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Publications

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Publications

See our peer reviewed results

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Bayesian network analysis of panomic biological big data identifies the importance of triglyceride-rich LDL in atherosclerosis development

Szilard Voros, Aruna T. Bansal, Michael R. Barnes, Jagat Narula, Pal Maurovich-Horvat, Gustavo Vazquez, Idean B. Marvasty, Bradley O. Brown, Isaac D. Voros, William Harris, Viktor Voros, Thomas Dayspring, David Neff, Alex Greenfield, Leon Furchtgott, Bruce Church, Karl Runge, Iya Khalil, Boris Hayete, Diego Lucero, Alan T. Remaley and Roger S. Newton

ASH Poster | Causal AI in silico Patient Model Identifies Minichromosome Maintenance (MCM) Family Genes as Novel Predictors of Overall Survival in Multiple Myeloma

Daniel Vagie, Derek Walkama, Laurel Mayhew, Todd Oakland, and Bruce Church
 

CTAD Poster | Causal in silico patient models can inform Alzheimer’s disease patient identification and endpoint selection for early-stage clinical trials

So-Youn Shin, Shokeen Deepanshi, Apoorva Bharthur, Todd Oakland, and Jeanne Latourelle

 

ICPE22 | Predictors of Treatment Changes in Patients With Rheumatoid Arthritis Using Machine Learning

Whitney Krueger, Linyan Li, Yuhang Liu, Yi Pan, Michelle Brauer, Alexander Liede, Seoyoung C. Kim, Earl Steinberg

 
   

ASCO | PTCH1 and AXIN2 modulation of AR copy number effects on AR gene overexpression in metastatic castration resistant prostate cancer (mCRPC).

Raymond T. Yan, Jeanne Latourelle, Omar Khalid, Ravi Bharat Parikh, Bruce W. Church

Evaluating Triple Therapy Treatment Pathways in Chronic Obstructive Pulmonary Disease (COPD): A Machine-Learning Predictive Model

Michael Bogart, Yuhang Liu, Todd Oakland, Marjorie Stiegler

The case for AI-driven cancer clinical trials – The efficacy arm in silico

Likhitha Kolla, Fred K. Gruber, Omar Khalid, Colin Hill, Ravi B. Parikh

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.

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.

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.

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.

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.

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.

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.

Novel Predictive Models for Metabolic Syndrome Risk- A “Big Data” Analytic Approach

Steinberg GB, Church BW, McCall CJ, Scott AB, Kalis BP. Am J Manag Care. 2014. 20(6):e221-e228. PMID: 25180505

Learning Models for Metabolic Syndrome from Medical Claims Data

Church, B., & Steinberg, G. (2012, October). Presented at the Strata Rx Conference, San Franscisco, CA.

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

The potential of biologic network models in understanding the etiopathogenesis of ovarian cancer

Khalil I, Brewer MA, Neyarapally T, Runowicz CD. Gynecol Oncol. 2010. 116(2):282-5. PMID: 19931138

Cross-talk between signaling pathways can generate robust oscillations in calcium and camp

Siso-Nadal F, Fox JJ, Laporte SA, Hébert TE, Swain PS. PLoS One. 2009. 4(10):e7189. PMID: 19844582

Achieving confidence in mechanism for drug discovery and development

Pitluk Z, Khalil I. Drug Discov Today. 2007. 12(21-22):924-30. PMID: 17993410

A systems biology dynamical model of mammalian G1 cell cycle progression

Haberichter T, Mädge B, Christopher RA, Yoshioka N, Dhiman A, Miller R, Gendelman R, Aksenov SV, Khalil IG, Dowdy SF. Mol Syst Biol. 2007. 3:84. PMID: 17299420

An integrated approach for inference and mechanistic modeling for advancing drug development

Aksenov SV, Church B, Dhiman A, Georgieva A, Sarangapani R, Helmlinger G, Khalil IG. FEBS Lett. 2005. 579(8):1878-83. PMID: 15763567

Systems biology for cancer

Khalil IG, Hill C. Curr Opin Oncol. 2005. 17(1):44-8. PMID: 15608512

The statistical mechanics of complex signaling networks: nerve growth factor signaling

Brown KS, Hill CC, Calero GA, Myers CR, Lee KH, Sethna JP, Cerione RA. Phys Biol. 2004. 1(3-4):184-95. PMID: 16204838

Data-driven computer simulation of human cancer cell

Christopher R, Dhiman A, Fox J, Gendelman R, Haberitcher T, Kagle D, Spizz G, Khalil IG, Hill C. Ann N Y Acad Sci. 2004. 1020:132-53. PMID: 15208190