In Memoriam: Ilya Shmulevich

at the Institute for Systems Biology

In Memoriam: Ilya Shmulevich

Professor

thorsson-shmulevich.isbscience.org/bio/ilya-shmulevich/

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.

  • 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

PhD, Electrical and Computer Engineering, Purdue University, 1997

My Google Scholar page

[1]
G. Qin, Y. Zhang, J. W. Tyner, C. J. Kemp, and I. Shmulevich, “Knowledge graphs facilitate prediction of drug response for acute myeloid leukemia,” iScience, vol. 27, no. 9, 2024, doi: 10.1016/j.isci.2024.110755. Cite Download
[1]
G. Qin et al., “Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia,” Clin Cancer Res, vol. 30, no. 12, pp. 2659–2671, 2024, doi: 10.1158/1078-0432.CCR-23-1674. Cite Download
[1]
R. Laubenbacher, B. Mehrad, I. Shmulevich, and N. Trayanova, “Digital twins in medicine,” Nat Comput Sci, vol. 4, no. 3, pp. 184–191, 2024, doi: 10.1038/s43588-024-00607-6. Cite Download
[1]
R. Laubenbacher et al., “Forum on immune digital twins: a meeting report,” NPJ Syst Biol Appl, vol. 10, no. 1, p. 19, 2024, doi: 10.1038/s41540-024-00345-5. Cite Download
[1]
R. Laubenbacher et al., “Toward mechanistic medical digital twins: some use cases in immunology,” Front Digit Health, vol. 6, p. 1349595, 2024, doi: 10.3389/fdgth.2024.1349595. Cite Download
[1]
Y. Zhang et al., “A framework towards digital twins for type 2 diabetes,” Front Digit Health, vol. 6, p. 1336050, 2024, doi: 10.3389/fdgth.2024.1336050. Cite Download
[1]
V. Nikolić, M. Echlin, B. Aguilar, and I. Shmulevich, “Computational capabilities of a multicellular reservoir computing system,” PLoS One, vol. 18, no. 4, p. e0282122, 2023, doi: 10.1371/journal.pone.0282122. Cite Download
[1]
M. Echlin, B. Aguilar, and I. Shmulevich, “Characterizing the Impact of Communication on Cellular and Collective Behavior Using a Three-Dimensional Multiscale Cellular Model,” Entropy (Basel), vol. 25, no. 2, p. 319, 2023, doi: 10.3390/e25020319. Cite Download
[1]
E. A. Stahlberg et al., “Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation,” Front Digit Health, vol. 4, p. 1007784, 2022, doi: 10.3389/fdgth.2022.1007784. Cite Download
[1]
B. Tercan et al., “SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery,” F1000Res, vol. 11, p. 493, 2022, doi: 10.12688/f1000research.110903.2. Cite Download
[1]
V. Thorsson et al., “927 Streamlining cancer immunotherapy research with the CRI iAtlas data resource and web portal,” J Immunother Cancer, vol. 10, no. Suppl 2, 2022, doi: 10.1136/jitc-2022-SITC2022.0927. Cite Download
[1]
V. Nikolić, M. Echlin, B. Aguilar, and I. Shmulevich, “Multicellular Reservoir Computing,” 2022, bioRxiv. doi: 10.1101/2022.03.26.485905. Cite Download
[1]
A. Silverstein et al., “Evolution of biomarker research in autoimmunity conditions for health professionals and clinical practice,” Elsevier, 2022, pp. 219–276. Accessed: Jan. 18, 2023. [Online]. Available: http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-194436 Cite
[1]
B. Tercan, B. Aguilar, S. Huang, E. R. Dougherty, and I. Shmulevich, “Probabilistic boolean networks predict transcription factor targets to induce transdifferentiation,” iScience, vol. 25, no. 9, 2022, doi: 10.1016/j.isci.2022.104951. Cite Download
[1]
R. Moser et al., “Synthetic lethal kinases in Ras/p53 mutant squamous cell carcinoma,” Oncogene, 2022, doi: 10.1038/s41388-022-02330-w. Cite
[1]
G. Qin et al., “A functional module states framework reveals transcriptional states for drug and target prediction,” Cell Rep, vol. 38, no. 3, p. 110269, 2022, doi: 10.1016/j.celrep.2021.110269. Cite Download
[1]
T. Hernandez-Boussard et al., “Digital twins for predictive oncology will be a paradigm shift for precision cancer care,” Nat Med, 2021, doi: 10.1038/s41591-021-01558-5. Cite
[1]
A. Fedorov et al., “NCI Imaging Data Commons,” Cancer Res, vol. 81, no. 16, pp. 4188–4193, 2021, doi: 10.1158/0008-5472.CAN-21-0950. Cite Download
[1]
D. L. Gibbs, B. Aguilar, V. Thorsson, A. V. Ratushny, and I. Shmulevich, “Patient-Specific Cell Communication Networks Associate With Disease Progression in Cancer,” Front Genet, vol. 12, p. 667382, 2021, doi: 10.3389/fgene.2021.667382. Cite Download
[1]
Y.-H. Lo et al., “A CRISPR/Cas9-engineered ARID1A-deficient human gastric cancer organoid model reveals essential and non-essential modes of oncogenic transformation,” Cancer Discov, 2021, doi: 10.1158/2159-8290.CD-20-1109. Cite Download
[1]
J. A. Eddy et al., “CRI iAtlas: an interactive portal for immuno-oncology research,” F1000Res, vol. 9, p. 1028, 2020, doi: 10.12688/f1000research.25141.1. Cite Download
[1]
J. B. Xavier et al., “The Cancer Microbiome: Distinguishing Direct and Indirect Effects Requires a Systemic View,” Trends Cancer, vol. 6, no. 3, pp. 192–204, 2020, doi: 10.1016/j.trecan.2020.01.004. Cite Download
[1]
E. J. R. Peterson et al., “Intricate Genetic Programs Controlling Dormancy in Mycobacterium tuberculosis,” Cell Rep, vol. 31, no. 4, p. 107577, 2020, doi: 10.1016/j.celrep.2020.107577. Cite Download
[1]
B. Aguilar et al., “A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma,” Gigascience, vol. 9, no. 7, 2020, doi: 10.1093/gigascience/giaa075. Cite Download
[1]
S. Cao et al., “Discovery of driver non-coding splice-site-creating mutations in cancer,” Nat Commun, vol. 11, no. 1, p. 5573, 2020, doi: 10.1038/s41467-020-19307-6. Cite Download
[1]
S. A. Danziger et al., “ADAPTS: Automated deconvolution augmentation of profiles for tissue specific cells,” PLoS ONE, vol. 14, no. 11, p. e0224693, 2019, doi: 10.1371/journal.pone.0224693. Cite Download
[1]
T. A. Knijnenburg et al., “Genomic and molecular characterization of preterm birth,” Proc. Natl. Acad. Sci. U.S.A., vol. 116, no. 12, pp. 5819–5827, 2019, doi: 10.1073/pnas.1716314116. Cite Download
[1]
V. Thorsson et al., “The Immune Landscape of Cancer,” Immunity, vol. 48, no. 4, pp. 812-830.e14, 2018, doi: 10.1016/j.immuni.2018.03.023. Cite Download
[1]
J. I. J. Orozco et al., “Epigenetic profiling for the molecular classification of metastatic brain tumors,” Nat Commun, vol. 9, no. 1, p. 4627, 2018, doi: 10.1038/s41467-018-06715-y. Cite Download
[1]
C. Kang, B. Aguilar, and I. Shmulevich, “Emergence of diversity in homogeneous coupled Boolean networks,” Phys Rev E, vol. 97, no. 5–1, p. 052415, 2018, doi: 10.1103/PhysRevE.97.052415. Cite
[1]
J. Saltz et al., “Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images,” Cell Reports, vol. 23, no. 1, pp. 181-193.e7, 2018, doi: 10.1016/j.celrep.2018.03.086. Cite Download
[1]
F. Sanchez-Vega et al., “Oncogenic Signaling Pathways in The Cancer Genome Atlas,” Cell, vol. 173, no. 2, pp. 321-337.e10, 2018, doi: 10.1016/j.cell.2018.03.035. Cite
[1]
Q. Gao et al., “Driver Fusions and Their Implications in the Development and Treatment of Human Cancers,” Cell Rep, vol. 23, no. 1, pp. 227-238.e3, 2018, doi: 10.1016/j.celrep.2018.03.050. Cite
[1]
T. A. Knijnenburg et al., “Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas,” Cell Rep, vol. 23, no. 1, pp. 239-254.e6, 2018, doi: 10.1016/j.celrep.2018.03.076. Cite
[1]
R. G. Jayasinghe et al., “Systematic Analysis of Splice-Site-Creating Mutations in Cancer,” Cell Rep, vol. 23, no. 1, pp. 270-281.e3, 2018, doi: 10.1016/j.celrep.2018.03.052. Cite
[1]
K.-L. Huang et al., “Pathogenic Germline Variants in 10,389 Adult Cancers,” Cell, vol. 173, no. 2, pp. 355-370.e14, 2018, doi: 10.1016/j.cell.2018.03.039. Cite
[1]
L. Ding et al., “Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics,” Cell, vol. 173, no. 2, pp. 305-320.e10, 2018, doi: 10.1016/j.cell.2018.03.033. Cite
[1]
B. Aguilar, A. Ghaffarizadeh, C. D. Johnson, G. J. Podgorski, I. Shmulevich, and N. S. Flann, “Cell death as a trigger for morphogenesis,” PLoS ONE, vol. 13, no. 3, p. e0191089, 2018, doi: 10.1371/journal.pone.0191089. Cite
[1]
S. M. Reynolds et al., “The ISB Cancer Genomics Cloud: A Flexible Cloud-Based Platform for Cancer Genomics Research,” Cancer Res., vol. 77, no. 21, pp. e7–e10, 2017, doi: 10.1158/0008-5472.CAN-17-0617. Cite
[1]
D. L. Gibbs and I. Shmulevich, “Solving the influence maximization problem reveals regulatory organization of the yeast cell cycle,” PLoS Comput. Biol., vol. 13, no. 6, p. e1005591, 2017, doi: 10.1371/journal.pcbi.1005591. Cite
[1]
V. Kytola et al., “Mutational Landscapes of Smoking-Related Cancers in Caucasians and African Americans: Precision Oncology Perspectives at Wake Forest Baptist Comprehensive Cancer Center,” Theranostics, vol. 7, no. 11, pp. 2914–2923, 2017, doi: 10.7150/thno.20355. Cite
[1]
Y. Sun et al., “MIIP haploinsufficiency induces chromosomal instability and promotes tumour progression in colorectal cancer,” J. Pathol., vol. 241, no. 1, pp. 67–79, 2017, doi: 10.1002/path.4823. Cite
[1]
W. Poole, K. Leinonen, I. Shmulevich, T. A. Knijnenburg, and B. Bernard, “Multiscale mutation clustering algorithm identifies pan-cancer mutational clusters associated with pathway-level changes in gene expression,” PLoS Comput. Biol., vol. 13, no. 2, p. e1005347, 2017, doi: 10.1371/journal.pcbi.1005347. Cite
[1]
T. A. Knijnenburg et al., “Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy,” Sci Rep, vol. 6, p. 36812, 2016, doi: 10.1038/srep36812. Cite
[1]
W. Poole, D. L. Gibbs, I. Shmulevich, B. Bernard, and T. A. Knijnenburg, “Combining dependent P-values with an empirical adaptation of Brown’s method,” Bioinformatics, vol. 32, no. 17, pp. i430–i436, 2016, doi: 10.1093/bioinformatics/btw438. Cite
[1]
Y. Sun et al., “MIIP haploinsufficiency induces chromosomal instability and promotes tumour progression in colorectal cancer,” J. Pathol., 2016, doi: 10.1002/path.4823. Cite
[1]
V. Dhankani et al., “Using Incomplete Trios to Boost Confidence in Family Based Association Studies,” Front Genet, vol. 7, p. 34, 2016, doi: 10.3389/fgene.2016.00034. Cite
[1]
H.-T. Yang, J.-H. Ju, Y.-T. Wong, I. Shmulevich, and J.-H. Chiang, “Literature-based discovery of new candidates for drug repurposing,” Brief. Bioinformatics, 2016, doi: 10.1093/bib/bbw030. Cite
[1]
Y. Liu et al., “Association of Somatic Mutations of ADAMTS Genes With Chemotherapy Sensitivity and Survival in High-Grade Serous Ovarian Carcinoma,” JAMA Oncol, vol. 1, pp. 486–94, Jul. 2015, doi: 10.1001/jamaoncol.2015.1432. Cite
[1]
G. Liu et al., “Augmentation of Response to Chemotherapy by microRNA-506 Through Regulation of RAD51 in Serous Ovarian Cancers.,” J Natl Cancer Inst, vol. 107, Jul. 2015, doi: 10.1093/jnci/djv108. 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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