours-Haematopoietic-Lymphoid-Medicine/dp/928324494X? SubscriptionId=AKIAIOBINVZYXZQZ2U3A&tag=chimbori05-20&
linkCode=xm2&camp=2025&creative=165953&creativeASIN=928324494X [80] S. H. Swerdlow, E. Campo, S. A. Pileri, N. L. Harris, H. Stein, R. Siebert,
R. Advani, M. Ghielmini, G. A. Salles, A. D. Zelenetz, and E. S. Jaffe, “The 2016 revision of the world health organization classification of lymphoid neoplasms,” Blood, vol. 127, no. 20, pp. 2375–2390, mar 2016.
[81] M.-G. Yu and H.-Y. Zheng, “Acute myeloid leukemia: Advancements in di- agnosis and treatment,” Chinese medical journal, vol. 130(2), p. 211218, Jan. 2017.
[82] I. D. Kouchkovsky and M. Abdul-Hay, “‘acute myeloid leukemia: a compre- hensive review and 2016 update’,” Blood Cancer Journal, vol. 6, no. 7, pp. e441–e441, jul 2016.
[83] S. Lee, J. Chen, G. Zhou, R. Z. Shi, G. G. Bouffard, M. Kocherginsky, X. Ge, M. Sun, N. Jayathilaka, Y. C. Kim, N. Emmanuel, S. K. Bohlander, M. Minden, J. Kline, O. Ozer, R. A. Larson, M. M. LeBeau, E. D. Green, J. Trent, T. Kar- rison, P. P. Liu, S. M. Wang, and J. D. Rowley, “Gene expression profiles in acute myeloid leukemia with common translocations using SAGE,” Proceedings of the National Academy of Sciences, vol. 103, no. 4, pp. 1030–1035, jan 2006. [84] S. Yohe, “Molecular genetic markers in acute myeloid leukemia,” Journal of
Clinical Medicine, vol. 4, no. 3, pp. 460–478, mar 2015.
[85] C. C. Kumar, “Genetic abnormalities and challenges in the treatment of acute myeloid leukemia,” Genes & Cancer, vol. 2, no. 2, pp. 95–107, feb 2011. [86] R. M. Leggett, R. H. Ramirez-Gonzalez, B. J. Clavijo, D. Waite, and R. P.
Davey, “Sequencing quality assessment tools to enable data-driven informatics for high throughput genomics,” Frontiers in Genetics, vol. 4, 2013.
[87] D. Blankenberg, A. Gordon, G. V. Kuster, N. Coraor, J. Taylor, and A. N. and, “Manipulation of FASTQ data with galaxy,” Bioinformatics, vol. 26, no. 14, pp. 1783–1785, jun 2010.
[88] A. M. Bolger, M. Lohse, and B. Usadel, “Trimmomatic: a flexible trimmer for illumina sequence data,” Bioinformatics, vol. 30, no. 15, pp. 2114–2120, apr 2014.
[89] R. Al-Ali, N. Kathiresan, M. E. Anbari, E. R. Schendel, and T. A. Zaid, “Work- flow optimization of performance and quality of service for bioinformatics ap- plication in high performance computing,” Journal of Computational Science, vol. 15, pp. 3–10, jul 2016.
[90] S. Hwang, E. Kim, I. Lee, and E. M. Marcotte, “Systematic comparison of variant calling pipelines using gold standard personal exome variants,” Scientific Reports, vol. 5, no. 1, dec 2015.
[91] “Variant effect predictor.” [Online]. Available: https://asia.ensembl.org/Tools/ VEP
[92] “A database for cancer driver gene/mutation.” [Online]. Available: http: //driverdb.tms.cmu.edu.tw/ddbv2/dataset.php
[93] “Mutational cancer drivers database.” [Online]. Available: https://www. intogen.org/search
[94] J. S. Welch, T. J. Ley, D. C. Link, C. A. Miller, D. E. Larson, D. C. Koboldt, L. D. Wartman, T. L. Lamprecht, F. Liu, J. Xia, C. Kandoth, R. S. Fulton, M. D. McLellan, D. J. Dooling, J. W. Wallis, K. Chen, C. C. Harris, H. K. Schmidt, J. M. Kalicki-Veizer, C. Lu, Q. Zhang, L. Lin, M. D. O’Laughlin, J. F. McMichael, K. D. Delehaunty, L. A. Fulton, V. J. Magrini, S. D. McGrath, R. T. Demeter, T. L. Vickery, J. Hundal, L. L. Cook, G. W. Swift, J. P. Reed, P. A. Alldredge, T. N. Wylie, J. R. Walker, M. A. Watson, S. E. Heath, W. D.