M. R. Yousefi

Yousefi, M. R., Ph.D.

Research Assistant Professor
Department of Electrical and Computer Engineering
The Ohio State University
205 Dreese Laboratory
2015 Neil Avenue
Columbus, OH 43210
yousefi@ece.osu.edu
641-292-8732

Education
  • Ph.D., Texas A&M University, College Station, USA, 2013
  • M.S., University of Tehran, Tehran, Iran, 2008
  • B.S., Sahand University of Technology, Tabriz, Iran, 2004
Research Interests
Genomic Signal Processing
  • Intervention in genetic regulatory networks
  • Systems biology
  • Small-sample classification and error estimation
  • RNA-Seq and microarray gene expression models
System Identification and Control
  • Stochastic systems
  • Optimal control
  • System identification
  • Time series analysis and prediction

Journal Publications

  1. Yousefi, M. R. and Dougherty, E. R., A Comparison Study of Optimal and Suboptimal Intervention Policies for Gene Regulatory Networks in the Presence of Uncertainty, EURASIP Journal on Bioinformatics and Systems Biology, Vol. 2014, 6, 2014.
    [SpringerOpen] [PDF]
  2. Ghaffari, N., Yousefi, M. R., Johnson, C. D., Ivanov, I., and Dougherty, E. R., Modeling the Next Generation Sequencing Sample Processing Pipeline for the Purposes of Classification, BMC bioinformatics, Vol. 14, 307, 2013.
    [BioMed Central] [PDF]
  3. Yousefi, M. R., Datta, A., and Dougherty, E. R., Optimal Intervention in Markovian Gene Regulatory Networks with Random-Length Therapeutic Response to Antitumor Drug, IEEE Transactions on Biomedical Engineering, Vol. 60, No. 12, 3542-3552, 2013.
    [IEEE Xplore] [PDF]
  4. Yousefi, M. R. and Dougherty, E. R., Intervention in Gene Regulatory Networks with Maximal Phenotype Alteration, Bioinformatics, Vol. 29, No. 14, 1758-1767, 2013.
    [Bioinformatics] [PDF]
  5. Yousefi, M. R. and Dougherty, E. R., Performance Reproducibility Index for Classification, Bioinformatics, Vol. 28, No. 21, 2824-2833, 2012.
    [Bioinformatics] [PDF]
  6. Yousefi, M. R., Datta, A., and Dougherty, E. R., Optimal Intervention Strategies for Therapeutic Methods with Fixed-Length Duration of Drug Effectiveness, IEEE Transactions on Signal Processing, Vol. 60, No. 2, 4930-4944, 2012.
    [IEEE Xplore] [PDF]
  7. Yousefi, M. R., Hua, J., and Dougherty, E. R., Multiple-Rule Bias in the Comparison of Classification Rules, Bioinformatics, Vol. 27, No. 12, 1675-1683, 2011.
    [Bioinformatics] [PDF]
  8. Amiri, F., Yousefi, M. R., Lucas, C., Shakery, A., and Yazdani, N., Mutual Information-Based Feature Selection for Intrusion Detection Systems, Journal of Network and Computer Applications, Vol. 34, No. 4, 1184-1199, 2011.
    [Journal of Network and Computer Applications]
  9. Yousefi, M. R., Hua, J., Sima, C., and Dougherty, E. R., Reporting Bias When Using Real Data Sets to Analyze Classification Performance, Bioinformatics, Vol. 26, No. 1, 68-76, 2010.
    [Bioinformatics] [PDF]
  10. Yousefi, M. R., Kasmaei, B. S., Vahabie, A., Lucas, C., and Araabi, B. N., Input Selection Based on Information Theory for Constructing Predictor Models of Solar and Geomagnetic Activity Indices, Solar Physics, Vol. 258, No. 2, 297-318, 2009.
    [Solar Physics]

Conference Publications

  1. Yousefi, M. R. and Ivanov, I., Optimal Control of Gene Regulatory Networks with Uncertain Intervention Effects, IEEE Global Conference on Signal and Information Processing (GlobalSIP), Austin, TX, December 2013 (Invited).
  2. Yousefi, M. R., Compromised Intervention Policies for Phenotype Alteration, IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), Houston, TX, November 2013.
  3. Yousefi, M. R. and Dougherty, E. R., Bayesian Optimal Control of Markovian Genetic Regulatory Networks, 47th Asilomar Conference on Signals, Systems and Computers (Asilomar), Pacific Grove, CA, November 2013.
  4. Yousefi, M. R., Datta, A., and Dougherty, E. R., Optimal Therapeutic Methods with Random-Length Response in Probabilistic Boolean Networks, in Proc. of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), Washington, DC, December 2012.
    [IEEE Xplore]
  5. Yousefi, M. R., Datta, A., and Dougherty, E. R., Optimal Cancer Therapy Based on a Tumor Growth Inhibition Model, in Proc. of IEEE Statistical Signal Processing Workshop (SSP), Ann Arbor, MI, August 2012.
    [IEEE Xplore]
  6. Yousefi, M. R., Datta, A., and Dougherty, E. R., Optimal Intervention Strategies for Cyclic Therapeutic Methods with Fixed-Length Duration of Effect, in Proc. of the 45th Asilomar Conference on Signals, Systems and Computers (Asilomar), Pacific Grove, CA, November 2011.
    [IEEE Xplore]
  7. Yousefi, M. R., Datta, A., and Dougherty, E. R., Modeling Cyclic and Acyclic Therapeutic Methods with Persistent Intervention Effect in Probabilistic Boolean Networks, in Proc. of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), San Antonio, TX, December 2011.
    [IEEE Xplore]
  8. Yousefi, M. R., Hua, J., Sima, C., and Dougherty, E. R., Effects of Partial Reporting of Classification Results, in Proc. of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), Cold Spring Harbor, NY, November 2010.
    [IEEE Xplore]
  9. Kasmaei, B. S., Yousefi, M. R., and Lucas, C., Recurrence Analysis of Solar-Terrestrial System, in the International Astroparticle Physics Symposium: Forecasting of the Radiation and Geomagnetic Storms by Networks of Particle Detectors (FORGES-2008), Nor Amberd, Armenia.
    [FORGES2008]
  10. Yousefi, M. R., Mirmomeni, M., and Lucas, C., Input Variables Selection Using Mutual Information for Neuro Fuzzy Modeling with the Application to Time Series Forecasting, in Proc. of the International Joint Conference on Neural Networks (IJCNN), Orlando, Florida, USA, August, 2007: 1121-1126.
    [IEEE Xplore]
  11. Vahabie, A., Yousefi, M. R., Araabi, B. N., Lucas, C., and Barghinia, S., Combination of Singular Spectrum Analysis and Autoregressive Model for Short Term Load Forecasting, in Proc. of IEEE Power Tech 2007, Lausanne, Switzerland, July 2007: 137.
    [IEEE Xplore]
  12. Vahabie, A., Yousefi, M. R., Araabi, B. N., Lucas, C., Barghinia, S., and Ansarimehr, P., Mutual Information Based Input Selection in Neuro-Fuzzy Modeling for Short Term Load Forecasting of Iran National Power System, in Proc. of IEEE International Conference on Control and Automation (ICCA), Guangzhou, China, May-June 2007: 2710-2715.
    [IEEE Xplore]

Other Publications

  1. Yousefi, M. R., Optimal Intervention in Markovian Genetic Regulatory Networks for Cancer Therapy, Ph.D. Dissertation, Department of Electrical Engineering, Texas A&M University, May 2013.
  2. Yousefi, M. R., Input Variable Selection in System Identification - Application in Time-Series Prediction., M.S. Thesis, Department of Electrical Engineering, University of Tehran, February 2008.

Genomic Signal Processing (GSP) is the engineering discipline that studies the processing of genomic signals and their control mechansisms. It brings together the mathematical foundations of signal processing, probability theory, graph theory, stochastic and optimal control systems, digital circuits, statistics and pattern recognition to solve fundamental problems in systems biology and medicine, with special emphasis on genomic regulation. Hence, GSP encompasses various methodologies concerning expression profiles: detection, prediction, classification, control, and statistical and dynamical modeling of gene networks.

Genetic regulatory networks (GRNs) refer to a class of complex models describing the multivariate functional relationships among a cohort of genes (or their products). Genes are nodes in a GRN, and edges describe regulatory relationships between genes. These networks aim to model cellular control mechanisms such as cell-cycle, cell differentation and the process of development in an organism. They are also capable of modeling how abnormal cell functions result from one or several breakdowns in gene regulations. This is especially important in translational medicine, whose ultimate goal is to develop therapies based on the disruption or mitigation of aberrant gene function contributing to the pathology of a disease. Therapies usually involve some procedure and several drug candidates acting on various gene products.

Developing therapeutic methods in the context of GRNs involves designing intervention strategies to alter the dynamics of the gene activity profiles of the network in some desired manner, thereby identifying potential drug targets. Our research aims to provide a rigorous mathematical framework, supported by optimal experimental design, to first carefully study such complex regulatory systems and then to formulate optimal therapeutic methods that will benefit cancer patients directly and society as a whole.

Presentations

  1. Control of Gene Networks and Reproducibility in Bioinformatics, in Department of Biomedical Informatics (BMI), The Ohio State University, January 29, 2014.
  2. Compromised Intervention Policies for Phenotype Alteration, in IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), Houston, TX, November 17-19, 2013.
  3. Intervention in Gene Regulatory Networks with Maximal Phenotype Alteration, in 2013 Student Research Week - Texas A&M University, College Station, TX, March 26-28, 2013.
  4. Optimal Therapeutic Methods with Random-Length Response in Probabilistic Boolean Networks, in IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), Washington, DC, December 2-4, 2012.
  5. Modeling Cyclic and Acyclic Therapeutic Methods with Persistent Intervention Effect in Probabilistic Boolean Networks, in IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), San Antonio, TX, December 4-6, 2011.
  6. Quantifying the Reporting Bias of Classification Performance on Real Data Sets, in 8th Annual Conference of the MidSouth Computational Biology and Bioinformatics Society (MCBIOS 2011), College Station, TX, April 1-2, 2011.
  7. Effects of Partial Reporting of Classification Results, IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), Cold Spring Harbor, NY, November 10-12, 2010.
  8. Combination of Singular Spectrum Analysis and Autoregressive Model for Short Term Load Forecasting, in IEEE PowerTech 2007, EPFL, Lausanne, Switzerland, July 1-5, 2007.

Posters

  1. Optimal Control of Gene Regulatory Networks with Uncertain Intervention Effects, in 1st IEEE Global Conference on Signal and Information Processing (GlobalSIP), Austin, TX, December 3-5, 2013 (Invited).
  2. Bayesian Optimal Control of Markovian Genetic Regulatory Networks, in 47th Asilomar Conference on Signals, Systems and Computers (Asilomar), Pacific Grove, CA, November 3-6, 2013.
  3. Optimal Cancer Therapy Based on a Tumor Growth Inhibition Model, in IEEE Statistical Signal Processing Workshop (SSP), Ann Arbor, MI, August 5-8, 2012.
  4. Optimal Intervention Strategies for Cyclic Therapeutic Methods with Fixed-Length Duration of Effect, in 45th Asilomar Conference on Signals, Systems and Computers (Asilomar), Pacific Grove, CA, November 6-9, 2011.

I received my master's degree in electrical engineering from the University of Tehran, Iran, in 2008 and my Ph.D. in electrical engineering from Texas A&M University, College Station, Texas in 2013. My Ph.D. research focused on designing optimal control strategies, taking into account probabilistic variabilities in tumor response to external interventions, for Markovian gene regulatory networks. I joined the Department of Electrical and Computer Engineering at The Ohio State University in June 2013 as a Research Assistant Professor. My current research interests include genomic signal processing and studying optimal intervention problems in gene regulatory networks from a probabilistic perspective.

Prior to 2008, I was a member of the Control and Intelligent Processing Center of Excellence (CIPCE) in Iran. I worked on nonlinear system identification and time series analysis with the applications in solar and geomagnetic activity prediction. My time series prediction model was ranked 4th among more than 90 participants in NN3 international competition and was also used as a model for the analysis of several stock indices in the Tehran Securities Exchange Technology Management Co., Iran.

Last update: August 22, 2014