Healthcare Research Posters

We love to present our discoveries at conferences. Check out our most recent poster presentations.

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