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Chapter 3 - Transcriptomic and Epigenomic Approaches for Epilepsy

Published online by Cambridge University Press:  06 January 2023

By
Anika Bongaarts ,
Jagoda Glowacka ,
Konrad Wojdan ,
Angelika Mühlebner ,
Eleonora Aronica  and
James D. Mills
Edited by
Rod C. Scott  and
J. Matthew Mahoney
Rod C. Scott
Affiliation:
University of Vermont
J. Matthew Mahoney
Affiliation:
University of Vermont
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Summary

In the mid-1980s a number of scientists and research bodies conceived the idea of determining the DNA sequence of the entire human genome. Initiated in 1990 and known as the Human Genome Project (HGP), this ambitious, publicly funded project relied on contributions from numerous international laboratories and remains the world’s largest collaborative biological-based project to date. The completion of the HGP thirteen years later in 2003 allowed scientists to view the human genome in its entirety for the first time [1]. It was thought that this would usher in a new age for biological research, allowing for a more comprehensive understanding of complex human diseases and phenotypes. While this was true to an extent, completion of this project led to a series of new, more complicated questions, as is often the case in research.

Type
Chapter
Information
A Complex Systems Approach to Epilepsy
Concept, Practice, and Therapy
 , pp. 19 - 40
Publisher: Cambridge University Press
Print publication year: 2023

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References

International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome. Nature, 431, 931–45 (2004).Google Scholar
Azevedo, F. A. C., Carvalho, L. R. B., Grinberg, L. T., et al. Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J. Comp. Neurol., 513(5), 532–41 (2009).CrossRefGoogle Scholar
Hillier, L. W., Coulson, A., Murray, J. I., et al. Genomics in C. elegans: So many genes, such a little worm. Genome Res., 15(12), 1651–60 (2005).CrossRefGoogle Scholar
Varki, A., and Altheide, T. K. Comparing the human and chimpanzee genomes: Searching for needles in a haystack. Genome Res., 15(12), 1746-58 (2009).Google Scholar
Djebali, S., Davis, C. A., Merkel, A., et al. Landscape of transcription in human cells. Nature, 489(7414), 101–8 (2012).CrossRefGoogle ScholarPubMed
Blencowe, B. J. Alternative splicing: New insights from global analyses. Cell, 126(1), 37–47 (2006).Google Scholar
Peng, Z., Cheng, Y., Tan, B. C., et al. Comprehensive analysis of RNA-Seq data reveals extensive RNA editing in a human transcriptome. Nat. Biotechnol., 30(3), 253–60 (2012).Google Scholar
Keshet, I., Yisraeli, J., and Cedar, H. Effect of regional DNA methylation on gene expression. Proc. Natl. Acad. Sci. USA, 82(9), 2560–4 (2006).Google Scholar
Mattick, J. S., and Makunin, I. V. Non-coding RNA. Hum. Mol. Genet., 15 Spec No 1, R17–29 (2006).Google Scholar
Thijs, R. D., Surges, R., O’Brien, T. J., and Sander, J. W. Epilepsy in adults. Lancet, 393(10172), 689–701 (2019).CrossRefGoogle ScholarPubMed
Fisher, R. S., Acevedo, C., Arzimanoglou, A., et al. ILAE official report: A practical clinical definition of epilepsy. Epilepsia, 55(4), 475–82 (2014).Google Scholar
Blumcke, I., Spreafico, R., Haaker, G., et al. Histopathological findings in brain tissue obtained during epilepsy surgery. N. Engl. J. Med., 377, 1648–56 (2017).Google Scholar
Wang, Z., Gerstein, M., and Snyder, M. RNA-Seq: A revolutionary tool for transcriptomics. Nat. Rev. Genet., 10(1), 57–63 (2009).CrossRefGoogle ScholarPubMed
Mills, J. D., and Janitz, M. Alternative splicing of mRNA in the molecular pathology of neurodegenerative diseases. Neurobiol. Aging, 33(5), 1012.e11–24 (2012).Google Scholar
Jeck, W. R., and Sharpless, N. E. Detecting and characterizing circular RNAs. Nat. Biotechnol., 32(5), 453–61 (2014).CrossRefGoogle ScholarPubMed
van Dijk, E. L., Jaszczyszyn, Y., and Thermes, C. Library preparation methods for next-generation sequencing: Tone down the bias. Exp. Cell Res., 322(1), 12–20 (2014).CrossRefGoogle ScholarPubMed
Kukurba, K. R., and Montgomery, S. B. RNA sequencing and analysis. Cold Spring Harb. Protoc., 2015(11), 951–69 (2015).Google Scholar
Szabo, L., and Salzman, J. Detecting circular RNAs: Bioinformatic and experimental challenges. Nat. Rev. Genet., 17(11), 679–92 (2016).Google Scholar
Mills, J. D., Kawahara, Y., and Janitz, M. Strand-specific RNA-Seq provides greater resolution of transcriptome profiling. Curr. Genomics, 14(3), 173–81 (2013).Google ScholarPubMed
Parkhomchuk, D., Borodina, T., Amstislavskiy, V., et al. Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic Acids Res., 37(18), e123 (2009).CrossRefGoogle ScholarPubMed
Conesa, A., Madrigal, P., Tarazona, S., et al. A survey of best practices for RNA-seq data analysis. Genome Biol., 17, 13 (2016).Google Scholar
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., and Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods, 5(7), 621–8 (2008).CrossRefGoogle ScholarPubMed
Sims, D., Sudbery, I., Ilott, N.E., Heger, A., and Ponting, C.P. Sequencing depth and coverage: Key considerations in genomic analyses. Nat. Rev. Genet., 15(2), 121–32 (2014).CrossRefGoogle ScholarPubMed
Gilad, Y.,and Mizrahi-Man, O. A reanalysis of mouse ENCODE comparative gene expression data. F1000Research, 4, 121 (2015).Google Scholar
Buschmann, D., Haberberger, A., Kirchner, B., et al. Toward reliable biomarker signatures in the age of liquid biopsies – How to standardize the small RNA-Seq workflow. Nucleic Acids Res., 44(13), 5995–6018 (2016).CrossRefGoogle ScholarPubMed
Amaral, P. P., Dinger, M. E., Mercer, T. R., and Mattick, J. S. The eukaryotic genome as an RNA machine. Science, 319(5871), 1787–9 (2008).Google Scholar
Martens-Uzunova, E. S., Olvedy, M., and Jenster, G. Beyond microRNA – Novel RNAs derived from small non-coding RNA and their implication in cancer. Cancer Lett., 340(2), 201–11 (2013).Google Scholar
Bartel, D. P. MicroRNAs: Target recognition and regulatory functions. Cell, 136(2), 215–33 (2009).CrossRefGoogle ScholarPubMed
Lewis, B. P., Burge, C. B., and Bartel, D. P. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell, 120(1), 15–20 (2005).Google Scholar
Zhao, Y., and Srivastava, D. A developmental view of microRNA function. Trends Biochem. Sci., 32(4), 189–97 (2007).CrossRefGoogle ScholarPubMed
Koppers-Lalic, D., Hackenberg, M., Bijnsdorp, I. V., et al. Nontemplated nucleotide additions distinguish the small RNA composition in cells from exosomes. Cell Rep., 8(6), 1649–58 (2014).Google Scholar
Lake, B. B., Ai, R., Kaeser, G. E., et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science, 352(6293), 1586–90 (2016).CrossRefGoogle ScholarPubMed
Salomon, R., Kaczorowski, D., Valdes-Mora, F., et al. Droplet-based single cell RNAseq tools: A practical guide. Lab Chip, 19(10), 1706–27 (2019).Google Scholar
Pollen, A. A., Nowakowski, T. J., Shuga, J., et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat. Biotechnol., 32(10), 1053–8 (2014).CrossRefGoogle ScholarPubMed
Kulkarni, A., Anderson, A. G., Merullo, D. P., and Konopka, G. Beyond bulk: A review of single cell transcriptomics methodologies and applications. Curr. Opin. Biotechnol., 58, 129–136 (2019).Google Scholar
Lake, B. B., Codeluppi, S., Yung, Y. C., et al. A comparative strategy for single-nucleus and single-cell transcriptomes confirms accuracy in predicted cell-type expression from nuclear RNA. Sci. Rep., 7, 6031 (2017).CrossRefGoogle ScholarPubMed
Burgess, D. J. Spatial transcriptomics coming of age. Nat. Rev. Genet., 20(6), 317 (2019).CrossRefGoogle ScholarPubMed
Eng, C. L., Lawson, M., Zhu, Q., et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature, 568(7751), 235–9 (2019).Google Scholar
Rodriques, S. G., Stickels, R. R., Goeva, A., et al. Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science, 363(6434), 1463–7 (2019).CrossRefGoogle ScholarPubMed
Sedlazeck, F. J., Lee, H., Darby, C. A., and Schatz, M. C. Piercing the dark matter: Bioinformatics of long-range sequencing and mapping. Nat. Rev. Genet., 19(6), 329–46 (2018).Google Scholar
Chaisson, M. J. P., Huddleston, J., Dennis, M. Y., et al. Resolving the complexity of the human genome using single-molecule sequencing. Nature, 517(7536), 608–11 (2015).Google Scholar
Pan, Q., Shai, O., Lee, L. J., Frey, B. J., and Blencowe, B. J. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat. Genet., 40(12), 1413–5 (2008).CrossRefGoogle ScholarPubMed
Matlin, A. J., Clark, F., Smith, C. W. J. Understanding alternative splicing: Towards a cellular code. Nat. Rev. Mol. Cell Biol., 6(5), 386–98 (2005).Google Scholar
Weirather, J. L., de Cesare, M., Wang, Y., et al. Comprehensive comparison of Pacific Biosciences and Oxford Nanopore Technologies and their applications to transcriptome analysis. F1000Research, 6, 100 (2017).CrossRefGoogle ScholarPubMed
Rhoads, A., Au, K. F. PacBio Sequencing and its applications. Genomics, Proteomics Bioinformatics, 13(5), 278–89 (2015).Google Scholar
Koren, S., Schatz, M. C., Walenz, B. P., et al. Hybrid error correction and de novo assembly of single-molecule sequencing reads. Nat. Biotechnol., 30(7), 693–700 (2012).Google Scholar
Costa-Silva, J., Domingues, D., and Lopes, F. M. RNA-Seq differential expression analysis: An extended review and a software tool. PLoS ONE, 12(12), e0190152 (2017).Google Scholar
Wang, L., Wang, S., and Li, W. RSeQC: Quality control of RNA-seq experiments. Bioinformatics, 28(16), 2184–5 (2012).Google Scholar
Brown, J., Pirrung, M., and McCue, L. A. FQC Dashboard: Integrates FastQC results into a web-based, interactive, and extensible FASTQ quality control tool. Bioinformatics, 33(19), 3137–9 (2017).Google Scholar
Bolger, A. M., Lohse, M., and Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30(15), 2114–2120 (2014).Google Scholar
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal, 1(1), 10–2 (2011).Google Scholar
Križanović, K., Echchiki, A., Roux, J., and Šikić, M. Evaluation of tools for long read RNA-seq splice-aware alignment. Bioinformatics, 34(5), 748–54 (2018).Google Scholar
Pertea, M., Kim, D., Pertea, G. M., Leek, J. T., and Salzburg, S.L. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat. Protoc., 11(9), 1650–67 (2016).Google Scholar
Trapnell, C., Roberts, A., Goff, L., et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc., 7(3), 562–78 (2012).Google Scholar
Li, B., and Dewey, C. N. RSEM: Accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics, 12, 323 (2011).Google Scholar
Afgan, E., Baker, D., Batut, B., et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res., 46(W1), W537–44 (2018).Google Scholar
Khatri, P., Sirota, M., and Butte, A. J. Ten years of pathway analysis: Current approaches and outstanding challenges. PLoS Comput. Biol., 8(2), e1002375 (2012).Google Scholar
Chan, J., Wang, X., Turner, J. A., Baldwin, N. E., and Gu, J. Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing. Bioinformatics, 35(16), 2818–26 (2019).Google Scholar
Langfelder, P., and Horvath, S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics, 9, 559 (2008).Google Scholar
Mills, J. D., Iyer, A. M., van Scheppingen, J., et al. Coding and small non-coding transcriptional landscape of tuberous sclerosis complex cortical tubers: Implications for pathophysiology and treatment. Sci. Rep., 7(1), 8089 (2017).Google Scholar
Srivastava, P. K., van Eyll, J., Godard, P., et al. A systems-level framework for drug discovery identifies Csf1R as an anti-epileptic drug target. Nat. Commun., 9, 3561 (2018).Google Scholar
Lipponen, A., El-Osta, A., Kaspi, A., et al. Transcription factors Tp73, Cebpd, Pax6, and Spi1 rather than DNA methylation regulate chronic transcriptomics changes after experimental traumatic brain injury. Acta Neuropathol. Commun., 6(1), 17 (2018).CrossRefGoogle ScholarPubMed
Fazekas, D., Koltai, M., Türei, D., et al. SignaLink 2 – a signaling pathway resource with multi-layered regulatory networks. BMC Syst. Biol., 7, 7 (2013).Google Scholar
Duan, Q., Flynn, C., Niepel, M., et al. LINCS Canvas Browser: Interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures. Nucleic Acids Res., 42, W449–60 (2014).CrossRefGoogle ScholarPubMed
Hogg, M. C., Raoof, R., El Naggar, H., et al. Elevation in plasma tRNA fragments precede seizures in human epilepsy. J. Clin. Invest., 129(7), 2946–51 (2019).Google Scholar
Law, J. A., and Jacobsen, S. E. Establishing, maintaining and modifying DNA methylation patterns in plants and animals. Nat. Rev. Genet., 11(3), 204–20 (2010).Google Scholar
Lister, R., O’Malley, R. C., Tonti-Filippini, J., et al. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell, 133(3), 523–36 (2008).CrossRefGoogle ScholarPubMed
Cokus, S. J., Feng, S., Zhang, X., et al. Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature, 452(7184), 215–9 (2008).CrossRefGoogle ScholarPubMed
Kurdyukov, S., and Bullock, M. DNA methylation analysis: Choosing the right method. Biology (Basel), 5(1), 3 (2016).Google Scholar
Xi, Y., and Li, W. BSMAP: Whole genome bisulfite sequence MAPping program. BMC Bioinformatics, 10, 232 (2009).CrossRefGoogle ScholarPubMed
Hoffmann, S., Otto, C., Kurtz, S., et al. Fast mapping of short sequences with mismatches, insertions and deletions using index structures. PLoS Comput. Biol., 5(9), e1000502 (2009).Google Scholar
Krueger, F., Andrews, S. R. Bismark: A flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics, 27(11), 1571–2 (2011).Google Scholar
Guo, W., Fiziev, P., Yan, W., et al. BS-Seeker2: A versatile aligning pipeline for bisulfite sequencing data. BMC Genomics, 14, 774 (2013).CrossRefGoogle ScholarPubMed
Moran, S., Arribas, C., and Esteller, M. Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences. Epigenomics, 8(3), 389–99 (2016).Google Scholar
Bock, C. Analysing and interpreting DNA methylation data. Nat. Rev. Genet., 13(10), 705–19 (2012).CrossRefGoogle ScholarPubMed
Morris, T. J., Beck, S. Analysis pipelines and packages for Infinium HumanMethylation450 BeadChip (450k) data. Methods, 72, 3–8 (2015).CrossRefGoogle ScholarPubMed
Kodandapani, R., Pio, F., Ni, C. Z., et al. A new pattern for helix-turn-helix recognition revealed by the PU.1 ETS-domain-DNA complex. Nature, 380(6573):456–460 (1996).Google Scholar
Jin, Z., and Liu, Y. DNA methylation in human diseases. Genes Dis., 5(1), 1–8 (2018).Google Scholar
Kobow, K., and Blümcke, I. Epigenetics in epilepsy. Neurosci. Lett., 667, 40–6 (2018).Google Scholar
Levenson, J. M., Roth, T. L., Lubin, F. D., et al. Evidence that DNA (cytosine-5) methyltransferase regulates synaptic plasticity in the hippocampus. J. Biol. Chem., 281(23), 15763–73 (2006).Google Scholar
Nelson, E. D., Kavalali, E. T., and Monteggia, L. M. Activity-dependent suppression of miniature neurotransmission through the regulation of DNA methylation. J. Neurosci., 28(2), 395–406 (2008).Google Scholar
Feng, J., Zhou, Y., Campbell, S. L., et al. Dnmt1 and Dnmt3a maintain DNA methylation and regulate synaptic function in adult forebrain neurons. Nat. Neurosci., 113(4), 423–30 (2010).Google Scholar
Zhu, Q., Wang, L., Zhang, Y., et al. Increased expression of DNA methyltransferase 1 and 3a in human temporal lobe epilepsy. J. Mol. Neurosci., 46(2), 420–6 (2012).Google Scholar
Martinowich, K., Hattori, D., Wu, H., et al. DNA methylation-related chromatin remodeling in activity-dependent BDNF gene regulation. Science, 302(5646), 890–3 (2003).Google Scholar
Li, H. J., Wan, R. P., Tang, L. J., et al. Alteration of Scn3a expression is mediated via CpG methylation and MBD2 in mouse hippocampus during postnatal development and seizure condition. Biochim. Biophys. Acta, 1849(1), 1–9 (2015).CrossRefGoogle ScholarPubMed
Machnes, Z. M., Huang, T. C. T., Chang, P. K. Y., et al. DNA methylation mediates persistent epileptiform activity in vitro and in vivo. PLoS ONE, 8(10), e76299 (2013).CrossRefGoogle ScholarPubMed
Ryley Parrish, R., Albertson, A. J., Buckingham, S. C., et al. Status epilepticus triggers early and late alterations in brain-derived neurotrophic factor and NMDA glutamate receptor Grin2b DNA methylation levels in the hippocampus. Neuroscience, 248, 602–19 (2013).Google Scholar
Kobow, K., Jeske, I., Hildebrandt, M., et al. Increased reelin promoter methylation is associated with granule cell dispersion in human temporal lobe epilepsy. J. Neuropathol. Exp. Neurol., 68(4), 356–64 (2009).Google Scholar
Belhedi, N., Perroud, N., Karege, F., et al. Increased CPA6 promoter methylation in focal epilepsy and in febrile seizures. Epilepsy Res., 108(1), 144–8 (2014).CrossRefGoogle ScholarPubMed
Sapio, M. R., Salzmann, A., Vessaz, M., et al. Naturally occurring carboxypeptidase A6 mutations: Effect on enzyme function and association with epilepsy. J. Biol. Chem., 287(51), 42900–9 (2012).Google Scholar
Miller-Delaney, S. F. C., Das, S., Sano, T., et al. Differential DNA methylation patterns define status epilepticus and epileptic tolerance. J. Neurosci., 32(5), 1577–88 (2012).Google Scholar
Kobow, K., Kaspi, A., Harikrishnan, K. N., et al. Deep sequencing reveals increased DNA methylation in chronic rat epilepsy. Acta Neuropathol., 126(5), 741–56 (2013).Google Scholar
Debski, K. J., Pitkanen, A., Puhakka, N., et al. Etiology matters – genomic DNA methylation patterns in three rat models of acquired epilepsy. Sci. Rep., 6, 25668 (2016).Google Scholar
Miller-Delaney, S. F. C., Bryan, K., Das, S., et al. Differential DNA methylation profiles of coding and non-coding genes define hippocampal sclerosis in human temporal lobe epilepsy. Brain, 138(Pt 3), 616–31 (2015).Google Scholar
Kobow, K., Ziemann, M., Kaipananickal, H., et al. Genomic DNA methylation distinguishes subtypes of human focal cortical dysplasia. Epilepsia, 60(6), 1091–1103 (2019).Google Scholar
Stone, T. J., Keeley, A., Virasami, A., et al. Comprehensive molecular characterisation of epilepsy-associated glioneuronal tumours. Acta Neuropathol., 135(1), 115–29 (2018).Google Scholar
Guintivano, J., Aryee, M. J., and Kaminsky, Z. A. A cell epigenotype specific model for the correction of brain cellular heterogeneity bias and its application to age, brain region and major depression. Epigenetics, 8(3), 290–302 (2013).Google Scholar
Noack, F., and Calegari, F. Epitranscriptomics: A new regulatory mechanism of brain development and function. Front. Neurosci., 12, 85 (2018).Google Scholar
Chen, S., Zhang, J., Ruan, X., et al. Voxel-based morphometry analysis and machine learning based classification in pediatric mesial temporal lobe epilepsy with hippocampal sclerosis. Brain Imaging Behav., 14(5), 1945–54 (2019).Google Scholar
Bharath, R. D., Panda, R., Raj, J., and Bhardwaj, S. Machine learning identifies “rsfMRI epilepsy networks” in temporal lobe epilepsy. Eur. Radiol., 29(7), 3496–505 (2019).Google Scholar
Regalia, G., Onorati, F., Lai, M., Caborni, C., Pocard, R. W. Multimodal wrist-worn devices for seizure detection and advancing research: Focus on the Empatica wristbands. Epilepsy Res., 153, 79–82 (2019).Google Scholar
Mirza, B., Wang, W., Wang, J., et al. Machine learning and integrative analysis of biomedical big data. Genes (Basel), 10(2), 87 (2019).Google Scholar
Bertsimas, D., Pawlowski, C., and Zhuo, Y. D. From predictive methods to missing data imputation: An optimization approach. J. Mach. Learn. Res., 18(196), 1–39 (2018).Google Scholar
Saeys, Y., Inza, I., and Larrañaga, P. A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19), 2507–17 (2007).Google Scholar
Hira, Z. M., and Gillies, D. F. A review of feature selection and feature extraction methods applied on microarray data. Adv. Bioinformatics, 2015, 198363 (2015).Google Scholar
Chandrashekar, G., and Sahin, F. A survey on feature selection methods. Comput. Electr. Eng., 40(1), 16–28 (2014).Google Scholar
Meng, C., Zeleznik, O. A., Thallinger, G. G., et al. Dimension reduction techniques for the integrative analysis of multi-omics data. Brief. Bioinform., 17(4), 628–41 (2016).Google Scholar
Lin, E., and Lane, H. Y. Machine learning and systems genomics approaches for multi-omics data. Biomark. Res., 5, 2 (2017).CrossRefGoogle ScholarPubMed
Lopez de Maturana, E., Alonso, L., Alarcón, P., et al. Challenges in the integration of omics and non-omics data. Genes (Basel), 10(3), 238 (2019).Google Scholar
Long, N. P., Jung, K. H., Anh, N. H., et al. An integrative data mining and omics-based translational model for the identification and validation of oncogenic biomarkers of pancreatic cancer. Cancers (Basel), 11(2), 155 (2019).Google Scholar
Zhang, L., Lv, C., Jin, Y., et al. Deep learning-based multi-omics data integration reveals two prognostic subtypes in high-risk neuroblastoma. Front. Genet., 9, 477 (2018).Google Scholar
Ali, M., Khan, S. A., Wennerberg, K., and Aittokallio, T. Global proteomics profiling improves drug sensitivity prediction: Results from a multi-omics, pan-cancer modeling approach. Bioinformatics, 34(8), 1353–62 (2018).Google Scholar
Francescatto, M., Chierici, M., Rezvan Dezfooli, S., et al. Multi-omics integration for neuroblastoma clinical endpoint prediction. Biol. Direct, 13(1), 5 (2018).Google Scholar
Kim, B. J., and Kim, S. H. Prediction of inherited genomic susceptibility to 20 common cancer types by a supervised machine-learning method. Proc. Natl. Acad. Sci. USA, 115(6), 1322–7 (2018).Google Scholar
Yu, K. H., Berry, G. J., Rubin, D. L., et al. Association of omics features with histopathology patterns in lung adenocarcinoma. Cell Syst., 5(6), 620–7 (2017).Google Scholar

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