24/7 Market News- Successful Retrospective Ovarian Cancer Study Data Paves way for Predictive Oncology’s entry in Biomarker Discovery Market

July 25, 2024

DENVER, Colo., Jul 25, 2024 (247marketnews.com)- Predictive Oncology Inc. (NASDAQ: POAI) is expanding its AI/ML driven drug discovery offering to include novel oncology biomarker discovery to predict patient outcomes and drug response in oncology, following results obtained in the retrospective ovarian cancer study with UPMC Magee-Womens Hospital, which were presented at the 2024 American Society of Clinical Oncology (ASCO) Annual Meeting.

Predictive Oncology’s CEO, Raymond Vennare, commented, “We believe the identification of novel cancer biomarkers represents the next significant opportunity for the application of our platform, which leverages the substantial value inherent in the diversified patient samples and data that we possess, as well as additional potential revenue streams for our company. Our technology has broad applicability, including the development of a clinical decision support tool to screen for clinical trial enrollment, and to inform subsequent drug discovery and development.

“These capabilities extend well beyond ovarian cancer and can be used in the discovery of biomarkers for other cancer types as well, and we look forward to further validating these capabilities through development collaborations with leading biopharmaceutical partners and healthcare networks.”

Arlette H. Uihlein, MD, SVP, Translational Medicine and Drug Discovery and Medical Director at Predictive Oncology, added, “We have already demonstrated the capabilities of our active machine learning platform to selectively utilize our diverse patient samples preserved in our biobank to predict responses to drugs with a very high degree of accuracy.

“We are now taking this one step further by applying state-of-the-art deep learning approaches for biomarker discovery related to both patient overall survival (OS) and drug response, which can be done with existing resources. Our platform enables us to apply deep learning to the correct patient cohorts and accelerate the initial stages of biomarker discovery.”

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