Origent Publications
Combination of ciprofloxacin/celecoxib as a novel therapeutic strategy for ALS, Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration
Salomon-Zimri S, Pushett A, Russek-Blum N, Van Eijk RPA, Birman N, Abramovich B, Eitan E, Elgrart K, Beaulieu D, Ennist DL, Berry JD, Paganoni S, Shefner JM, Drory VE
Detectable Effect Cluster Analysis: A Novel Machine-Learning Subgroup Analysis Method for Drug Rescue
The poster will be presented at the MDA Clinical & Scientific Conference on March 13-16 2022.
Use of Machine Learning Predictions as Covariates to Optimize Clinical Trials of Neurologic Diseases
The poster was presented at the MDA Clinical & Scientific Conference on March 13-16 2022.
Development and Validation of a Machine-Learning ALS Survival Model Lacking Vital Capacity (VC-Free) for use in Clinical Trials during the COVID-19 Pandemic
Danielle Beaulieu,James D. Berry,Sabrina Paganoni,Jonathan D. Glass,Christina Fournier,Jonavelle Cuerdo,Mark Schactman & David L. Ennist.
Published in Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, on August 10, 2021.
Evidence for Generalizability of Edaravone Efficacy using a Novel Machine Learning Risk-based Subgroup Analysis Tool
Benjamin Rix Brooks, Erik P. Pioro, Danielle Beaulieu, Albert A. Taylor, Mark Schactman, Mike Keymer, Wendy Agnese, Johnna Perdrizet, Stephen Apple & David L. Ennist
Published in Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, on July 10, 2021.
Book: Neurotherapeutics in the Era of Translational Medicine
The Origent Team has authored a chapter in the new book Neurotherapeutics in the Era of Translational Medicine.
Poster: A Machine Learning ALS Survival Model Lacking Vital Capacity for use in Clinical Trials during the COVID-19 Pandemic
Presented at the 31st International Symposium on ALS/MND, December 9, 2020.
Poster: Detectable Effect Cluster Analysis: A Novel Machine-Learning Subgroup Analysis Method for Drug Rescue
Presented at the 31st International Symposium on ALS/MND, December 9, 2020.
Poster: Detectable Effect Cluster Analysis: A Novel Machine-Learning Based Clinical Trial Subgroup Analysis Tool
Presented at the 30th International Symposium on ALS/MND in Perth, Australia on December 4, 2019.
Poster: Estimate of an Acthar® Gel Treatment Effect in ALS Patients using Virtual Controls
Presented at the 30th International Symposium on ALS/MND in Perth, Australia on December 4, 2019.
Poster: Detectable Effect Cluster Analysis: A Novel Machine Learning Based Clinical Trial Subgroup Analysis Tool
Presented at the 18th Annual NEALS Meeting in Clearwater, Florida, October 3rd, 2019.
Design and Analysis of a Clinical Trial Using Previous Trials as Historical Control
David A. Schoenfeld, Dianne M. Finkelstein, Eric Macklin, Neta Zach, David L. Ennist, Albert A. Taylor, Nazem Atassi, The Pooled Resource Open-Access ALS Clinical Trials Consortium.
Published in Clinical Trials., on July 1, 2019.
Poster: Rapid Deployment of a Machine Learning-based Derived Biomarker using Publicly Available Data Sources for Covariate Adjusted Descriptive Modeling
Presented at the 2019 ASA Symposium on Data Science and Statistics in Bellevue, Washington on May 31, 2019.
Poster: Increasing ALS Clinical Trial Efficiency using Machine Learning Models
Presented at the 2019 MDA Clinical and Scientific Conference in Orlando, Florida on April 15, 2019. Re-presented at the 71st Annual Meeting of the American Academy of Neurology in Philadelphia, Pennsylvania on May 7, 2019, and at the ENCALS Meeting 2019 in Tours, France on May 16, 2019.
Poster: Validation of a Suite of Machine Learning Models using the Longitudinal VITALITY-ALS Data Set
Presented at the 29th International Symposium on ALS/MND in Glasgow, Scotland on December 8, 2018.
Pilot trial of inosine to elevate urate levels in amyotrophic lateral sclerosis
Katharine Nicholson, James Chan, Eric A. Macklin, Mark Levine‐Weinberg, Christopher Breen, Rachit Bakshi, Daniela L. Grasso, Anne‐Marie Wills, Samad Jahandideh, Albert A. Taylor, Danielle Beaulieu, David L. Ennist, Ovidiu Andronesi, Eva‐Maria Ratai, Michael A. Schwarzschild, Merit Cudkowicz, Sabrina Paganoni
Published online in Annals of Clinical and Translational Neurology on October 22, 2018
Poster: Machine Learning Applications for Increasing the Efficiency of ALS Clinical Trials
Presented at the 17th Annual NEALS Meeting in Clearwater, Florida, October 3rd, 2018.
Poster: Increasing Study Power using a Machine Learning Approach
Presented at the Joint Statistical Meeting (JSM) of the American Statistical Association (ASA) on July 30, 2018.
Poster: Machine Learning Models for the Assessment of Potential ALS Biomarkers
Presented at the 2018 Meeting of the European Network to Cure ALS (ENCALS), on June 21, 2018.
Improved Stratification of ALS Clinical Trials Using Predicted Survival
James D. Berry, Albert A. Taylor, Danielle Beaulieu, Lisa Meng, Amy Bian, Jinsy Andrews, Mike Keymer, David L. Ennist, Bernard Ravina
Published online in Annals of Clinical and Translational Neurology on March 8, 2018
Longitudinal Modeling to Predict Vital Capacity in Amyotrophic Lateral Sclerosis
Samad Jahandideh, Albert A. Taylor, Danielle Beaulieu, Mike Keymer, Lisa Meng, Amy Bian, Nazem Atassi, Jinsy Andrews & David L. Ennist
Published in Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, on December 20, 2017.
Poster: Machine Learning Models for the Assessment of Potential ALS Biomarkers
Presented at the 28th International Symposium on ALS/MND in Boston, Massachusetts, on December 9, 2017
Poster: Validation of Predictive ALS Machine Learning Models with a Contemporary, External Dataset and Application to Trial Simulations
Presented at the 16th Annual NEALS Meeting in Clearwater, Florida, October 4, 2017, and also at the 28th International Symposium on ALS/MND in Boston, Massachusetts, on December 9, 2017
Poster 162: Machine Learning Models for the Clinical Development of Gene and Cell Therapies
Presented at the 20th ASGCT Annual Meeting in Washington, DC, May 10-13, 2017. Objectives: We hypothesized that computer models incorporating both survival and disease progression predictions could serve as tools to develop virtual controls and to stratify patients...
Poster: The Proper Use of Historical Controls in ALS Trials
Presented at the 27th International Symposium on Amyotrophic Lateral Sclerosis and Motor Neurone Disease, Dublin Ireland, December 2016. Objectives: We asked, for what types of ALS human clinical trials can concurrent controls, historical controls, and virtual...
Poster: ALS Resistance is Regional and Not Explained by Demographics, Medications or Labs
Presented at the 27th International Symposium on Amyotrophic Lateral Sclerosis and Motor Neurone Disease, Dublin Ireland, December 2016. Objectives: To develop a meaningful operational definition of “ALS resistance” which captures patients with unexpectedly long...
Poster: Predicting Disease Progression for ALS Clinic Patients
Presented at the 27th International Symposium on Amyotrophic Lateral Sclerosis and Motor Neurone Disease, Dublin Ireland, December 2016.
Poster: In silico Stratification of ALS Patients using Machine Learning Algorithms
Presented at the 27th International Symposium on Amyotrophic Lateral Sclerosis and Motor Neurone Disease, Dublin Ireland, December 2016. Objectives: We hypothesized that computer models incorporating predictions for both survival and disease progression as measured...
Poster: Machine Learning Model for the Prediction of Slow Vital Capacity
Presented at the 27th International Symposium on Amyotrophic Lateral Sclerosis and Motor Neurone Disease, Dublin Ireland, December 2016. Objectives: To develop a model that predicts SVC using the PRO-ACT database. Conclusions: We hypothesized the possibility of...
Predicting Disease Progression in Amyotrophic Lateral Sclerosis.
Taylor AA, Fournier C, Polak M, Wang L, Zach N, Keymer M, Glass JD, Ennist DL.
Published online in Annals of Clinical and Translational Neurology on September 7, 2016
Poster: Analysis of Function and Survival in ALS Patients with Diaphragm Pacing using Virtual Controls
Taylor A, Miller R, Onders R and Ennist D. Analysis of function and survival in ALS patients with diaphragm pacing using virtual controls [v1; not peer reviewed]. F1000Research 2016, 5:120 (poster)
Being PRO-ACTive: What can a Clinical Trial Database Reveal About ALS?
Neta Zach, David L. Ennist, Albert A. Taylor, Hagit Alon, Alexander Sherman, Robert Kueffner, Jason Walker, Ervin Sinani, Igor Katsovskiy, Merit Cudkowicz, Melanie L. Leitner
Published online in Neurotherapeutics on January 23, 2015
Crowdsourced Analysis of Clinical Trial Data to Predict Amyotrophic Lateral Sclerosis Progression
Co-authors of this publication include Dr. Liuxia Wang and Guang Li of Origent. Published online in Nature Biotechnology on November 2, 2014.
Sentrana Presents Prize Winning Research at RECOMB Conference
Guang “Eric” Li describes the algorithm that was declared a winner of the DREAM Phil Bowen ALS Prediction Prize Challenge.