Bayesian Machine Learning on CALGB/SWOG 80405 (Alliance) and PEAK Data Identifies Heterogeneous Landscape of Clinical Predictors of Overall Survival (OS) in Different Populations of Metastatic Colorectal Cancer (mCRC)
July 2019
Rahul K Das, Fang-Shu Ou, Cecilia Washburn, Federico Innocenti, Andrew B. Nixon, Heinz-Josef Lenz, Charles Blanke, Donna Niedzwiecki, Iya Khalil, Brian D. Harms, Alan P. Venook
Machine-Learning Enabled Identification of Markers of Huntington’s Disease Progression
February 2019
Rahul K Das, Jing Tu, Jeanne Latourelle, Cecilia Washburn, Brian Harms, Iya Khalil, John H Warner, Edward J Wild, Cristina Sampaio, Amrita Mohan
Causal modeling of CALGB 80405 (Alliance) identifies network drivers of metastatic colorectal cancer (mCRC)
June 2018
Das, R.K., Furchtgott, L., Cunha, D., Fang-Shu, O., Innocenti, F., Heinz-Josef, L., Meyerhardt, J., Rich, K., Latourelle, J., Niedzwiecki, D., Nixon, A., O’Reilly, E.M., Wuest, D., Hayete, B., Khalil, I., Venook, A. (2018, June). Presented at the ASCO Annual Meeting, Chicago, Illinois.
Reconstruction and simulation of regulatory networks in the Htt allelic series using causal machine learning
February 2018
Latourelle J, Yan R, Beste M, Yang T, Hayete B, Khalil I, Aaronson J, Rosinski J. 13th Annual HD Therapeutics Conference. Palm Springs, CA. 2018.
Predictors of Disease Modifying Therapy Initiation in Patients with Multiple Sclerosis Using Electronic Health Records Data - A Machine Learning Perspective
May 2017
Icten Z, Hitchcock C, Davis S, Ciofani D, Sanky M, Hadzi T, Khalil I, Alas V. ISPOR Annual Meeting 2017.
Novel Predictive Modeling Identifies and Quantifies Factors That Predict the Risk of Hypoglycemia in Patients with Type 2 Diabetes (T2D)
March 2017
Thai N, Wei L, Anderson J, Alas V, Zhou S, Berhanu P, Sung J, Dalal M. AMCP Managed Care Specialty Pharmacy Annual Meeting 2017.
Using Clinical Trial and Real World Data to Bridge Efficacy to Effectiveness of Fingolimod in Multiple Sclerosis Patients
October 2017
Ivanov V, Torgovitsky R, Tchetgen E, Church B, Alas V, Khalil I, Risson V, Kahler K, Olson M, ISPOR. 2016. PND8.
Identification of Clinical and Genetic Predictors of Parkinson’s Disease Progression via Bayesian Machine Learning
September 2016
Latourelle J, Beste M, Hadzi T, Hayete B, Miller R, Oppenheim J, Valko M, Wuest D, Khalil I, Venuto C, World Parkinson’s Congress (WPC), 2016. 1312.
Treatment Patterns Among Schizophrenia Patients Receiving Paliperidone Palmitate or Atypical Oral Antipsychotics in Community Behavioral Health Organizations
September 2016
Jeffrey P. Anderson, Kruti Joshi, Zeynep Icten, Veronica Alas. 28th Annual US Psychiatric and Mental Health Congress. San Diego, CA. 2015.
HD causal modeling using network ensemble simulations of gene expression data
April 2014
Jong-Min Lee, Kevin Correia, Douglas D. Barker, James F. Gusella, Marcy E. MacDonald, Paul D. McDonagh, Jignesh R. Parikh, Iya G. Khalil, Keith Elliston, Seung Kwak. CHDI Foundation, Inc.‘s Annual HD Therapeutics Conference. Palm Springs, CA.
Learning Models for Metabolic Syndrome from Medical Claims Data
October 2012
Church, B., & Steinberg, G. (2012, October). Presented at the Strata Rx Conference, San Franscisco, CA.
Quantification and analysis of combination drug synergy in high-throughput transcriptome studies
June 2010
Gümüs, Z.H., Siso-Nadal, F., Gjrezi, A., McDonagh, P., Khalil, I., Giannakakou, P., Weinstein, H. (2010, June). Presented at the IEEE International Conference on Bioinformatics and Bioengineering, Philadelphia, PA. doi: 10.1109/BIBE.2010.46
Confirmation of peroxiredoxin II as a driver gene for doxorubicin sensitivity identified from drug-induced expression profiling of the NCI-60 cell lines using Reverse Engineering (REFS) network models
April 2012
Monks, A., Hose, C.D., Hayete, B., Runge, K., DeCaprio, D., Teicher, B.A., Khalil, I., McDonagh, P.D., Doroshow, J.H. (2012, April). Presented at the 103rd Annual Meeting of the American Association for Cancer Research. Chicago, IL. doi: 10.1158/1538-7445.AM2012-5663
Machine learning approach to personalized medicine in breast cancer patients: development of data-driven, personalized, causal modeling through identification and understanding of optimal treatments for predicting better disease outcomes
April 2018
Kaplan, G.H., Berry, A.B., Rinn, K.J., Ellis, E.D., Birchfield, G.R., Wahl, T.A., Liu, X., Tameishi, M., Beatty, J.D., Dawson, P.L., Mehta, V.K., Holman, A., Atwood, M.K., Alexander, S., Bonham, C., Summers, L., Khalil, I., Hayete, B., Wuest, D., Zheng, W., Liu, Y., Wang, X., Brown, T.D. (2018, April). Presented at the AACR Annual Meeting, Chicago, IL.
Multiple Myeloma Drivers of High Risk and Response to Stem Cell Transplantation Identified by Causal Machine Learning: Out-of-Cohort and Experimental Validation
December 2017
Furchtgott L, Bolomsky A, Gruber F, Samur M, Keats J, Yeesil J, Stangelberge K, Attal M, Moreau P, Avet-Loiseau H, Runge K, Wuest D, Rich K, Khalil I, Hayete B, Ludwig H, Munshi N, Auclair D. ASH Annual Meeting 2017. 3029.
Prediction of Hypoglycemia Risk Among Patients with Type 2 Diabetes (T2D) Using an Ensemble-Based, Hypothesis-Free Procedure
May 2017
Thai N, Wei LJ, Alas V, Khalil I, Berhanu P, Dalal MR, Sung J. ISPOR Annual Meeting 2017.
Bayesian Network Models of Multiple Myeloma: Drivers of High Risk and Durable Response
December 2016
Gruber F, Hayete B, Keats J, McBride K, Runge K, DeRome M, Lonial S, Khalil I, Auclair D, ASH Annual Meeting. 2016.
The Health Care Cost of Primary Headache and Associated Co-Morbidities
April 2016
Valko M, Alas V, Strickland I, Staats P, Errico J, AMCP. 2016. G27
Data-Driven Reconstruction and Simulation of Transcriptional Regulatory Networks in the Htt Allelic Series
April 2016
Beste, M., Yang, T., Latourelle, J., Hayete, B., Menalled, L., Brunner, D., Alexandrov, V., Kwak, S., Howland, D., Aaronson, J., Khalil, I., Rosinski, J. (2016, April). Presented at the CHDI Foundation, Inc.‘s 11th Annual HD Therapeutics Conference, Palm Springs, CA.
Predictors of Remission in Schizophrenia Patients Treated With Paliperidone Palmitate or Oral Antipsychotics in Community Behavioral Health Organizations
September 2015
Icten, Z., Joshi, K., Anderson, J., Alas, V. (2015, September). Presented at the 28th Annual US Psychiatric and Mental Health Congress, San Diego, CA.
A Mathematical Model of Long-Term Outcomes in Parkinson’s Disease
June 2013
Hayete, B., Laramie, J., Bienkowska, J., Eberly, S., Khalil, I., Lang, A., Marek, K., Oakes, D., Shoulson, I., Singleton, A., Song, T., Verma, A., Wien, M., Ravina, B. (2013, June). Presented at the 17th International Congress of Parkinson’s Disease and Movement Disorders, Sydney, Australia.
Data-driven computational modeling to identify biomarkers of response to lenvatinib (E7080) in melanoma
April 2013
Kadowaki, T., Funahashi, Y., Matsui, J., Pavan, K., Sachdev, P., O’Brien, J., Xing, H., McDonagh, P.D., Khalil, I., Kurzrock, R., Hong, D.S., Nemunaitis, J. (2013, April). Presented at the 104th Annual Meeting of the American Association for Cancer Research, Washington, DC.
Accurate Prediction of Clinical Disease Progression in Patients With Advanced Fibrosis Due to NASH using a Bayesian Machine Learning Approach
April 2018
Latourelle J, Tu J, Das R, Furchtgott L, Schoeberl B, Smiechowski B, Church B, Khalil I, Hayete B, Djedjos S, Nguyen T, Xiao Y, Aguilar R, Chen G, Subramnian, Myers R, Ratziu V, Nezam A, Bosch, Goodman Z, Harrison S, Sanyal A. The International Liver Congress™. Paris, France. 2018.
Statistical Modeling of CALGB 80405 (Alliance) to Identify Influential Factors in Metastatic Colorectal Cancer (CRC) Dependent on Primary (1o) Tumor Side
June 2017
Furchtogott L, Swanson D, Hayete B, Khalil, I, Wuest D, Rich K, Nixon AB, Niedzwiecki D, Meyerhardt JA, O’Reilly EM, Ou F, Heinz Josef L, Innocenti F, Venook AP. ASCO Annual Meeting 2017. 3528.
Machine Learning Methodology Predicts Comorbidities are Associated With Increased Total Healthcare Costs Among Patients With Severe Peripheral Artery Disease
April 2017
Berger JS, Haskell L, Ting W, Lurie F, Eapen Z, Valko M, Alas V, Rich K, Crivera C, Schein J. Quality of Care and Outcomes Research in Cardiovascular Disease and Stroke 2017 Scientific Sessions.
Machine Learning Methodology Identifies Predictors of a Cardiovascular Composite Measure Among Severe Peripheral Artery Disease Patients
November 2017
Ting W, Haskell L, Lurie F, Berger JS, Eapen Z, Valko M, Alas V, Rich K, Crivera C, Schein J, AHA Scientific Sessions 2016. 14448
Novel Predictive Modeling Identifies and Quantifies Factors That Predict the Risk of Hypoglycemia in Patients with Type 2 Diabetes (T2D)
April 2016
Thai N, Wei L, Anderson J, Alas V, Zhou S, Berhanu P, Sung J, Dalal M, AMCP. 2016. E26.
Investigation of Mechanisms of Response in Multiple Myeloma Via Bayesian Causal Inference: An Early Analysis of the CoMMpass Study Data
December 2015
Fred Gruber, Boris Hayete, Jonathan Keats, Kyle McBride, Karl Runge, Mary DeRome, Sagar Lonial, Iya Khalil, Daniel Auclair. American Society of Hematology (ASH) 57th Annual Meeting & Exposition. Orlando, FL. 2015.
Power of Reverse Engineering and Forward Simulation Platform for Driving Precision Medicine
November 2015
Khalil, I., & Wasserman, S. (2015, November). Presented at the Pharmaceutical R&D Information Systems Management Executive Forum, Plainsboro Township, NJ.
Identification of Determinants of Progression to Type 2 Diabetes Using Electronic Health Records and Big Data Analytics
June 2014
Anderson, J.P., Parikh, J.R., Shenfeld, D.K., Church, B.W., Laramie, J.M., Piper, B.A., Willke, R.J., Mardekian, J., Rublee, D.A. (2014, June). Presented at the ISPOR 19th International Meeting, Montreal, Canada.
Reverse-engineered, forward-simulation of MEK-dependent molecular networks reveal novel regulators of cell cycle and cancer cell survival
April 2012
Gendelman, R., Xing, H., Sarde, P., Mirzoeva, O.K., Feiler, H., Gray, J.W., McDonagh, P.D., Khalil, I., Korn, W.M. (2012, April). Presented at the 103rd Annual Meeting of the American Association for Cancer Research, Chicago, IL. http://dx.doi.org/10.1158/1538-7445.AM2012-986