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BOOKS:

ElsevierPredictive Modeling of Drug Sensitivity by Ranadip Pal

BookEmerging Research in the Analysis and Modeling of Gene Regulatory Networks edited by Ivan Ivanov, Xiaoning Qian and Ranadip Pal

JOURNAL PUBLICATIONS:

  1. R. Rahman, K. Matlock, S. Ghosh, R. Pal “Heterogeneity Aware Random Forest for Drug Sensitivity Prediction” Scientific Reports in press
  2. K. Matlock, C. De-Niz, R. Rahman, S. Ghosh, R. Pal “Investigation of Model Stacking for Drug Sensitivity Prediction” BMC Bioinformatics in press
  3. R. Rahman, J. Otridge, R. Pal, “IntegratedMRF: Random Forest based framework for integrating prediction from different data types”, Bioinformatics (2017) 33 (9): 1407-1410
  4. K. Matlock, N. Berlow, C. Keller, R. Pal, “Combination therapy design for maximizing sensitivity and minimizing toxicity” BMC Bioinformatics 2017 18(Suppl 4):116
  5. C. De-Niz, R. Rahman, R. Pal , “Algorithms for Drug Sensitivity Prediction”, Algorithms (2016) 9 (4), 77
  6. S. Haider, R. Rahman, S. Ghosh, R. PalA copula based approach for design of multivariate random forests for drug sensitivity prediction”, PLOS ONE 10(12), e0144490, 2015
  7. R. Rahman, S. Haider, S. Ghosh, R. Pal "Design of Probabilistic Random Forests with Applications to Anticancer Drug Sensitivity Prediction". Cancer Informatics. 2015;14(Suppl 5):57-73. doi:10.4137/CIN.S30794.
  8. C. S. Grasso, Y. Tang, N. Truffaux, N. Berlow, L. Liu, M. Debily, M. J. Quist, L. E. Davis, E. C. Huang, P. J. Woo, A. Ponnuswami, S. Chen, T. Johung, W. Sun, M. Kogiso, Y. Du, Q. Lin, Y. Huang, M. Hütt-Cabezas, K. E. Warren, L. Le Dret, P. S. Meltzer, H. Mao, M. Quezado, D. G. van Vuurden, J. Abraham, M. Fouladi, M. N. Svalina, N. Wang, C Hawkins, J. Nazarian, M. M. Alonso, E. Raabe, E. Hulleman, P. T. Spellman, X. Li, C. Keller*, R. Pal*, J. Grill*, M. Monje* “Functionally-defined Therapeutic Targets in Diffuse Intrinsic Pontine Glioma”, Nature Medicine, 2015, doi:10.1038/nm.3855 *These authors jointly directed this work.
  9. Halvorson KG, Barton KL, Schroeder K, Misuraca KL, Hoeman C, Chung A,  Crabtree DM, Cordero FJ, Singh R, Spasojevic I, Berlow N, Pal R, Becher OJ, A High-Throughput In Vitro Drug Screen in a Genetically Engineered Mouse Model of Diffuse Intrinsic Pontine Glioma Identifies BMS-754807 as a Promising Therapeutic Agent.” PLoS ONE 10(3): e0118926, 2015. doi:10.1371/journal.pone.0118926
  10. R Navabi, S Abedi, SH Hosseinian, R Pal “On the fast convergence modeling and accurate calculation of PV output energy for operation and planning studies” Energy Conversion and Management 89, 497-506, 2015
  11. S. Hettmer, Z. Li, A. N Billin, F. G Barr, DDW Cornelison, A R Ehrlich, D C Guttridge, A Hayes-Jordan, L J Helman, P J Houghton, J Khan, D M Langenau, C M Linardic, R Pal, T A Partridge, G K Pavlath, R Rota, B W Schäfer, J Shipley, B Stillman, L H Wexler, A J Wagers, C Keller “Rhabdomyosarcoma: Current Challenges and Their Implications for Developing TherapiesCold Spring Harbor perspectives in medicine, Vol 4, Issue 11, Pg a025650, 2014
  12. N. Berlow, L. Davis, C. Keller, R. Pal, “Inference of dynamic biological networks based on responses to drug perturbationsEURASIP Journal on Bioinformatics and Systems Biology, 2014, 2014:14  doi:10.1186/s13637-014-0014-1
  13. Q. Wan, R. Pal “An ensemble based top performing approach for NCI-DREAM drug sensitivity prediction challenge”, PLoS ONE 9(6): e101183, 2014 doi:10.1371/journal.pone.0101183
  14. J. C. Costello et al. “A community effort to assess and improve drug sensitivity prediction algorithms.” Nature Biotechnology, doi:10.1038/nbt.2877 , 2014
  15. N. Berlow, S. Haider, Q. Wan, M. Geltzeiler, L. E. Davis, C. Keller, R. PalAn integrated approach to anti-cancer drugs sensitivity prediction”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2014 10.1109/TCBB.2014.2321138
  16. M. U. Caglar, R. PalA Diverse Stochastic Search Algorithm for Combination Therapeutics”, BioMed Research International, 2014, doi 10.1155/2014/873436
  17. R. PalModeling and inference of genetic interactions”, WIREs Data Mining Knowl Discov 2013. doi: 10.1002/widm.1103
  18. N. Berlow, L. Davis, E. Cantor, B. Seguin, C. Keller, R. Pal, A new approach for prediction of tumor sensitivity to targeted drugs based on functional data”, BMC Bioinformatics , 2013, 14:239 doi:10.1186/1471-2105-14-239 Highly accessed
  19. M. U. Caglar, R. Pal, “Stochastic Model Simulation Using Kronecker Product Analysis and Zassenhaus Formula Approximation”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, in press.
  20. S. Haider, R. Pal, “Integrated Analysis of Transcriptomic and Proteomic Data”, Current Genomics, Vol 14, Issue 2,  pages 91-110, 2013
  21. S. Haider, R. Pal, "Boolean network inference from time series data incorporating prior biological knowledge", BMC Genomics,2012, 13(Suppl 6):S9   <link to Supplementary Information>
  22. N. Berlow, R. Pal, “Generation of Stationary control policies with best expected performance for a family of Markov Chains”, Journal of Biological Systems, Vol. 20, No. 4,pages  423-440, 2012.
  23. J. Abraham, Y. X. Chua, J. M. Glover, J. W. Tyner, M. M. Loriaux, A. Kilcoyne, F. J. Giles, L. D.  Nelon, J. S. Carew, Y. Ouyang, J. E. Michalek, R. Pal, B. J. Druker, B. P. Rubin, C. Keller,  An adaptive Src–PDGFRA–Raf axis in rhabdomyosarcoma”, Biochemical and Biophysical Research Communications, Vol 426 (3), Pages 363-368, 2012
  24. R. Pal, S. Bhattacharya “Transient dynamics of reduced order models of genetic regulatory networksIEEE Transactions on Computational Biology and Bioinformatics, Vol 9, Issue 4, Pg. 1230-1244,2012
  25. R. Pal, S. Bhattacharya, M. U. Caglar, “Robust Approaches for Genetic Regulatory Network Modeling and Intervention”, IEEE Signal Processing Magazine, Vol. 29, No. 1, Pg. 66-76, 2012
  26. B. P. Rubin, K. Nishijo, H. H. Chen, X. Yi, D. P. Schuetze, R. Pal, S. I. Prajapati, J. Abraham, B. R. Arenkiel, QR Chen, S. Davis, A. T. McCleish, M. R. Capecchi, J. E. Michalek, L. A. Zarzabal, J. Khan, Z. Yu, D. M. Parham, F. G. Barr, P. S. Meltzer, Y. Chen, C. Keller "Evidence for an Unanticipated Relationship Between Undifferentiated Pleomorphic Sarcoma and Embryonal Rhabdomyosarcoma" Cancer Cell, Vol. 19 (2), Pg. 177-91, 2011
  27. R. Pal , S. Bhattacharya “Characterizing the effect of coarse-scale PBN modeling on dynamics and intervention performance of genetic regulatory networks represented by Stochastic Master Equation models”, IEEE Transactions on Signal Processing, Vol. 58, Pg. 3341 - 3351, No.6, 2010.
  28. R. PalContext-Sensitive Probabilistic Boolean Networks: Steady State Properties, Reduction and Steady State Approximation”, IEEE Transactions on Signal Processing, Vol. 58, Pg. 879-890, No.2, 2010   Supplementary Information
  29. R. Pal , A. Datta and E. Dougherty "Bayesian Robustness in the Control of Gene Regulatory Networks", IEEE Transactions on Signal Processing , Vol 57, Pg. 3667-3678, 2009    Supplementary Information
  30. R. Layek, A. Datta, R. Pal, E. R. Dougherty, “Adaptive Intervention in Probabilistic Boolean Networks”, Bioinformatics, Vol 25, Pg. 2042-2048, 2009.
  31. E. R. Dougherty, R. Pal, X. Qian, A. Datta “ Stationary and Structural Control in Gene Regulatory Networks: Basic Concepts”, International Journal of Systems Science, Vol. 41, No. 1, 5-16, 2010
  32. R. Pal , A. Datta and E. Dougherty " Robust Intervention in Probabilistic Boolean Networks ", IEEE Transactions on Signal Processing , Vol 56, No. 3, Pg. 1280-94, 2008.
  33. Y. Qian, J. Venkatraj, R. Barhoumi, R. Pal, A. Datta, J. R. Wild and E. Tiffany-Castiglioni "Comparative Non-cholinergic Neurotoxic Effects of Paraoxon and Diisopropyl Fluorophosphate (DFP) on Human Neuroblastoma and Astrocytoma Cell Lines", Toxicology and Applied Pharmacology, Vol. 219, No.2-3, Pg. 162-171, 2007.
  34. A. Datta, R. Pal, A. Choudhary and E. Dougherty " Control Approaches for Probabilistic Gene Regulatory Networks ", IEEE Signal Processing Magazine, Vol. 24, No. 1, 54-63, 2007.
  35. I. Ivanov, R. Pal and E. Dougherty, " Size Reducing Mappings between Probabilistic Boolean Networks " , IEEE Transactions on Signal Processing , Vol. 55, no 5, 2310-2322, 2007.
  36. R. Pal , A. Datta and E. Dougherty, “ Optimal Infinite Horizon Control for Probabilistic Boolean Networks” , IEEE Transactions on Signal Processing, Vol. 54, no. 6 : 2375-2387, 2006.
  37. A. Datta, R. Pal and E. Dougherty, “ Intervention in Probabilistic Gene Regulatory Networks ”, Current Bioinformatics, Vol. 1, No. 2: 167-184, 2006.
  38. R. Pal , I. Ivanov, A. Datta and E. Dougherty. “ Generating Boolean Networks with a Prescribed Attractor StructureBioinformatics, 2005, 21: 4021-4025.
  39. R. Pal , A. Datta, A. J. Fornace, M. L. Bittner and E. Dougherty. “Boolean Relationships among Genes Responsive to Ionizing Radiation in the NCI 60 ACDS”Bioinformatics, 2005, 21: 1542–1549.
  40. R. Pal , A. Datta, M. L. Bittner and E. Dougherty. “ Intervention in Context Sensitive Probabilistic Boolean Networks ”, Bioinformatics, 2005, 21: 1211-1218
  41. X. Zhou , X. Wang, R. Pal , I. Ivanov, M. Bittner and E. Dougherty , “ A Bayesian Connectivity-based Approach to Constructing Probabilistic Gene Regulatory Networks ”, Bioinformatics, 2004 20: 2918-2927

PATENTS

  • J McGlone, B Nutter, S Mitra, R Pal - US Patent 9,084,411, 2015: Livestock identification system and method.   (Patent Licensed by Animal Biotech http://www.animal-biotech.com )
  • R. Pal, C. Keller, B. Seguin, N. Berlow, US Patent Application 14661918: Target Inhibition Map System for Combination Therapy Design and Methods of Using Same, 2016 (Patent Licensed by First Ascent Biomedical)

Editorial/Foreword

Consortium Publication

Conference Publications:

  1. K. Matlock, N. Berlow, C. Keller, R. Pal, "Combination therapy design for maximizing sensitivity and minimizing toxicity", accepted in CNB-MAC 2016
  2. H. Xie, R. Pal, S. Mitra, "A Descriptive Model of Resting-State Networks Using Markov Chains" 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016)
  3. J. E. Hill, K. Matlock, R. Pal, B. Nutter, S. Mitra, "Automated segmentation of MS lesions in FLAIR, DIR and T2-w MR images via an information theoretic approach". Proc. SPIE 9784, Medical Imaging 2016
  4. R. Rahman and R. Pal, "Analyzing drug sensitivity prediction based on dose response curve characteristics," 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Las Vegas, NV, 2016, pp. 140-143.
    doi: 10.1109/BHI.2016.7455854
  5. S. Haider and R. Pal. “Analysis of multivariate drug sensitivity dependence structure using copulas”. 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Pg. 1352-1355, 2014
  6. Q. Wan and R. Pal. “Multi-objective optimization of ensemble of regression trees using genetic algorithms”. 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Pg. 1356-1359, 2014
  7. Y. Li, R. Pal and Y. Li “Non-Contact Multi-Radar Smart Probing of Body Orientation Based on Micro-Doppler Signatures”Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE ,  Pg. 598-601, 2014
  8. Q. Wan, Y. Li, C. Li and R. Pal “Gesture Recognition for Smart Home Applications Using Portable Radar Sensors”, Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE , Pg. 6414-6417, 2014
  9. Q. Wan and R. Pal. “A multivariate random forest based framework for drug sensitivity prediction”.IEEE International Workshop on Genomic Signal Processing and Statistics(GENSIPS), 2013,doi  10.1109/GENSIPS.2013.6735929
  10. N. Berlow, S. Haider, R. Pal, and C. Keller. “Quantifying the inference power of a drug screen for
    predictive analysis” IEEE International Workshop on Genomic Signal Processing and Statistics(GENSIPS), pgs: 49-52, 2013. doi 10.1109/GENSIPS.2013.6735928
  11. S. Haider and R. Pal “Inference of tumor inhibition pathways from drug perturbation data” accepted IEEE Global Conference on Signal and Image Processing (GlobalSIP), pgs: 95-98, 2013 doi 10.1109/GlobalSIP.2013.6736823
  12. S. Haider, N. Berlow, R. Pal, L. Davis and C. Keller “Combination Therapy Design for Targeted Therapeutics from a Drug-Protein Interaction Perspective” IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS, pages 58-61, 2012 10.1109/GENSIPS.2012.6507726
  13. R. Pal and N. Berlow and S. Haider “Anticancer Drug Sensitivity Analysis: An integrated approach applied to Erlotinib sensitivity prediction in the CCLE Database” IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS, pages 9-12, 2012 10.1109/GENSIPS.2012.6507714
  14. M. U. Caglar, R. Pal, "Complexity reduction of Stochastic Master Equation Simulation based on Kronecker Product Analysis", BCB’12 Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine, pages 186-193, 2012 , doi 10.1145/2382936.2382960
  15. N. Berlow, R. Pal, L Davis, C. Keller "Analyzing Pathway Design From Drug Perturbation Experiments", IEEE Statistical Signal Processing (SSP) workshop, 2012, pages 552-55, doi 10.1109/SSP.2012.6319757
  16. M. U. Caglar, R. Pal “Simulation of stochastic models with Kronecker product analysis & Zassenhaus approximation” Biophysical Society 56th annual Meeting, San Diego, California; February 25-29, 2012
  17. R. Pal, N. Berlow, “A Kinase inhibition map approach for tumor sensitivity prediction and combination therapy design for targeted drugs”, Pacific Symposium on Biocomputing (PSB) 2012 PMID 22174290      Download Software
  18. N. Berlow, R. Pal, “A novel approach for tumor sensitivity prediction and combination therapy design for targeted drugs” IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS, 2011
  19. S. Haider, R. Pal, “Inference of a Genetic Regulatory Network model from limited time series data”  IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS, 2011
  20. S. Bhattacharya, R. Pal, “A novel critical time analysis approach for Genetic Regulatory Networks” IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS, 2011
  21.  M. U. Caglar, R. Pal, “Relationships between genetic regulatory network models’, IEEE Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, California, Nov, 2011
  22. N. Berlow, R. Pal, “Generation Of Intervention Strategy For A Genetic Regulatory Network Represented By A Family Of Markov Chains” 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2011. PMID  2225610
  23. R. Pal, D. Hoover, "Analyzing the effects of coarse-scale modeling of genetic regulatory networks", accepted in International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2011
  24. M. U. Caglar, R. Pal, “Comparison of Control Approaches in Genetic Regulatory Networks by Using Stochastic Master Equation Models, Probabilistic Boolean Network Models and Differential Equation Models and Estimated Error Analyzes”, American Physical Society, APS March Meeting 2011, March 21-25, 2011, abstract #X40.014
  25. R. Pal, M. U. Caglar, "Approximating the average behavior of stochastic master equation models", IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS, 2010
  26. R. Pal , S. Bhattacharya “Effect of coarse-scale modeling on control outcome of genetic regulatory networksAmerican Control Conference, 2010, Pg. 5942 - 5947.
  27. K. Akrofi, R. Pal, M. Baker, B. Nutter, R. Schiffer “ Classification of Alzheimer’s Disease and Mild Cognitive Impairment by Pattern Recognition of EEG Power and Coherence " ICASSP, 2010.,      Supplementary Info
  28. Y. Yang, R. Pal, M. O’Boyle “ Classification of cognitive states using functional MRI data”, SPIE symposium on Medical Imaging, 2010
  29. R. Pal, S. Bhattacharya “ Steady-State Preserving Reduction for Genetic Regulatory Network Models”, IEEE International Symposium of Computer-Based Medical Systems (IEEE CBMS 2009)
  30. R. Pal “ Analyzing Steady State Probability Distributions of Context-Sensitive Probabilistic Boolean Networks”, IEEE International Workshop on Genomic Signal Processing and Statistics , GENSIPS, 2009 supplementary info
  31. R. Pal , A. Datta, E. R. Dougherty “ Quantification of data extraction noise in Probabilistic Boolean Network Modeling”, IEEE International Workshop on Genomic Signal Processing and Statistics , GENSIPS, 2009  supplementary info
  32. R. Layek, A. Datta, R. Pal, E. Dougherty “Adaptive Intervention in Probabilistic Boolean Networks”, Proceedings of the American Control Conference, Pg. 5647-5652, 2009.
  33. S. Mitra, M. O’Boyle, F. Afrin, B. Nutter, M. Baker, R. Pal , B. Ghosh, ”Generating Structure function Correlation by ICA- based Mapping of Activation Patterns on Co-registered fMRI and FADTI”, IEEE Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, California, October 26-29, 2008.
  34. R. Pal , A. Datta and E. Dougherty, "Comparison of Robust Strategies for the control of gene regulatory networks", IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS, Phoenix, June 8-10, 2008.
  35. R. Pal , H. Lahdesmaki, I. Shmulevich, O. Yli-Harja and E. Dougherty, "On the constraint of gene regulatory networks to canalizing functions and post classes", IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS, Phoenix, June 8-10, 2008.
  36. A. Datta, R. Pal  and E. Dougherty, “Control Approaches for Probabilistic Gene Regulatory Networks,” Proceedings of the 14 th Yale Workshop on Adaptive and Learning Systems, pages 7-13, June 2-4, 2008
  37. R. Pal , A. Datta and E. Dougherty "Robust Intervention in Probabilistic Boolean Networks", Proceedings of the Asilomar Conference on Signals, Systems and Computers, November 2007.
  38. R. Pal , A. Datta and E. Dougherty " Bayesian Robustness in the control of Gene Regulatory Networks ", Proceedings of the IEEE Statistical Signal Processing Workshop, 31-35, Madison, Wisconsin, August 2007.
  39. R. Pal , A. Datta and E. Dougherty " Robustness of Intervention strategies for Probabilistic Boolean Networks ", IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS, Tuusula, Finland, June 2007.
  40. R. Pal , A. Datta and E. Dougherty " Robust Intervention in Probabilistic Boolean Networks ", Proceedings of the American Control Conference, 2405-2410, New York, NY, July 2007.
  41. I. Ivanov, R. Pal, and E. Dougherty , "Applying Reduction Mappings in Designing Genomic Regulatory Networks", IEEE/NLM International Workshop on Life Science Systems and Applications, 2006
  42. R. Pal , I. Ivanov, A. Datta, M. L. Bittner and E. Dougherty " Synthesizing Boolean Networks with a Given Attractor Structure ",IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS, College Station, Texas, May 2006
  43. R. Pal , A. Datta and E. Dougherty, " Optimal Infinite Horizon Control for Probabilistic Boolean Networks ", Proceedings of the American Control Conference, 668-673, Minneapolis, MN, June 2006.
  44. R. Pal , A. Datta and E. Dougherty, " Altering Steady-State Probabilities in Probabilistic Boolean Networks ", IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS, College Station, Texas, May 2006.
  45. I. Ivanov, R. Pal and E. Dougherty, "Dynamics-Preserving Size Reduction Mappings for Probabilistic Boolean Networks", IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS, College Station, Texas, May 2006.
  46. R. Pal , A. Datta, Michael Bittner, E. Dougherty "External Control in a Special Class of Probabilistic Boolean Networks", Proceedings of the American Control Conference, 411-416, Portland, OR, June 2005 .
  47. R. Pal , A. Datta, M. L. Bittner and E. Dougherty, " External Control in Probabilistic Boolean Networks ", IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS, Newport, RI, May 2005.