Predictive Oncology Inc.’s (NASDAQ: POAI) AI Solutions to

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Predictive Oncology Inc.’s (NASDAQ: POAI) AI Solutions to Combat Cancer, Similar Technological Solutions Called Upon to Fight COVID-19

- Artificial intelligence (AI) at forefront of scientific discovery, proving importance in fighting complex diseases
- Science turns to AI as effective tool to help find COVID-19 vaccines and therapeutics
- Predictive Oncology harnesses power of AI to improve cancer patient outcomes

With the global onslaught of COVID-19, scientists are harnessing the speed and power of artificial intelligence (AI) to find solutions. The complexity of this lethal contagion demands a rapid yet comprehensive response, and only advanced computer systems can parse through mountains of data sets swiftly enough to stem the pandemic. AI’s ability to sort and sift data quickly, spot patterns and make predictions can help identify both potential vaccines and therapeutics. Predictive Oncology Inc. (NASDAQ: POAI) is utilizing its proprietary AI solutions to understand and battle cancer – positioning the company as an attractive option for investors looking to invest in the future of cutting-edge medicine. These same AI solutions may be utilized to help find COVID-19 vaccines and therapeutics.

Through its subsidiary Helomics, a leader in AI and functional precision medicine, POAI is bringing its innovative technology to cancer research, where it’s working with the pharmaceutical, diagnostic and biotech industries to develop predictive models of how tumors respond to drugs, which can be used both for clinical decision support and research into new therapies.

The power and utility of artificial intelligence was delineated in a recent Wall Street Journal article titled ‘Biotech Companies Tap AI to Speed Path to Coronavirus Treatments’ (http://nnw.fm/BJ7qk). While drug development – from invention to clinical trials – can take more than a year, health care industry leaders are looking to AI to identify drug prospects to test on humans within months. POAI’s predictive models can be applied clinically to aid patients and in the development of new drugs by researchers.

Aggressive situations like the steady spread of COVID-19, among other fast-moving cancers and diseases, has the global community scrambling for an expedited solution – a problem that AI-powered technology has the potential to help solve. “[COVID-19] is a rapidly developing situation, and we firmly believe that AI-driven experimentation is the right approach for these kinds of problems, to speed up the process,” stated professor Robert Murphy, Ph.D., a recognized expert in AI and computational biology. Dr. Murphy also sits on Predictive Oncology’s advisory board and is the head of the computational biology department in the School of Computer Science at Carnegie Mellon University and co-founder of AI predictive-modeling company Quantitative Medicine LLC.

In a pivotal collaborative move, Predictive Oncology has completed a letter of intent (http://nnw.fm/EC98y) to acquire Quantitative Medicine and integrate its CoRE predictive modeling platform with POAI’s massive historic database and tumor profiling expertise with the aim of revolutionizing the way precision therapies are developed. The acquisition will provide POAI with Quantitative Medicine’s proven machine learning framework, CoRE. A predictive model-building platform for drug screening and optimization campaigns, CoRE uses hybrid machine learning approaches to rapidly build predictive models that spawn specific wet lab experiments. This cutting-edge technology, similar in methodology to the current AI and machine learning approaches now used in the battle against COVID-19, has the potential to expedite POAI’s mission to use AI and machine learning to discover unique solutions to the future of disease.

For more information, visit the company’s website at www.Predictive-Oncology.com

Please see full disclaimers on the NetworkNewsWire website applicable to all content provided by NNW, wherever published or re-published: http://NNW.fm/Disclaimer

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