NetraMark Holdings Inc. announced the publication of new peer-reviewed research that adds to the growing body of evidence supporting the power of its NetraAI solution to provide unique insights into disease biology and identify well-defined patient subpopulations that drive clinical trial success. The data, which appears in the current issue of Frontiers in Computational Neuroscience, identified several genes that shed light into ALS pathophysiology and represents new avenues for treatment. The analysis also identified subpopulations of ALS patients based on disease onset.

In this study, NetraAI, a unique machine learning (ML) environment, was used to analyze data collected by Answer ALS, the large collaborative effort in ALS, bringing together multiple research organizations and key opinion leaders. Over 800 ALS patients and 100 healthy controls from eight neuromuscular clinics distributed across the United States were enrolled in this project. NetraAI was made available to medical experts at the Gladstone Institute, allowing them to interact with the ML-generated hypotheses and to evaluate the findings and examine the causal factors that the NetraAI model suggested.

This approach bridges a critical gap that exists between advanced ML techniques and human medical expertise.nexplainable subsets are collections of patients that can lead to suboptimal overfit models and inaccurate insights due to poor correlations with the variables involved. The NetraAI uses the explainable subsets to derive insights and hypotheses (including factors that influence treatment and placebo responses, as well as adverse events) that can significantly increase the chances of a clinical trial success. AI methods lack these focus mechanisms and assign every patient to a class, even when this leads to "overfitting" which drowns out critical information that could have been used to improve a trial's chance of success.

Key findings from the study include: NetraAI replicated ALS drug targets identified using other analytic methods, but also identified several genes belonging to the same gene family as those previously reported as well as wholly novel targets. These findings identify specific genetic factors with the potential to accurately define novel subtypes of bulbar and limb-initiated ALS for improved personalized medicine approaches. Identification of these subpopulations has significant potential to improve clinical trial outcomes by matching therapeutic interventions to patient disease mechanisms.