- NetraAI’s biological and clinical signatures successfully boosted the accuracy of all eight evaluated algorithms across every single dataset tested
- The study showed improved performance across conventional machine learning, neural-network and foundation-model (including Fable 5) approaches in schizophrenia, major depressive disorder and pancreatic cancer
- Findings support what the company believes is the potential of an unbiased discovery-first approach to uncover clinically relevant structure in complex trial data and inform future patient-stratification and trial-enrichment strategies
TORONTO, Sept. 10, 2026 (GLOBE NEWSWIRE) — NetraMark Holdings Inc. (the “Company” or “NetraMark”) (TSX: AIAI) (OTCQB: AINMF) (Frankfurt: PF0), a clinical intelligence company transforming clinical trials with AI-powered precision analytics in the pharmaceutical industry, today announced the publication of new research in MDPI’s peer-reviewed AI journal.
The study, “Interpretable Subgroup Discovery with Abstention in Small, Heterogeneous Clinical Trials: A Retrospective Multi-Dataset Study,” evaluated NetraMark’s proprietary NetraAI technology across clinical datasets in schizophrenia, major depressive disorder and pancreatic cancer. The publication is available at: https://www.mdpi.com/2673-2688/7/9/349
The study benchmarked NetraAI against eight industry-standard predictive models, ranging from traditional biostatistical methods and deep neural networks to major AI foundation models. When provided with the compact variables discovered by NetraAI, every model saw an immediate, significant increase in predictive power across all three clinical datasets tested. The findings provide what the Company believes is important evidence of NetraAI’s fundamental differentiation from conventional machine learning, neural networks and newer foundation-model approaches.
Across all three datasets, researchers tested eight different AI and machine-learning methods using the original clinical data. Performance was generally weak to modest.
When those same AI methods were instead provided with the variables discovered by NetraAI, every method improved on every dataset tested.
“This study shows what makes NetraAI different,” said Dr. Joseph Geraci, Ph.D., founder, Chief Scientific and Technical Officer of NetraMark. “NetraAI is not another prediction model. It is designed to find clinically meaningful patient subpopulations that other analytical methods can miss. In this study, every AI and machine learning method tested performed better after being given the variables discovered by NetraAI. That is the role we believe NetraAI can play in clinical development: helping companies find the patients, variables and signals that are otherwise hidden in complex trial data.”
A Different Kind of AI for Clinical Trials
Most AI systems are built to learn from enormous datasets. Clinical trials present a very different challenge: relatively few patients, but often thousands of clinical, genomic and biological measurements.
NetraAI was built for this problem. Rather than trying to predict an answer for every patient, NetraAI searches for small, explainable combinations of variables that identify meaningful patient subpopulations. When the data do not support a reliable assignment, NetraAI can make a “No Call” instead of forcing a prediction.
To test whether general-purpose AI models could natively discover these same patient subpopulations without NetraAI’s specialized approach, the study also evaluated a pretrained foundation model. On the original clinical trial data, its performance was near chance 0.53 to 0.59 Area Under Curve (AUC). When it was given the variables discovered by NetraAI, its performance improved (achieving a near perfect AUC in the pancreatic cancer analysis) for a specific explainable subpopulation discovered by NetraAI.
“We do not see foundation models as competing with NetraAI,” Geraci continued. “The study suggests that NetraAI can do something complementary by discovering the clinical structure first. Other AI systems can then make better use of the data. For pharmaceutical companies, that could mean a clearer path to characterize the patients most likely to drive a successful clinical trial. The power of explainability makes all of this possible. ”
One Platform Across Multiple Diseases
In this paper, the same NetraAI architecture was evaluated across schizophrenia, major depressive disorder and pancreatic cancer, using datasets ranging from clinical measurements to high-dimensional genomic information.
In the pancreatic cancer analysis, for example, NetraAI reduced a search space containing approximately 25,000 genomic variables to a compact three-variable signature associated with observed regimen-associated response. While none of the eight other industry-standard AI and machine learning models tested could independently discover this compact signature on their own, they were all able to leverage NetraAI’s variables to achieve near-perfect predictive accuracy within the called patient subgroup. The finding remains exploratory and requires external validation.
NetraMark believes the ability to identify such compact, explainable patient subgroups could ultimately help pharmaceutical sponsors better understand drug response, comparator or placebo response and patient heterogeneity, and use those insights to inform future trial design and patient-enrichment strategies.
“Pharmaceutical companies spend enormous amounts of capital and years generating clinical trial data,” said George Achilleos, Chief Executive Officer of NetraMark “Our commercial thesis is straightforward: valuable information may remain hidden inside those datasets after conventional analysis is complete. NetraAI is designed to find it. If those discoveries can help sponsors make better decisions about the next clinical trial, we believe that represents a substantial opportunity for NetraMark.”
For more information on NetraMark and NetraAI, visit www.netramark.com.
About NetraAI
NetraAI is engineered to include focus mechanisms that separate small datasets into explainable and unexplainable subsets. Unexplainable subsets are collections of patients that can lead to suboptimal overfit models and inaccurate insights due to poor correlations with the variables involved. NetraAI uses explainable subsets to derive insights and hypotheses (including factors that influence treatment and placebo responses and adverse events), potentially increasing the likelihood of a clinical trial’s success. Many other AI methods lack these focus mechanisms and assign every patient to a class, often leading to “overfitting,” which drowns out critical information that could have been used to improve a trial’s chance of success.
About NetraMark
NetraMark is focused on being a leader in the development of Generative Artificial Intelligence (Gen AI)/Machine Learning (ML) solutions that are targeted at the biotechnology and pharmaceutical industries. The Company’s product offering uses a novel topology-based algorithm that has the ability to parse patient data sets into subsets of people that are strongly related according to several variables simultaneously. This allows NetraMark to use a variety of ML methods, depending on the character and size of the data, to transform the data into powerfully intelligent data that activates traditional AI/ML methods. The result is that NetraMark can work with much smaller datasets often prevalent in clinical trials, and accurately segment diseases into different types, as well as help to classify patients for sensitivity to drugs and/or efficacy of treatment.
For further details on the Company please see the Company’s publicly available documents filed on the System for Electronic Document Analysis and Retrieval+ (SEDAR+).
Forward-Looking Statements
This press release contains “forward-looking information” within the meaning of applicable Canadian securities legislation including statements regarding the potential of an unbiased discovery first approach to uncover clinically relevant structures in complex trial data and inform future patient stratification and trial enrichment strategies, NetraAI’s fundamental differentiation from conventional machine learning, neural networks and newer foundation-model approaches; the ability of pharmaceutical sponsors to better understand drug response, comparator or placebo response and patient heterogeneity, and use those insights to inform future trial design and patient-enrichment strategies and the size of the potential opportunity for NetraMark which are based on NetraMark’s current internal expectations, estimates, projections, assumptions and beliefs, and views of future events. Forward-looking information can be identified by the use of forward-looking terminology such as “expect”, “likely”, “may”, “will”, “should”, “intend”, “anticipate”, “potential”, “proposed”, “estimate” and other similar words, including negative and grammatical variations thereof, or statements that certain events or conditions “may”, “would” or “will” happen, or by discussions of strategy. Forward-looking information includes estimates, plans, expectations, opinions, forecasts, projections, targets, guidance, or other statements that are not statements of fact. The forward-looking statements are expectations only and are subject to known and unknown risks, uncertainties and other important factors that could cause actual results of the Company or industry results to differ materially from future results, performance or achievements including that the findings are exploratory and subject to third party validation. Any forward-looking information speaks only as of the date on which it is made, and, except as required by law, NetraMark does not undertake any obligation to update or revise any forward-looking information, whether as a result of new information, future events, or otherwise. New factors emerge from time to time, and it is not possible for NetraMark to predict all such factors.
When considering these forward-looking statements, readers should keep in mind the risk factors and other cautionary statements as set out in the materials we file with applicable Canadian securities regulatory authorities on SEDAR+ at www.sedarplus.com including our Annual Information Form for the year ended September 30, 2025. These risk factors and other factors could cause actual events or results to differ materially from those described in any forward-looking information. The Toronto Stock Exchange does not accept responsibility for the adequacy or accuracy of this release.
Contact Information:
Swapan Kakumanu – CFO | swapan@netramark.com | 403-681-2549
Adam Peeler – Investor Relations | adam.peeler@loderockadvisors.com | 416-427-1235
Media Contact:
netramark@ampublicrelations.com
