MDS Poster | Gemini Digital Twins Identified Neuro-Common and Disease-Specific Drivers of Blood NfL Change Rate
X.Shen, S.Sathe, L.Sun, P.Ashrap, K.Johnson, S.Sukhram, S.Reddy, S-Y.Shin, J.Latourelle, C.Sampaio
AAIC Poster | Alzheimer’s Disease Mechanistic Pathways were Discovered through in silico Experiments in Causal AI based Digital Twins
Apoorva Bharthur Sanjay, Deepanshi Shokeen, Xinyu Shen, So-Youn Shin and Jeanne Latourelle
HD Therapeutics Conference Poster | Gemini Digital Twins Identified Both Common and Disease-specific Drivers of Cognitive Progression in Huntington’s and Alzheimer’s Diseases
L.Sun, S.Sathe, X.Shen, P.Ashrap, K.Johnson, S.Sukhram, S.Reddy, S-Y.Shin, J.Latourelle, C.Sampaio
AACR Poster | Infer Cancer Cell Gene Dependency in Multiple Myeloma Using Causal AI in-silico Patient Model
Brandon Nathasingh, Derek Walkama, Laurel Mayhew, Kendall Loh, Jeanne Latourelle, Bruce W Church, Yaoyu E Wang
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.
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,…
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.
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
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