Origent Data Sciences and Cytokinetics Present Analyses Demonstrating Baseline Data Predict Measures of Vital Capacity in Patients With ALS

SOUTH SAN FRANCISCO, Calif., Dec. 07, 2016 (GLOBE NEWSWIRE) -- Origent Data Sciences, Inc. (Origent) and Cytokinetics, Inc. (Nasdaq:CYTK) today announced results from the first part of a research collaboration to refine and prospectively validate an Origent computer model to predict the course of ALS disease progression leveraging data from Cytokinetics’ clinical trials of tirasemtiv. The poster, presented by David L. Ennist, Ph.D., MBA, Chief Science Officer, Origent Data Sciences and Jinsy A. Andrews, M.D., Director of Neuromuscular Clinical Trials, Columbia University, at the 27th International Symposium on ALS/MND in Dublin, Ireland, showed that the Gradient Boosting Machine (GBM) algorithm was the optimal model to predict slow vital capacity (SVC) at times subsequent to baseline and that forced vital capacity (FVC) records could be used to predict slow vital capacity (SVC) scores of ALS patients using this machine learning technique.

The release as fetched from its publisher. cytokinetics.com ↗