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Ilya Shmulevich received his Ph.D. in Electrical and Computer Engineering from Purdue University, West Lafayette, IN, in 1997. His graduate research was in the area of nonlinear signal processing, with a focus on the theory and design of nonlinear digital filters, Boolean algebra, lattice theory, and applications to music pattern recognition. From 1997-1998, he was a postdoctoral researcher at the Nijmegen Institute for Cognition and Information at the University of Nijmegen (now Radboud University) and National Research Institute for Mathematics and Computer Science at the University of Amsterdam in The Netherlands, where he studied computational models of music perception and recognition, focusing on tonality induction and rhythm complexity. In 1998-2000, he worked as a senior researcher at the Tampere International Center for Signal Processing at the Signal Processing Laboratory in Tampere University of Technology, Tampere, Finland. While in Tampere, he did research in nonlinear systems, image recognition and classification, image correspondence, computational learning theory, multiscale and spectral methods, and statistical signal processing.

This background proved to be fruitful for undertaking problems in computational biology at a time when genomic technologies were beginning to produce large amounts of data. In 2001, he joined the Department of Pathology at The University of Texas M. D. Anderson Cancer Center as an Assistant Professor and held an adjunct faculty appointment in the Department of Statistics in Rice University. He and his colleagues developed statistical approaches for cancer classification, diagnosis, and prognosis, and applied them to the study of metastasis, cancer progression, and tumor heterogeneity for multiple different cancer types. He co-developed the model class of probabilistic Boolean networks (PBNs), which has been applied to the study of gene regulatory networks in cancer.

Dr. Shmulevich joined the ISB faculty in 2005 where he was a Professor, directing a Genome Data Analysis Center within The Cancer Genome Atlas (TCGA) consortium. He also directed one of three NCI Cancer Genomics Cloud Pilots, which is now operating as an NCI Cancer Genomics Cloud Resource (isb-cgc.org).

Dr. Shmulevich’s research interests included theoretical studies of complex systems, including information theoretic approaches, as well as the application of image processing and analysis to high-throughput cellular imaging. His main research interest was multiscale modeling for cancer therapy.

Dr. Shmulevich was a co-author or co-editor of six books in the areas of computational biology. He held Affiliate Professor appointments in the Departments of Bioengineering and Electrical Engineering at the University of Washington, and also held affiliate appointments in the Department of Signal Processing in Tampere University of Technology, Finland and in the Department of Electronic and Electrical Engineering in Strathclyde University, Glasgow, UK.

PhD, Electrical and Computer Engineering, Purdue University, 1997

  • Computational Systems Biology
  • Cancer Research, Immuno-oncology
  • Signal and Image Processing
  • Complex Systems Theory
  • Computational Learning Theory, Machine Learning
  • Multiscale Theory and Analysis
  • Complexity of Algorithms
  • Music Pattern Recognition
  • Cognition, Perception, and Mathematical Psychology
Ellrott, K., Wong, C. K., Yau, C., Castro, M. A. A., Lee, J. A., Karlberg, B. J., Grewal, J. K., Lagani, V., Tercan, B., Friedl, V., Hinoue, T., Uzunangelov, V., Westlake, L., Loinaz, X., Felau, I., Wang, P. I., Kemal, A., Caesar-Johnson, S. J., Shmulevich, I., … Laird, P. W. (2025). Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets. Cancer Cell. https://doi.org/10.1016/j.ccell.2024.12.002 Cite
Qin, G., Dai, J., Chien, S., Martins, T. J., Loera, B., Nguyen, Q. H., Oakes, M. L., Tercan, B., Aguilar, B., Hagen, L., McCune, J., Gelinas, R., Monnat, R. J., Shmulevich, I., & Becker, P. S. (2024). Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research, 30(12), 2659–2671. https://doi.org/10.1158/1078-0432.CCR-23-1674 Cite Download
L Rocha, H., Aguilar, B., Getz, M., Shmulevich, I., & Macklin, P. (2024). A multiscale model of immune surveillance in micrometastases gives insights on cancer patient digital twins. NPJ Systems Biology and Applications, 10(1), 144. https://doi.org/10.1038/s41540-024-00472-z Cite
Ellrott, K., Wong, C. K., Yau, C., Castro, M. A., Lee, J., Karlberg, B., Grewal, J. K., Lagani, V., Tercan, B., Friedl, V., Hinoue, T., Uzunangelov, V., Westlake, L., Loinaz, X., Felau, I., Wang, P., Kemal, A., Caesar-Johnson, S. J., Shmulevich, I., … Laird, P. W. (2024). Abstract 6548: Leveraging compact feature sets for TCGA-based molecular subtype classification on new samples. Cancer Research, 84(6_Supplement), 6548. https://doi.org/10.1158/1538-7445.AM2024-6548 Cite
Qin, G., Narsinh, K., Wei, Q., Roach, J. C., Joshi, A., Goetz, S. L., Moxon, S. T., Brush, M. H., Xu, C., Yao, Y., Glen, A. K., Morris, E. D., Ralevski, A., Roper, R., Belhu, B., Zhang, Y., Shmulevich, I., Hadlock, J., & Glusman, G. (2024). Generating Biomedical Knowledge Graphs from Knowledge Bases, Registries, and Multiomic Data. bioRxiv. https://doi.org/10.1101/2024.11.14.623648 Cite Download
Qin, G., Zhang, Y., Tyner, J. W., Kemp, C. J., & Shmulevich, I. (2024). Knowledge graphs facilitate prediction of drug response for acute myeloid leukemia. IScience, 27(9). https://doi.org/10.1016/j.isci.2024.110755 Cite Download
Laubenbacher, R., Mehrad, B., Shmulevich, I., & Trayanova, N. (2024). Digital twins in medicine. Nature Computational Science, 4(3), 184–191. https://doi.org/10.1038/s43588-024-00607-6 Cite Download
Laubenbacher, R., Adler, F., An, G., Castiglione, F., Eubank, S., Fonseca, L. L., Glazier, J., Helikar, T., Jett-Tilton, M., Kirschner, D., Macklin, P., Mehrad, B., Moore, B., Pasour, V., Shmulevich, I., Smith, A., Voigt, I., Yankeelov, T. E., & Ziemssen, T. (2024). Forum on immune digital twins: a meeting report. NPJ Systems Biology and Applications, 10(1), 19. https://doi.org/10.1038/s41540-024-00345-5 Cite Download
Laubenbacher, R., Adler, F., An, G., Castiglione, F., Eubank, S., Fonseca, L. L., Glazier, J., Helikar, T., Jett-Tilton, M., Kirschner, D., Macklin, P., Mehrad, B., Moore, B., Pasour, V., Shmulevich, I., Smith, A., Voigt, I., Yankeelov, T. E., & Ziemssen, T. (2024). Toward mechanistic medical digital twins: some use cases in immunology. Frontiers in Digital Health, 6, 1349595. https://doi.org/10.3389/fdgth.2024.1349595 Cite Download
Zhang, Y., Qin, G., Aguilar, B., Rappaport, N., Yurkovich, J. T., Pflieger, L., Huang, S., Hood, L., & Shmulevich, I. (2024). A framework towards digital twins for type 2 diabetes. Frontiers in Digital Health, 6, 1336050. https://doi.org/10.3389/fdgth.2024.1336050 Cite Download
Nikolić, V., Echlin, M., Aguilar, B., & Shmulevich, I. (2023). Computational capabilities of a multicellular reservoir computing system. PloS One, 18(4), e0282122. https://doi.org/10.1371/journal.pone.0282122 Cite Download
Echlin, M., Aguilar, B., & Shmulevich, I. (2023). Characterizing the Impact of Communication on Cellular and Collective Behavior Using a Three-Dimensional Multiscale Cellular Model. Entropy (Basel, Switzerland), 25(2), 319. https://doi.org/10.3390/e25020319 Cite Download
Stahlberg, E. A., Abdel-Rahman, M., Aguilar, B., Asadpoure, A., Beckman, R. A., Borkon, L. L., Bryan, J. N., Cebulla, C. M., Chang, Y. H., Chatterjee, A., Deng, J., Dolatshahi, S., Gevaert, O., Greenspan, E. J., Hao, W., Hernandez-Boussard, T., Jackson, P. R., Kuijjer, M., Lee, A., … Zervantonakis, I. (2022). Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation. Frontiers in Digital Health, 4, 1007784. https://doi.org/10.3389/fdgth.2022.1007784 Cite Download
Tercan, B., Qin, G., Kim, T.-K., Aguilar, B., Phan, J., Longabaugh, W., Pot, D., Kemp, C. J., Chambwe, N., & Shmulevich, I. (2022). SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery. F1000Research, 11, 493. https://doi.org/10.12688/f1000research.110903.2 Cite Download
Thorsson, V., Heimann, C., Lamb, A., Gibbs, D., Bortone, D., Dexheimer, S., Vensko, S., Chae, Y., Shmulevich, I., Vincent, B., & Eddy, J. (2022). 927 Streamlining cancer immunotherapy research with the CRI iAtlas data resource and web portal. Journal for ImmunoTherapy of Cancer, 10(Suppl 2). https://doi.org/10.1136/jitc-2022-SITC2022.0927 Cite Download
Nikolić, V., Echlin, M., Aguilar, B., & Shmulevich, I. (2022). Multicellular Reservoir Computing. bioRxiv. https://doi.org/10.1101/2022.03.26.485905 Cite Download
Silverstein, A., Dudaev, A., Studneva, M., Aitken, J., Blokh, S., Miller, A. D., Tanasova, S., Rose, N., Ryals, J., Borchers, C., Nordström, A., Moiseyakh, M., Herrera, A. S., Skomorohov, N., Marshall, T., Wu, A., Cheng, R. H., Syzko, K., Cotter, P. D., … Suchkov, S. (2022). Evolution of biomarker research in autoimmunity conditions for health professionals and clinical practice (pp. 219–276). Elsevier. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-194436 Cite
Tercan, B., Aguilar, B., Huang, S., Dougherty, E. R., & Shmulevich, I. (2022). Probabilistic boolean networks predict transcription factor targets to induce transdifferentiation. IScience, 25(9). https://doi.org/10.1016/j.isci.2022.104951 Cite Download
Moser, R., Gurley, K. E., Nikolova, O., Qin, G., Joshi, R., Mendez, E., Shmulevich, I., Ashley, A., Grandori, C., & Kemp, C. J. (2022). Synthetic lethal kinases in Ras/p53 mutant squamous cell carcinoma. Oncogene. https://doi.org/10.1038/s41388-022-02330-w Cite
Qin, G., Knijnenburg, T. A., Gibbs, D. L., Moser, R., Monnat, R. J., Kemp, C. J., & Shmulevich, I. (2022). A functional module states framework reveals transcriptional states for drug and target prediction. Cell Reports, 38(3), 110269. https://doi.org/10.1016/j.celrep.2021.110269 Cite Download
Hernandez-Boussard, T., Macklin, P., Greenspan, E. J., Gryshuk, A. L., Stahlberg, E., Syeda-Mahmood, T., & Shmulevich, I. (2021). Digital twins for predictive oncology will be a paradigm shift for precision cancer care. Nature Medicine. https://doi.org/10.1038/s41591-021-01558-5 Cite
Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S., Aerts, H. J. W. L., Homeyer, A., Lewis, R., Akbarzadeh, A., Bontempi, D., Clifford, W., Herrmann, M. D., Höfener, H., Octaviano, I., Osborne, C., Paquette, S., Petts, J., Punzo, D., Reyes, M., … Kikinis, R. (2021). NCI Imaging Data Commons. Cancer Research, 81(16), 4188–4193. https://doi.org/10.1158/0008-5472.CAN-21-0950 Cite Download
Gibbs, D. L., Aguilar, B., Thorsson, V., Ratushny, A. V., & Shmulevich, I. (2021). Patient-Specific Cell Communication Networks Associate With Disease Progression in Cancer. Frontiers in Genetics, 12, 667382. https://doi.org/10.3389/fgene.2021.667382 Cite Download
Lo, Y.-H., Kolahi, K. S., Du, Y., Chang, C.-Y., Krokhotin, A., Nair, A., Sobba, W. D., Karlsson, K., Jones, S. J., Longacre, T. A., Mah, A. T., Tercan, B., Sockell, A., Xu, H., Seoane, J. A., Chen, J., Shmulevich, I., Weissman, J. S., Curtis, C., … Kuo, C. J. (2021). A CRISPR/Cas9-engineered ARID1A-deficient human gastric cancer organoid model reveals essential and non-essential modes of oncogenic transformation. Cancer Discovery. https://doi.org/10.1158/2159-8290.CD-20-1109 Cite Download
Eddy, J. A., Thorsson, V., Lamb, A. E., Gibbs, D. L., Heimann, C., Yu, J. X., Chung, V., Chae, Y., Dang, K., Vincent, B. G., Shmulevich, I., & Guinney, J. (2020). CRI iAtlas: an interactive portal for immuno-oncology research. F1000Research, 9, 1028. https://doi.org/10.12688/f1000research.25141.1 Cite Download
Xavier, J. B., Young, V. B., Skufca, J., Ginty, F., Testerman, T., Pearson, A. T., Macklin, P., Mitchell, A., Shmulevich, I., Xie, L., Caporaso, J. G., Crandall, K. A., Simone, N. L., Godoy-Vitorino, F., Griffin, T. J., Whiteson, K. L., Gustafson, H. H., Slade, D. J., Schmidt, T. M., … Wargo, J. A. (2020). The Cancer Microbiome: Distinguishing Direct and Indirect Effects Requires a Systemic View. Trends in Cancer, 6(3), 192–204. https://doi.org/10.1016/j.trecan.2020.01.004 Cite Download
Peterson, E. J. R., Abidi, A. A., Arrieta-Ortiz, M. L., Aguilar, B., Yurkovich, J. T., Kaur, A., Pan, M., Srinivas, V., Shmulevich, I., & Baliga, N. S. (2020). Intricate Genetic Programs Controlling Dormancy in Mycobacterium tuberculosis. Cell Reports, 31(4), 107577. https://doi.org/10.1016/j.celrep.2020.107577 Cite Download
Aguilar, B., Gibbs, D. L., Reiss, D. J., McConnell, M., Danziger, S. A., Dervan, A., Trotter, M., Bassett, D., Hershberg, R., Ratushny, A. V., & Shmulevich, I. (2020). A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma. GigaScience, 9(7). https://doi.org/10.1093/gigascience/giaa075 Cite Download
Cao, S., Zhou, D. C., Oh, C., Jayasinghe, R. G., Zhao, Y., Yoon, C. J., Wyczalkowski, M. A., Bailey, M. H., Tsou, T., Gao, Q., Malone, A., Reynolds, S., Shmulevich, I., Wendl, M. C., Chen, F., & Ding, L. (2020). Discovery of driver non-coding splice-site-creating mutations in cancer. Nature Communications, 11(1), 5573. https://doi.org/10.1038/s41467-020-19307-6 Cite Download
Danziger, S. A., Gibbs, D. L., Shmulevich, I., McConnell, M., Trotter, M. W. B., Schmitz, F., Reiss, D. J., & Ratushny, A. V. (2019). ADAPTS: Automated deconvolution augmentation of profiles for tissue specific cells. PloS One, 14(11), e0224693. https://doi.org/10.1371/journal.pone.0224693 Cite Download
Knijnenburg, T. A., Vockley, J. G., Chambwe, N., Gibbs, D. L., Humphries, C., Huddleston, K. C., Klein, E., Kothiyal, P., Tasseff, R., Dhankani, V., Bodian, D. L., Wong, W. S. W., Glusman, G., Mauldin, D. E., Miller, M., Slagel, J., Elasady, S., Roach, J. C., Kramer, R., … Niederhuber, J. E. (2019). Genomic and molecular characterization of preterm birth. Proceedings of the National Academy of Sciences of the United States of America, 116(12), 5819–5827. https://doi.org/10.1073/pnas.1716314116 Cite Download
Thorsson, V., Gibbs, D. L., Brown, S. D., Wolf, D., Bortone, D. S., Ou Yang, T.-H., Porta-Pardo, E., Gao, G. F., Plaisier, C. L., Eddy, J. A., Ziv, E., Culhane, A. C., Paull, E. O., Sivakumar, I. K. A., Gentles, A. J., Malhotra, R., Farshidfar, F., Colaprico, A., Parker, J. S., … Shmulevich, I. (2018). The Immune Landscape of Cancer. Immunity, 48(4), 812-830.e14. https://doi.org/10.1016/j.immuni.2018.03.023 Cite Download
Orozco, J. I. J., Knijnenburg, T. A., Manughian-Peter, A. O., Salomon, M. P., Barkhoudarian, G., Jalas, J. R., Wilmott, J. S., Hothi, P., Wang, X., Takasumi, Y., Buckland, M. E., Thompson, J. F., Long, G. V., Cobbs, C. S., Shmulevich, I., Kelly, D. F., Scolyer, R. A., Hoon, D. S. B., & Marzese, D. M. (2018). Epigenetic profiling for the molecular classification of metastatic brain tumors. Nature Communications, 9(1), 4627. https://doi.org/10.1038/s41467-018-06715-y Cite Download
Kang, C., Aguilar, B., & Shmulevich, I. (2018). Emergence of diversity in homogeneous coupled Boolean networks. Physical Review. E, 97(5–1), 052415. https://doi.org/10.1103/PhysRevE.97.052415 Cite
Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T., Batiste, R., Arnam, J. V., Caesar-Johnson, S. J., Demchok, J. A., Felau, I., Kasapi, M., Ferguson, M. L., Hutter, C. M., Sofia, H. J., Tarnuzzer, R., … Thorsson, V. (2018). Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Reports, 23(1), 181-193.e7. https://doi.org/10.1016/j.celrep.2018.03.086 Cite Download
Sanchez-Vega, F., Mina, M., Armenia, J., Chatila, W. K., Luna, A., La, K. C., Dimitriadoy, S., Liu, D. L., Kantheti, H. S., Saghafinia, S., Chakravarty, D., Daian, F., Gao, Q., Bailey, M. H., Liang, W.-W., Foltz, S. M., Shmulevich, I., Ding, L., Heins, Z., … Schultz, N. (2018). Oncogenic Signaling Pathways in The Cancer Genome Atlas. Cell, 173(2), 321-337.e10. https://doi.org/10.1016/j.cell.2018.03.035 Cite
Gao, Q., Liang, W.-W., Foltz, S. M., Mutharasu, G., Jayasinghe, R. G., Cao, S., Liao, W.-W., Reynolds, S. M., Wyczalkowski, M. A., Yao, L., Yu, L., Sun, S. Q., Fusion Analysis Working Group, Cancer Genome Atlas Research Network, Chen, K., Lazar, A. J., Fields, R. C., Wendl, M. C., Van Tine, B. A., … Ding, L. (2018). Driver Fusions and Their Implications in the Development and Treatment of Human Cancers. Cell Reports, 23(1), 227-238.e3. https://doi.org/10.1016/j.celrep.2018.03.050 Cite
Knijnenburg, T. A., Wang, L., Zimmermann, M. T., Chambwe, N., Gao, G. F., Cherniack, A. D., Fan, H., Shen, H., Way, G. P., Greene, C. S., Liu, Y., Akbani, R., Feng, B., Donehower, L. A., Miller, C., Shen, Y., Karimi, M., Chen, H., Kim, P., … Wang, C. (2018). Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas. Cell Reports, 23(1), 239-254.e6. https://doi.org/10.1016/j.celrep.2018.03.076 Cite
Jayasinghe, R. G., Cao, S., Gao, Q., Wendl, M. C., Vo, N. S., Reynolds, S. M., Zhao, Y., Climente-González, H., Chai, S., Wang, F., Varghese, R., Huang, M., Liang, W.-W., Wyczalkowski, M. A., Sengupta, S., Li, Z., Payne, S. H., Fenyö, D., Miner, J. H., … Ding, L. (2018). Systematic Analysis of Splice-Site-Creating Mutations in Cancer. Cell Reports, 23(1), 270-281.e3. https://doi.org/10.1016/j.celrep.2018.03.052 Cite
Huang, K.-L., Mashl, R. J., Wu, Y., Ritter, D. I., Wang, J., Oh, C., Paczkowska, M., Reynolds, S., Wyczalkowski, M. A., Oak, N., Scott, A. D., Krassowski, M., Cherniack, A. D., Houlahan, K. E., Jayasinghe, R., Wang, L.-B., Zhou, D. C., Liu, D., Cao, S., … Ding, L. (2018). Pathogenic Germline Variants in 10,389 Adult Cancers. Cell, 173(2), 355-370.e14. https://doi.org/10.1016/j.cell.2018.03.039 Cite
Ding, L., Bailey, M. H., Porta-Pardo, E., Thorsson, V., Colaprico, A., Bertrand, D., Gibbs, D. L., Weerasinghe, A., Huang, K.-L., Tokheim, C., Cortés-Ciriano, I., Jayasinghe, R., Chen, F., Yu, L., Sun, S., Olsen, C., Kim, J., Taylor, A. M., Cherniack, A. D., … Cancer Genome Atlas Research Network. (2018). Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics. Cell, 173(2), 305-320.e10. https://doi.org/10.1016/j.cell.2018.03.033 Cite
Aguilar, B., Ghaffarizadeh, A., Johnson, C. D., Podgorski, G. J., Shmulevich, I., & Flann, N. S. (2018). Cell death as a trigger for morphogenesis. PloS One, 13(3), e0191089. https://doi.org/10.1371/journal.pone.0191089 Cite
Reynolds, S. M., Miller, M., Lee, P., Leinonen, K., Paquette, S. M., Rodebaugh, Z., Hahn, A., Gibbs, D. L., Slagel, J., Longabaugh, W. J., Dhankani, V., Reyes, M., Pihl, T., Backus, M., Bookman, M., Deflaux, N., Bingham, J., Pot, D., & Shmulevich, I. (2017). The ISB Cancer Genomics Cloud: A Flexible Cloud-Based Platform for Cancer Genomics Research. Cancer Research, 77(21), e7–e10. https://doi.org/10.1158/0008-5472.CAN-17-0617 Cite
Gibbs, D. L., & Shmulevich, I. (2017). Solving the influence maximization problem reveals regulatory organization of the yeast cell cycle. PLoS Computational Biology, 13(6), e1005591. https://doi.org/10.1371/journal.pcbi.1005591 Cite
Kytola, V., Topaloglu, U., Miller, L. D., Bitting, R. L., Goodman, M. M., D Agostino, R. B., Desnoyers, R. J., Albright, C., Yacoub, G., Qasem, S. A., DeYoung, B., Thorsson, V., Shmulevich, I., Yang, M., Shcherban, A., Pagni, M., Liu, L., Nykter, M., Chen, K., … Zhang, W. (2017). Mutational Landscapes of Smoking-Related Cancers in Caucasians and African Americans: Precision Oncology Perspectives at Wake Forest Baptist Comprehensive Cancer Center. Theranostics, 7(11), 2914–2923. https://doi.org/10.7150/thno.20355 Cite
Sun, Y., Ji, P., Chen, T., Zhou, X., Yang, D., Guo, Y., Liu, Y., Hu, L., Xia, D., Liu, Y., Multani, A. S., Shmulevich, I., Kucherlapati, R., Kopetz, S., Sood, A. K., Hamilton, S. R., Sun, B., & Zhang, W. (2017). MIIP haploinsufficiency induces chromosomal instability and promotes tumour progression in colorectal cancer. The Journal of Pathology, 241(1), 67–79. https://doi.org/10.1002/path.4823 Cite
Poole, W., Leinonen, K., Shmulevich, I., Knijnenburg, T. A., & Bernard, B. (2017). Multiscale mutation clustering algorithm identifies pan-cancer mutational clusters associated with pathway-level changes in gene expression. PLoS Computational Biology, 13(2), e1005347. https://doi.org/10.1371/journal.pcbi.1005347 Cite
Knijnenburg, T. A., Klau, G. W., Iorio, F., Garnett, M. J., McDermott, U., Shmulevich, I., & Wessels, L. F. A. (2016). Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy. Scientific Reports, 6, 36812. https://doi.org/10.1038/srep36812 Cite
Poole, W., Gibbs, D. L., Shmulevich, I., Bernard, B., & Knijnenburg, T. A. (2016). Combining dependent P-values with an empirical adaptation of Brown’s method. Bioinformatics (Oxford, England), 32(17), i430–i436. https://doi.org/10.1093/bioinformatics/btw438 Cite
Sun, Y., Ji, P., Chen, T., Zhou, X., Yang, D., Guo, Y., Liu, Y., Hu, L., Xia, D., Liu, Y., Multani, A. S., Shmulevich, I., Kucherlapati, R., Kopetz, S., Sood, A. K., Hamilton, S. R., Sun, B., & Zhang, W. (2016). MIIP haploinsufficiency induces chromosomal instability and promotes tumour progression in colorectal cancer. The Journal of Pathology. https://doi.org/10.1002/path.4823 Cite

W. Zhang and I. Shmulevich, Eds., Computational And Statistical Approaches To Genomics, Kluwer Academic Publishers, Boston, 2002;  2nd Edition, Springer, 2006.

W. Zhang, I. Shmulevich, J. Astola, Microarray Quality Control, Wiley and Sons, March 2004.

E. R. Dougherty, I. Shmulevich, J. Chen, Z. J. Wang, Eds. Genomic Signal Processing and Statistics, EURASIP Book Series on Signal Processing and Communications, Hindawi Publishing Corp., 2005.

I. Shmulevich and E. R. Dougherty, Genomic Signal Processing, Princeton University Press, 2007.

I. Shmulevich and E. R. Dougherty, Probabilistic Boolean Networks: The Modeling and Control of Gene Regulatory Networks, SIAM Press, 2009.

 

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