• 검색 결과가 없습니다.

Conclusion and Summary

In this dissertation, how to improve algorithms for pathway analysis of transcriptome data were addressed. Furthermore, computational and deployment issues in development process with applied algorithms were also introduced.

In chapter 2, improved pathway clustering with additional network information was addressed. It was observed that including PPI to similarity provides additional biological insights. The resulting R package ‘GScluster’, provides useful functions and visualizations for further biological research.

In chapter 3, an enhanced algorithm for pathway analysis and resampling method were introduced. By incorporating additional network information, the resulting R package ‘netGO’ provided more relevant gene-sets compared to conventional tools. In particular, when a small number of genes were used,

‘netGO’ showed better performances. Also, an additional package to visualize graph and network structure, ‘shinyCyJS’ was developed and included in ‘netGO’.

In chapter 4, a novel strategy for analysis single-cell RNA sequencing data was presented. By using cell-level analysis, more detailed pathway analysis can be performed. Moreover, it highlighted new pathways related to various biological functions. The R package ‘CellEnrich’ provided additional utilities to investigate the pathways enriched in the cell-level and visualizations.

In chapter 5, common and specific issues related to the development of R packages were discussed.

There are various challenges in computational field, besides the design of algorithm. In addition, brief process and implemented result related with chapter 2 and 3 are described. Although these issues are not related directly with biology, development of analysis applications needs to be more studied from an engineering point of view.

The presented contents in this dissertation have academic uniqueness in both biological and computational perspectives, and are expected to be applied to further researches in pathway analysis and development of R / shiny.



Adam, J. K., Odhav, B., & Bhoola, K. D. (2003). Immune responses in cancer. Pharmacology &

therapeutics, 99(1), 113-132.

Alexeyenko, A., Lee, W., Pernemalm, M., Guegan, J., Dessen, P., Lazar, V., . . . Pawitan, Y. (2012). Network enrichment analysis: extension of gene-set enrichment analysis to gene networks. BMC Bioinformatics, 13(1), 226. doi:10.1186/1471-2105-13-226

Alimonti, A., Carracedo, A., Clohessy, J. G., Trotman, L. C., Nardella, C., Egia, A., . . . Pandolfi, P. P.

(2010). Subtle variations in Pten dose determine cancer susceptibility. Nature Genetics, 42(5), 454-458. doi:10.1038/ng.556

Almaas, E., Vazquez, A., & Barabasi, A.-L. (2013). Scale-free networks in biology. Biological networks, 3. doi:10.1142/9789812772367_0001

Amberger, J. S., Bocchini, C. A., Schiettecatte, F., Scott, A. F., & Hamosh, A. (2015). OMIM. org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic acids research, 43(D1), D789-D798.

Arcangeli, A., Pillozzi, S., & Becchetti, A. (2012). Targeting ion channels in leukemias: a new challenge for treatment. Curr Med Chem, 19. doi:10.2174/092986712798992093

Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., . . . Eppig, J. T. (2000). Gene ontology: tool for the unification of biology. Nature Genetics, 25(1), 25-29.

Athar, A., Füllgrabe, A., George, N., Iqbal, H., Huerta, L., Ali, A., . . . Brazma, A. (2018). ArrayExpress update – from bulk to single-cell expression data. Nucleic acids research, 47(D1), D711- D715. doi:10.1093/nar/gky964

Baj-Krzyworzeka, M., Majka, M., Pratico, D., Ratajczak, J., Vilaire, G., Kijowski, J., . . . Ratajczak, M. Z.

(2002). Platelet-derived microparticles stimulate proliferation, survival, adhesion, and chemotaxis of hematopoietic cells. Experimental Hematology, 30(5), 450-459.


Ballouz, S., Pavlidis, P., & Gillis, J. (2017). Using predictive specificity to determine when gene set analysis is biologically meaningful. Nucleic acids research, 45(4), e20-e20.

Bannert, N., Farzan, M., Friend, D. S., Ochi, H., Price, K. S., Sodroski, J., & Boyce, J. A. (2001). Human Mast Cell Progenitors Can Be Infected by Macrophagetropic Human Immunodeficiency Virus Type 1 and Retain Virus with Maturation In Vitro. Journal of Virology, 75(22), 10808.


Barrett, T., Wilhite, S. E., Ledoux, P., Evangelista, C., Kim, I. F., Tomashevsky, M., . . . Holko, M. (2013).

NCBI GEO: archive for functional genomics data sets-update. Nucleic Acids Res, 41. doi:10.1093/nar/gks1193


Bennett, B. D., & Bushel, P. R. (2017). goSTAG: gene ontology subtrees to tag and annotate genes within a set. Source Code for Biology and Medicine, 12(1), 6. doi:10.1186/s13029-017-0066- 1

Bentley, B. (2017). Connectomics of extrasynaptic signalling: applications to the nervous system of Caenorhabditis elegans.

Bindea, G., Mlecnik, B., Hackl, H., Charoentong, P., Tosolini, M., Kirilovsky, A., . . . Galon, J. (2009).

ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics, 25(8), 1091-1093.

Buettner, F., Natarajan, K. N., Casale, F. P., Proserpio, V., Scialdone, A., Theis, F. J., . . . Stegle, O. (2015).

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nature Biotechnology, 33(2), 155-160.


Bumgarner, R. (2013). Overview of DNA microarrays: types, applications, and their future. Current protocols in molecular biology, Chapter 22, Unit-22.21.


Bush, W. S., & Moore, J. H. (2012). Genome-wide association studies. PLoS computational biology, 8(12).

Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018). Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature Biotechnology, 36(5), 411-420. doi:10.1038/nbt.4096

Chang, W., Cheng, J., Allaire, J., Xie, Y., & McPherson, J. (2017). Shiny: web application framework for R. R package version, 1(5).

Chatr-aryamontri, A., Breitkreutz, B.-J., Oughtred, R., Boucher, L., Heinicke, S., Chen, D., . . . Tyers, M.

(2014). The BioGRID interaction database: 2015 update. Nucleic acids research, 43(D1), D470-D478. doi:10.1093/nar/gku1204

Chiappetta, N., & Gruber, B. (2006). The Role of Mast Cells in Osteoporosis. Seminars in Arthritis and Rheumatism, 36(1), 32-36. doi:https://doi.org/10.1016/j.semarthrit.2006.03.004

Chiu, A., Ayub, M., Dive, C., Brady, G., & Miller, C. J. (2017). twoddpcr: an R/Bioconductor package and Shiny app for Droplet Digital PCR analysis. Bioinformatics, 33(17), 2743-2745.

Choi, Hae W., Brooking-Dixon, R., Neupane, S., Lee, C.-J., Miao, Edward A., Staats, Herman F., &

Abraham, Soman N. (2013). Salmonella Typhimurium Impedes Innate Immunity with a Mast- Cell-Suppressing Protein Tyrosine Phosphatase, SptP. Immunity, 39(6), 1108-1120.


Cline, M. S., Smoot, M., Cerami, E., Kuchinsky, A., Landys, N., Workman, C., . . . Gross, B. (2007).

Integration of biological networks and gene expression data using Cytoscape. Nature protocols, 2(10), 2366.

Collins, K., Jacks, T., & Pavletich, N. P. (1997). The cell cycle and cancer. Proceedings of the National

97 Academy of Sciences, 94(7), 2776-2778.

Consortium, G. O. (2019). The gene ontology resource: 20 years and still GOing strong. Nucleic acids research, 47(D1), D330-D338.

Cotto, K. C., Wagner, A. H., Feng, Y.-Y., Kiwala, S., Coffman, A. C., Spies, G., . . . Griffith, M. (2018).

DGIdb 3.0: a redesign and expansion of the drug–gene interaction database. Nucleic acids research, 46(D1), D1068-D1073.

Council, N. R. (2005). Catalyzing Inquiry at the Interface of Computing and Biology. Washington, DC:

The National Academies Press.

Creixell, P., Reimand, J., Haider, S., Wu, G., Shibata, T., Vazquez, M., . . . Sander, C. (2015). Pathway and network analysis of cancer genomes. Nature Methods, 12(7), 615.

Cunningham, F., Achuthan, P., Akanni, W., Allen, J., Amode, M. R., Armean, I. M., . . . Boddu, S. (2019).

Ensembl 2019. Nucleic acids research, 47(D1), D745-D751.

Currie, E., Schulze, A., Zechner, R., Walther, T. C., & Farese Jr, R. V. (2013). Cellular fatty acid metabolism and cancer. Cell metabolism, 18(2), 153-161.

Draghici, S., Khatri, P., Tarca, A. L., Amin, K., Done, A., Voichita, C., . . . Romero, R. (2007). A systems biology approach for pathway level analysis. Genome research, 17(10), 1537-1545.

Drost, H.-G., & Paszkowski, J. (2017). Biomartr: genomic data retrieval with R. Bioinformatics, 33(8), 1216-1217.

Du, J., Yuan, Z., Ma, Z., Song, J., Xie, X., & Chen, Y. (2014). KEGG-PATH: Kyoto encyclopedia of genes and genomes-based pathway analysis using a path analysis model. Molecular BioSystems, 10(9), 2441-2447. doi:10.1039/C4MB00287C

Duda, J., & Zoger, S. (2002). Presentation of M4 acute myeloid leukemia in anuric renal failure with hyperuricemia and enlarged kidneys. Journal of Pediatric Hematology Oncology, 24. doi:10.1097/00043426-200201000-00015

Elias, S., Yamin, R., Golomb, L., Tsukerman, P., Stanietsky-Kaynan, N., Ben-Yehuda, D., & Mandelboim, O. (2014). Immune evasion by oncogenic proteins of acute myeloid leukemia. Blood, 123. doi:10.1182/blood-2013-09-526590

Fairweather, D., Frisancho-Kiss, S., Yusung, S. A., Barrett, M. A., Davis, S. E., Gatewood, S. J. L., . . . Rose, N. R. (2004). Interferon-γ Protects against Chronic Viral Myocarditis by Reducing Mast Cell Degranulation, Fibrosis, and the Profibrotic Cytokines Transforming Growth Factor-β1, Interleukin-1β, and Interleukin-4 in the Heart. The American Journal of Pathology, 165(6), 1883-1894. doi:https://doi.org/10.1016/S0002-9440(10)63241-5

Feng, B.-S., He, S.-H., Zheng, P.-Y., Wu, L., & Yang, P.-C. (2007). Mast Cells Play a Crucial Role in Staphylococcus aureus Peptidoglycan-Induced Diarrhea. The American Journal of Pathology, 171(2), 537-547. doi:https://doi.org/10.2353/ajpath.2007.061274

Franz, M., Lopes, C. T., Huck, G., Dong, Y., Sumer, O., & Bader, G. D. (2015). Cytoscape. js: a graph theory library for visualisation and analysis. Bioinformatics, 32(2), 309-311.


Gardiner, C., Harrison, P., Chavda, N., MacKie, I., & Machin, S. (1999). Platelet activation responses in vitro to human mast cell activation. British journal of haematology, 106(1), 208-215.

Geerlings, S. E., & Hoepelman, A. I. (1999). Immune dysfunction in patients with diabetes mellitus (DM). FEMS Immunology & Medical Microbiology, 26(3-4), 259-265.

Glaab, E., Baudot, A., Krasnogor, N., Schneider, R., & Valencia, A. (2012). EnrichNet: network-based gene set enrichment analysis. Bioinformatics, 28(18), i451-i457.

Glass, K., & Girvan, M. (2014). Annotation enrichment analysis: an alternative method for evaluating the functional properties of gene sets. Scientific reports, 4, 4191.

Glazko, G. V., & Emmert-Streib, F. (2009). Unite and conquer: univariate and multivariate approaches for finding differentially expressed gene sets. Bioinformatics, 25(18), 2348-2354.

Goeman, J. J., & Bühlmann, P. (2007). Analyzing gene expression data in terms of gene sets:

methodological issues. Bioinformatics, 23(8), 980-987. doi:10.1093/bioinformatics/btm051 Gordon, J. R., & Galli, S. J. (1991). Release of both preformed and newly synthesized tumor necrosis

factor alpha (TNF-alpha)/cachectin by mouse mast cells stimulated via the Fc epsilon RI. A mechanism for the sustained action of mast cell-derived TNF-alpha during IgE-dependent biological responses. Journal of Experimental Medicine, 174(1), 103-107.


Goustin, A. S., Leof, E. B., Shipley, G. D., & Moses, H. L. (1986). Growth factors and cancer. Cancer research, 46(3), 1015-1029.

Grcevic, D., Marusic, A., Grahovac, B., Jaksic, B., & Kusec, R. (2003). Expression of bone morphogenetic proteins in acute promyelocytic leukemia before and after combined all trans-retinoic acid and cytotoxic treatment. Leuk Res, 27. doi:10.1016/S0145-2126(02)00281-3

Greene, C. S., Tan, J., Ung, M., Moore, J. H., & Cheng, C. (2014). Big data bioinformatics. Journal of cellular physiology, 229(12), 1896-1900.

Grün, D., Muraro, Mauro J., Boisset, J.-C., Wiebrands, K., Lyubimova, A., Dharmadhikari, G., . . . van Oudenaarden, A. (2016). De Novo Prediction of Stem Cell Identity using Single-Cell

Transcriptome Data. Cell Stem Cell, 19(2), 266-277.


Gu, Z., & Mullighan, C. G. (2019). ShinyCNV: a Shiny/R application to view and annotate DNA copy number variations. Bioinformatics, 35(1), 126-129.

Guirimand, T., Delmotte, S., & Navratil, V. (2014). VirHostNet 2.0: surfing on the web of virus/host molecular interactions data. Nucleic acids research, 43(D1), D583-D587.


Haddon, D. J., Hughes, M. R., Antignano, F., Westaway, D., Cashman, N. R., & McNagny, K. M. (2009).

Prion Protein Expression and Release by Mast Cells After Activation. The Journal of Infectious Diseases, 200(5), 827-831. doi:10.1086/605022

Hänzelmann, S., Castelo, R., & Guinney, J. (2013). GSVA: gene set variation analysis for microarray


and RNA-Seq data. BMC Bioinformatics, 14(1), 7. doi:10.1186/1471-2105-14-7

Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., . . . Guo, G. (2018). Mapping the Mouse Cell Atlas

by Microwell-Seq. Cell, 172(5), 1091-1107.e1017.


Henegar, C., Cancello, R., Rome, S., Vidal, H., Clément, K., & Zucker, J.-D. (2006). Clustering biological annotations and gene expression data to identify putatively co-regulated biological processes. Journal of bioinformatics and computational biology, 4(04), 833-852.

Henry, L., & Wickham, H. (2018). rlang: Functions for base types and core R and ‘tidyverse’features.

R Package Version 0.2. 0, available at https://CRAN. R-project. org/package= rlang, 655. Hepp, K. D. (1972). Adenylate Cyclase and Insulin Action: Effect of Insulin, Nonsuppressible Insulin‐

Like Material, and Diabetes on Adenylate‐Cyclase Activity in Mouse Liver. European journal of biochemistry, 31(2), 266-276.

Hinault, C., Kawamori, D., Liew, C. W., Maier, B., Hu, J., Keller, S. R., . . . Kulkarni, R. N. (2011). Δ40 Isoform of p53 controls β-cell proliferation and glucose homeostasis in mice. Diabetes, 60(4), 1210-1222.

Hitchcock, I. S., Fox, N. E., Prévost, N., Sear, K., Shattil, S. J., & Kaushansky, K. (2008). Roles of focal adhesion kinase (FAK) in megakaryopoiesis and platelet function: studies using a megakaryocyte lineage–specific FAK knockout. Blood, 111(2), 596-604. doi:10.1182/blood- 2007-05-089680

Hodge, A. M., English, D. R., O'Dea, K., Sinclair, A. J., Makrides, M., Gibson, R. A., & Giles, G. G. (2007).

Plasma phospholipid and dietary fatty acids as predictors of type 2 diabetes: interpreting the role of linoleic acid. The American Journal of Clinical Nutrition, 86(1), 189-197.


Hoheisel, J. D. (2006). Microarray technology: beyond transcript profiling and genotype analysis.

Nature Reviews Genetics, 7(3), 200-210. doi:10.1038/nrg1809

Hong, Y., Ho, K. S., Eu, K. W., & Cheah, P. Y. (2007). A susceptibility gene set for early onset colorectal cancer that integrates diverse signaling pathways: implication for tumorigenesis. Clinical Cancer Research, 13(4), 1107-1114.

Hu, Y., Jin, Y., Han, D., Zhang, G., Cao, S., Xie, J., . . . Wang, M. (2012). Mast Cell-Induced Lung Injury in Mice Infected with H5N1 Influenza Virus. Journal of Virology, 86(6), 3347.


Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2008). Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic acids research, 37(1), 1- 13. doi:10.1093/nar/gkn923

Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009a). Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic acids research, 37(1), 1-13.


Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009b). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols, 4(1), 44-57.


Huang, X., & Jan, L. Y. (2014). Targeting potassium channels in cancer. J Cell Biol, 206. doi:10.1083/jcb.201404136

Hwang, S., Kim, C. Y., Yang, S., Kim, E., Hart, T., Marcotte, E. M., & Lee, I. (2019). HumanNet v2: human gene networks for disease research. Nucleic acids research, 47(D1), D573-D580.

Iwawaki, T., & Oikawa, D. (2013). The role of the unfolded protein response in diabetes mellitus.

Paper presented at the Seminars in immunopathology.

Jagadish, H. V., & Olken, F. (2004). Database management for life sciences research. SIGMOD Rec., 33(2), 15–20. doi:10.1145/1024694.1024697

Jensen, M. V., Haldeman, J. M., Zhang, H., Lu, D., Huising, M. O., Vale, W. W., . . . Newgard, C. B.

(2013). Control of voltage-gated potassium channel Kv2. 2 expression by pyruvate-isocitrate cycling regulates glucose-stimulated insulin secretion. Journal of Biological Chemistry, 288(32), 23128-23140.

Jochum, W., Passegué, E., & Wagner, E. F. (2001). AP-1 in mouse development and tumorigenesis.

Oncogene, 20(19), 2401-2412.

Kahn, S. (2003). The relative contributions of insulin resistance and beta-cell dysfunction to the pathophysiology of type 2 diabetes. Diabetologia, 46(1), 3-19.

Kanehisa, M., & Goto, S. (2000). KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic acids research, 28(1), 27-30. doi:10.1093/nar/28.1.27

Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M., & Tanabe, M. (2016). KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res, 44. doi:10.1093/nar/gkv1070 Kashyap, H., Ahmed, H. A., Hoque, N., Roy, S., & Bhattacharyya, D. K. (2015). Big data analytics in

bioinformatics: A machine learning perspective. arXiv preprint arXiv:1506.05101.

Kataoka, T. R., Fujimoto, M., Moriyoshi, K., Koyanagi, I., Ueshima, C., Kono, F., . . . Haga, H. (2013).

PD-1 regulates the growth of human mastocytosis cells. Allergology International, 62(1), 99- 104.

Keller, M. P., Choi, Y., Wang, P., Davis, D. B., Rabaglia, M. E., Oler, A. T., . . . Edwards, S. (2008). A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility. Genome research, 18(5), 706-716.

Kentsis, A., Reed, C., Rice, K. L., Sanda, T., Rodig, S. J., Tholouli, E., . . . Ngo, V. (2012). Autocrine activation of the MET receptor tyrosine kinase in acute myeloid leukemia. Nat Med, 18. doi:10.1038/nm.2819

Khatri, P., Sirota, M., & Butte, A. J. (2012). Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges. PLoS computational biology, 8(2), e1002375.



Khaznadar, Z., Henry, G., Setterblad, N., Agaugue, S., Raffoux, E., Boissel, N., . . . Dulphy, N. (2014).

Acute myeloid leukemia impairs natural killer cells through the formation of a deficient cytotoxic immunological synapse. Eur J Immunol, 44. doi:10.1002/eji.201444500

Kim, E., Hwang, S., Kim, H., Shim, H., Kang, B., Yang, S., . . . Lee, I. (2016). MouseNet v2: a database of gene networks for studying the laboratory mouse and eight other model vertebrates.

Nucleic acids research, 44(D1), D848-D854.

Kim, H., Shin, J., Kim, E., Kim, H., Hwang, S., Shim, J. E., & Lee, I. (2014). YeastNet v3: a public database of data-specific and integrated functional gene networks for Saccharomyces cerevisiae.

Nucleic acids research, 42(D1), D731-D736.

Kim, J., Yoon, S., & Nam, D. (2020). netGO: R-Shiny package for network-integrated pathway enrichment analysis. Bioinformatics.

Kim, W., Shin, Y.-K., Kim, B.-J., & Egan, J. M. (2010). Notch signaling in pancreatic endocrine cell and diabetes. Biochemical and biophysical research communications, 392(3), 247-251.

Koeffler, H. P., & Golde, D. W. (1980). Humoral modulation of human acute myelogenous leukemia cell growth in vitro. Cancer Res, 40.

Koeppen, K., Stanton, B. A., & Hampton, T. H. (2017). ScanGEO: parallel mining of high-throughput gene expression data. Bioinformatics, 33(21), 3500-3501.

Kohase, M., May, L. T., Tamm, I., Vilcek, J., & Sehgal, P. B. (1987). A cytokine network in human diploid fibroblasts: interactions of beta-interferons, tumor necrosis factor, platelet-derived growth factor, and interleukin-1. Molecular and Cellular Biology, 7(1), 273. doi:10.1128/MCB.7.1.273 Kotini, A. G., Chang, C.-J., Chow, A., Yuan, H., Ho, T.-C., Wang, T., . . . Olszewska, M. (2017). Stage-

specific human induced pluripotent stem cells map the progression of myeloid transformation to transplantable leukemia. Cell Stem Cell, 20(3), 315-328. e317.

Kunicki, T. J., Nugent, D., Staats, S., Orchekowski, R., Wayner, E., & Carter, W. (1988). The human fibroblast class II extracellular matrix receptor mediates platelet adhesion to collagen and is identical to the platelet glycoprotein Ia-IIa complex. Journal of Biological Chemistry, 263(10), 4516-4519.

Kurosaki, T., Gander, I., Wirthmueller, U., & Ravetch, J. V. (1992). The beta subunit of the Fc epsilon RI is associated with the Fc gamma RIII on mast cells. Journal of Experimental Medicine, 175(2), 447-451. doi:10.1084/jem.175.2.447

Lam, F. W., Burns, A. R., Smith, C. W., & Rumbaut, R. E. (2011). Platelets enhance neutrophil transendothelial migration via P-selectin glycoprotein ligand-1. American Journal of Physiology-Heart and Circulatory Physiology, 300(2), H468-H475.


Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META group research note, 6(70), 1.

Lawlor, N., George, J., Bolisetty, M., Kursawe, R., Sun, L., Sivakamasundari, V., . . . Stitzel, M. L. (2017).


Single-cell transcriptomes identify human islet cell signatures and reveal cell-type–specific expression changes in type 2 diabetes. Genome research, 27(2), 208-222.

Lee, T., Yang, S., Kim, E., Ko, Y., Hwang, S., Shin, J., . . . Kim, C. (2015). AraNet v2: an improved database of co-functional gene networks for the study of Arabidopsis thaliana and 27 other nonmodel plant species. Nucleic acids research, 43(D1), D996-D1002.

Lewis, J. C., Maldonado, J. E., & Mann, K. (1976). Phagocytosis in human platelets: localization of acid phosphatase-positive phagosomes following latex uptake.

Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, Jill P., & Tamayo, P. (2015). The Molecular Signatures Database Hallmark Gene Set Collection. Cell Systems, 1(6), 417-425.


Liberzon, A., Subramanian, A., Pinchback, R., Thorvaldsdóttir, H., Tamayo, P., & Mesirov, J. P. (2011).

Molecular signatures database (MSigDB) 3.0. Bioinformatics, 27(12), 1739-1740.


Licata, L., Briganti, L., Peluso, D., Perfetto, L., Iannuccelli, M., Galeota, E., . . . Cesareni, G. (2011). MINT, the molecular interaction database: 2012 update. Nucleic acids research, 40(D1), D857-D861.


Louis, C. U., & Butani, L. (2008). High blood pressure and hypertension in children with newly diagnosed acute leukemia and lymphoma. Pediatr Nephrol, 23. doi:10.1007/s00467-007- 0720-y

Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550.

Lun, A., McCarthy, D., & Marioni, J. (2016). A step-by-step workflow for low-level analysis of single- cell RNA-seq data with Bioconductor [version 2; peer review: 3 approved, 2 approved with reservations]. F1000Research, 5(2122). doi:10.12688/f1000research.9501.2

Maaten, L. v. d., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of machine learning research, 9(Nov), 2579-2605.

Maere, S., Heymans, K., & Kuiper, M. (2005). BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics, 21(16), 3448-3449.

Mahmud H, Scherpen FJG, Meeuwsen-de Boer T, Lourens HJ, de Bont ES. Essential role for phospholipase C gamma 1 (PLC-gamma 1) in the survival of t(8;21) acute myeloid leukemia.

Blood. 2016;(22):128.

Masson, N., & Ratcliffe, P. J. (2014). Hypoxia signaling pathways in cancer metabolism: the importance of co-selecting interconnected physiological pathways. Cancer & metabolism, 2(1), 3.

Matsunaga, Y., & Terada, T. (2000). Mast cell subpopulations in chronic inflammatory hepatobiliary diseases. Liver, 20(2), 152-156. doi:10.1034/j.1600-0676.2000.020002152.x

Mauro, M. i. c. h. a. e. l. J. (2003). Hyperleukocytosis in Acute Myeloid Leukemia. New England Journal

103 of Medicine, 349. doi:10.1056/NEJMicm010149

McInnes, L., Healy, J., & Melville, J. (2018). Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426.

McMurdie, P. J., & Holmes, S. (2015). Shiny-phyloseq: Web application for interactive microbiome analysis with provenance tracking. Bioinformatics, 31(2), 282-283.

Merelli, I., Pérez-Sánchez, H., Gesing, S., & D’Agostino, D. (2014). Managing, analysing, and integrating big data in medical bioinformatics: open problems and future perspectives.

BioMed research international, 2014.

Merico, D., Isserlin, R., Stueker, O., Emili, A., & Bader, G. D. (2010). Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation. PLOS ONE, 5(11), e13984.


Mihaila, R. G., Olteanu, A., Dragomir, I., & Morar, S. (2015). Pulmonary arterial hypertension emerged in a patient with acute myeloid leukemia. The role of transfusions. Biomedical Research- India, 26.

Minamino, T., Orimo, M., Shimizu, I., Kunieda, T., Yokoyama, M., Ito, T., . . . Matsubara, H. (2009). A crucial role for adipose tissue p53 in the regulation of insulin resistance. Nature medicine, 15(9), 1082.

Mishra, A., & Macgregor, S. (2015). VEGAS2: software for more flexible gene-based testing. Twin Research and Human Genetics, 18(1), 86-91.

Morris, A. P., Voight, B. F., Teslovich, T. M., Ferreira, T., Segre, A. V., Steinthorsdottir, V., . . . Mahajan, A. (2012). Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nature Genetics, 44(9), 981.

Mostafavi, S., Ray, D., Warde-Farley, D., Grouios, C., & Morris, Q. (2008). GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biology, 9(1), S4. doi:10.1186/gb-2008-9-s1-s4

Muraro, Mauro J., Dharmadhikari, G., Grün, D., Groen, N., Dielen, T., Jansen, E., . . . van Oudenaarden, A. (2016). A Single-Cell Transcriptome Atlas of the Human Pancreas. Cell Systems, 3(4), 385- 394.e383. doi:https://doi.org/10.1016/j.cels.2016.09.002

Nam, D., & Kim, S.-Y. (2008). Gene-set approach for expression pattern analysis. Briefings in Bioinformatics, 9(3), 189-197. doi:10.1093/bib/bbn001

Nasdala, I., Wolburg-Buchholz, K., Wolburg, H., Kuhn, A., Ebnet, K., Brachtendorf, G., . . . Vestweber, D. (2002). A transmembrane tight junction protein selectively expressed on endothelial cells and platelets. Journal of Biological Chemistry, 277(18), 16294-16303.

Nelson, D. L., Lehninger, A. L., & Cox, M. M. (2008). Lehninger principles of biochemistry: Macmillan.

O’Bryant, D. M., Nilles, M. L., & Bradley, D. S. (2007). Mast cells play a critical role in host defenses against Yersinia pestis (44.8). The Journal of Immunology, 178(1 Supplement), S49. Retrieved from http://www.jimmunol.org/content/178/1_Supplement/S49.4.abstract


O’Driscoll, A., Daugelaite, J., & Sleator, R. D. (2013). ‘Big data’, Hadoop and cloud computing in genomics. Journal of biomedical informatics, 46(5), 774-781.

Ogris, C., Guala, D., Helleday, T., & Sonnhammer, E. L. (2017). A novel method for crosstalk analysis of biological networks: improving accuracy of pathway annotation. Nucleic acids research, 45(2), e8-e8.

Olivera, A., & Rivera, J. (2005). Sphingolipids and the Balancing of Immune Cell Function: Lessons from the Mast Cell. The Journal of Immunology, 174(3), 1153.


Olsson, N., Siegbahn, A., & Nilsson, G. (1999). Serum Amyloid A Induces Chemotaxis of Human Mast Cells by Activating a Pertussis Toxin-Sensitive Signal Transduction Pathway. Biochemical and biophysical research communications, 254(1), 143-146.


Oomen, S. P. M. A., Lichtenauer-Kaligis, E. G. R., Verplanke, N., Hofland, J., Lamberts, S. W. J., Lowenberg, B., & Touw, I. P. (2001). Somatostatin induces migration of acute myeloid leukemia cells via activation of somatostatin receptor subtype 2. Leukemia, 15. doi:10.1038/sj.leu.2402061

Orchard, S., Kerrien, S., Abbani, S., Aranda, B., Bhate, J., Bidwell, S., . . . Hermjakob, H. (2012). Protein interaction data curation: the International Molecular Exchange (IMEx) consortium. Nature Methods, 9(4), 345-350. doi:10.1038/nmeth.1931

Oshlack, A., & Wakefield, M. J. (2009). Transcript length bias in RNA-seq data confounds systems biology. Biology direct, 4(1), 14.

Pardanani, A., Reeder, T. L., Kimlinger, T. K., Baek, J. Y., Li, C.-Y., Butterfield, J. H., & Tefferi, A. (2003).

Flt-3 and c-kit mutation studies in a spectrum of chronic myeloid disorders including systemic mast cell disease. Leukemia Research, 27(8), 739-742.


Patel, S., & Santani, D. (2009). Role of NF-κB in the pathogenesis of diabetes and its associated complications. Pharmacological Reports, 61(4), 595-603.

Pavlidis, P., Qin, J., Arango, V., Mann, J. J., & Sibille, E. (2004). Using the gene ontology for microarray data mining: a comparison of methods and application to age effects in human prefrontal cortex. Neurochemical research, 29(6), 1213-1222.

Peracchi, M., Lombardi, L., Maiolo, A. T., Bamonti-Catena, F., Toschi, V., Chiorboli, O., . . . Polli, E. E.

(1983). Plasma and urine cyclic nucleotide levels in patients with acute and chronic leukemia.

Blood, 61.

Pontén, A., Li, X., Thorén, P., Aase, K., Sjöblom, T., Östman, A., & Eriksson, U. (2003). Transgenic Overexpression of Platelet-Derived Growth Factor-C in the Mouse Heart Induces Cardiac Fibrosis, Hypertrophy, and Dilated Cardiomyopathy. The American Journal of Pathology, 163(2), 673-682. doi:https://doi.org/10.1016/S0002-9440(10)63694-2