See our peer reviewed results
Diabetes Therapy: Application of Machine Learning Models to Evaluate Hypoglycemia Risk in Type 2 Diabetes
Nature Leukemia: Multiple Myeloma DREAM Challenge reveals epigenetic regulator PHF19 as marker of aggressive disease
Mike J. Mason ● Carolina Schinke ● Christine L. P. Eng ● Fadi Towfic ● Fred Gruber ● Andrew Dervan ● Brian S. White ● Aditya Pratapa ● Yuanfang Guan8 ● Hongjie Chen ● Yi Cui10 ● Bailiang Li ● Thomas Yu ● Elias Chaibub Neto ● Konstantinos Mavrommatis ● Maria Ortiz ● Valeriy Lyzogubov ● Kamlesh Bisht ● Hongyue Y. Dai ● Frank Schmitz ● Erin Flynt ● Dan Rozelle ● Samuel A. Danziger ● Alexander Ratushny6 ● Multiple Myeloma DREAM Consortium ● William S. Dalton ● Hartmut Goldschmidt17,18 ● Herve Avet-Loiseau19 ● Mehmet Samur20,21 ● Boris Hayete5 ● Pieter Sonneveld22 ● Kenneth H. Shain23,24 ● Nikhil Munshi ● Daniel Auclair ● Dirk Hose ● Gareth Morgan ● Matthew Trotter ● Douglas Bassett ● Jonathan Goke ● Brian A. Walker ● Anjan Thakurta ● Justin Guinney
ASCO-SITC: Causal Modeling of TCGA NSCLC and HNSCC Data Identifies Network Drivers of Tumor Immune Subtypes
Rahul K Das, Raymond T Yan, Aishwarya Krishnakumar, Bruce Church, Iya Khalil, Xinmeng J Mu, Mateusz Maciejewski, Craig Davis
Evaluation of machine learning methodology for the prediction of healthcare resource utilization and healthcare costs in patients with critical limb ischemia—is preventive and personalized approach on the horizon?
Jeffrey S. Berger, Lloyd Haskell, Windsor Ting, Fedor Lurie, Shun-Chiao Chang, Luke A. Mueller, Kenneth Elder, Kelly Rich, Concetta Crivera, Jeffrey R. Schein, Veronica Alas
ML and precision public health: Saving mothers and babies from dying in rural India
Kasey Morris, Vincent S. Huang, Mokshada Jain, B.M. Ramesh, Hannah Kemp, James Blanchard, Shajy Isac, Bidyut Sarkar, Vikas Gothalwal, Vasanthakumar Namasivayam, Sema Sgaier. Presented at AI for Social Good workshop at NeurIPS (2019).
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)
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
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)
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.
Accurate Prediction of Clinical Disease Progression in Patients With Advanced Fibrosis Due to NASH using a Bayesian Machine Learning Approach
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.
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
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.
Systems biology and in vitro validation identifies family with sequence similarity 129 member A (FAM129A) as an asthma steroid response modulator
McGeachie MJ, Clemmer GL, Hayete B, Xing H, Runge K, Wu AC, Jiang X, Lu Q, Church B, Khalil I, Tantisira K, Weiss S. The Journal of allergy and clinical immunology. February 27, 2018. DOI: 10.1016/j.jaci.2017.11.059
Reconstruction and simulation of regulatory networks in the Htt allelic series using causal machine learning
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.
Multiple Myeloma Drivers of High Risk and Response to Stem Cell Transplantation Identified by Causal Machine Learning: Out-of-Cohort and Experimental Validation
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.
Machine Learning Methodology Identifies Predictors of a Cardiovascular Composite Measure Among Severe Peripheral Artery Disease Patients
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
Using Clinical Trial and Real World Data to Bridge Efficacy to Effectiveness of Fingolimod in Multiple Sclerosis Patients
Ivanov V, Torgovitsky R, Tchetgen E, Church B, Alas V, Khalil I, Risson V, Kahler K, Olson M, ISPOR. 2016. PND8.
Large-scale identification of clinical and genetic predictors of motor progression in patients with newly diagnosed Parkinson’s disease: a longitudinal cohort study and validation
Latourelle J, Beste M, Hadzi T, Miller R, Oppenheim J, Valko M, Wuest D, Church B, Khalil I, Hayete, B, Venuto C. Lancet Neurology Online. September 25, 2017. DOI http://dx.doi.org/10.1016/S1474-4422(17)30331-9
A Bayesian mathematical model of motor and cognitive outcomes in Parkinson’s disease
Hayete B, Wuest D, Laramie J, McDonagh P, Church B, Eberly S, et al. (2017) PLoS ONE 12(6): e0178982.
Statistical Modeling of CALGB 80405 (Alliance) to Identify Influential Factors in Metastatic Colorectal Cancer (CRC) Dependent on Primary (1o) Tumor Side
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.
Prediction of Hypoglycemia Risk Among Patients with Type 2 Diabetes (T2D) Using an Ensemble-Based, Hypothesis-Free Procedure
Thai N, Wei LJ, Alas V, Khalil I, Berhanu P, Dalal MR, Sung J. ISPOR Annual Meeting 2017.
Predictors of Disease Modifying Therapy Initiation in Patients with Multiple Sclerosis Using Electronic Health Records Data – A Machine Learning Perspective
Icten Z, Hitchcock C, Davis S, Ciofani D, Sanky M, Hadzi T, Khalil I, Alas V. ISPOR Annual Meeting 2017.
Machine Learning Methodology Predicts Comorbidities are Associated With Increased Total Healthcare Costs Among Patients With Severe Peripheral Artery Disease
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.
Novel Predictive Modeling Identifies and Quantifies Factors That Predict the Risk of Hypoglycemia in Patients with Type 2 Diabetes (T2D)
Thai N, Wei L, Anderson J, Alas V, Zhou S, Berhanu P, Sung J, Dalal M. AMCP Managed Care Specialty Pharmacy Annual Meeting 2017.
Bayesian network inference modeling identifies TRIB1 as a novel regulator of cell cycle progression and survival in cancer cells
Gendelman R, Xing H, Mirzoeva OK, Sarde P, Curtis C, Feiler H, McDonagh P, Gray JW, Khalil I, Korn WM. Cancer Research. Cancer Res January 13 2017 DOI: 10.1158/0008-5472.CAN-16-0512.
Bayesian Network Models of Multiple Myeloma: Drivers of High Risk and Durable Response
Gruber F, Hayete B, Keats J, McBride K, Runge K, DeRome M, Lonial S, Khalil I, Auclair D, ASH Annual Meeting. 2016.
Inferring Gene Networks for Strains of Dehalococcoides Highlights Conserved Relationships between Genes Encoding Core Catabolic and Cell-Wall Structural Proteins
Mansfeldt CB, Heavner GW, Rowe AR, Hayete B, Church BW, Richardson RE (2016) PLoS ONE 11(11): e0166234. doi:10.1371/journal.pone.0166234.
Treatment Patterns Among Schizophrenia Patients Receiving Paliperidone Palmitate or Atypical Oral Antipsychotics in Community Behavioral Health Organizations
Jeffrey P. Anderson, Kruti Joshi, Zeynep Icten, Veronica Alas. 28th Annual US Psychiatric and Mental Health Congress. San Diego, CA. 2015.
Identification of Clinical and Genetic Predictors of Parkinson’s Disease Progression via Bayesian Machine Learning
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.
Data-Driven Reconstruction and Simulation of Transcriptional Regulatory Networks in the Htt Allelic Series
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.
Novel Predictive Modeling Identifies and Quantifies Factors That Predict the Risk of Hypoglycemia in Patients with Type 2 Diabetes (T2D)
Thai N, Wei L, Anderson J, Alas V, Zhou S, Berhanu P, Sung J, Dalal M, AMCP. 2016. E26.
The Health Care Cost of Primary Headache and Associated Co-Morbidities
Valko M, Alas V, Strickland I, Staats P, Errico J, AMCP. 2016. G27
Clinical and Economic Burden of Commercially Insured Patients with Acromegaly in the United States: A Retrospective Analysis
Hilary Placzek, PhD, MPH; Yaping Xu, MD, MPH; Yunming Mu, PhD; Susan M. Begelman, MD; and Maxine Fisher, PhD, J Managed Care Spec Pharm. 2015;21(12):1106-14
Reverse Engineering and Evaluation of Prediction Models for Progression to Type 2 Diabetes: An Application of Machine Learning Using Electronic Health Records
Anderson JP, Parikh JR, Shenfeld DK, Ivanov V, Marks C, Church BW, Laramie JM, Mardekian J, Piper BA, Willke RJ, Rublee DA. Journal of Diabetes Science and Technology. 2016. 10(1):6-18. PMID: 26685993
Investigation of Mechanisms of Response in Multiple Myeloma Via Bayesian Causal Inference: An Early Analysis of the CoMMpass Study Data
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
Khalil, I., & Wasserman, S. (2015, November). Presented at the Pharmaceutical R&D Information Systems Management Executive Forum, Plainsboro Township, NJ.
Predictors of Remission in Schizophrenia Patients Treated With Paliperidone Palmitate or Oral Antipsychotics in Community Behavioral Health Organizations
Icten, Z., Joshi, K., Anderson, J., Alas, V. (2015, September). Presented at the 28th Annual US Psychiatric and Mental Health Congress, San Diego, CA.
Identification of Determinants of Progression to Type 2 Diabetes Using Electronic Health Records and Big Data Analytics
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.
HD causal modeling using network ensemble simulations of gene expression data
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.
A Mathematical Model of Long-Term Outcomes in Parkinson’s Disease
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
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.
Learning Models for Metabolic Syndrome from Medical Claims Data
Church, B., & Steinberg, G. (2012, October). Presented at the Strata Rx Conference, San Franscisco, CA.
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
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
Reverse-engineered, forward-simulation of MEK-dependent molecular networks reveal novel regulators of cell cycle and cancer cell survival
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
Causal modeling using network ensemble simulations of genetic and gene expression data predicts genes involved in rheumatoid arthritis
Xing H, McDonagh PD, Bienkowska J, Cashorali T, Runge K, Miller RE, Decaprio D, Church B, Roubenoff R, Khalil IG, Carulli J. PLoS Comput Biol. 2011. 7(3):e1001105. PMID: 21423713
Quantification and analysis of combination drug synergy in high-throughput transcriptome studies
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