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Predictive Technology Group Appoints Dr. Hugh S. Taylor, M.D. to the Predictive Technology Group's Scientific Advisory Board

(January 04, 2018)

SALT LAKE CITY, Utah, Jan. 04, 2018 (GLOBE NEWSWIRE) -- Predictive Technology Group, Inc. (OTC PINK:PRED) announces that Dr. Hugh S. Taylor, M.D., joined the Predictive Technology Groupís Scientific Advisory Board. Predictive Technology Group, Inc.†specializes in genetic testing, diagnostics, and therapeutic products to advance precision medicine. Dr. Taylor, a Chief of Obstetrics and Gynecology at Yale University, brings extensive clinical research and development expertise in endometriosis and womenís health to PREDís Scientific Advisory Board team.

Dr. Taylorís many accomplishments include Anita O'Keeffe Young Professor and Chairs Department of Obstetrics Gynecology and Reproductive Sciences at Yale School of Medicine and Chief of Obstetrics, and Gynecology at Yale-New Haven Hospital. He teaches as Professor of Molecular, Cellular and Developmental Biology at Yale University. His clinical interests include endometriosis, infertility, implantation, Ashermanís syndrome, menopause and uterine anomalies.

Also, Dr. Taylor, a board-certified specialist in Obstetrics and Gynecology and in Reproductive Endocrinology, received eight National Institutes of Health research grants. Dr. Taylor published more than 300 articles in leading medical journals, continues as the Editor-In-Chief of Reproductive Sciences, and elected to the membership†of the National Academy of Medicine.

Dr. Taylor received his undergraduate training at Yale University and his medical degree from the University Of Connecticut School Of Medicine. He completed his residency in obstetrics and gynecology at Yale. His postdoctoral training included a fellowship in reproductive endocrinology and infertility, as well as, a fellowship in molecular biology, both at Yale.

Predictive plans to launch several new molecular diagnostic tests focused on womenís health, and a therapeutic drug for the potential treatment of endometriosis.

About Predictive Technology Group, Inc.

Predictive Technology Group, Inc. (OTC PINK:PRED), a Salt Lake City, UT, based Life-Sciences Holding company formed in 2013, aims to revolutionize patient care through predictive data analytics, novel gene-based diagnostics, and companion therapeutics through its wholly owned subsidiaries Predictive Therapeutics and Predictive Biotech. Its subsidiary companies currently focus on four main categories: Endometriosis, Scoliosis, Degenerative Disc Disease and Regenerative Tissue Products. PRED is exploring possibilities for additional disease identification and treatment categories in its integrated, registered laboratories. PRED subsidiaries use genetic and other information as cornerstones in the development of new diagnostics that assess a personís risk of illness and therapeutic products designed to identify, prevent and treat diseases more effectively, www.predictivetechnologygroup.com, www.predrx.com and www.predbiotech.com.

For more information about Predictive Technology Group, Inc. and the release, contact Rich Kaiser, Investor Relations, YES INTERNATIONAL, 757-306-6090 and [email protected]

Forward-Looking Statements- To the extent any statements made in this release contain information that is not historical, these statements are essentially forward-looking and are subject to risks and uncertainties, including the difficulty of predicting FDA approvals, acceptance and demand for new vaccines and other pharmaceutical products, the impact of competitive products and pricing, new product development and launch, reliance on key strategic alliances, availability of raw materials, availability of additional intellectual property rights, availability of future financing sources, the regulatory environment, and other risks the Company may identify from time to time in the future.

Contact:

Rich KaiserInvestor Relations


757-306-6090

A photo accompanying this announcement is available at http://www.globenewswire.com/NewsRoom/AttachmentNg/53c13148-ed21-495c-9d2b-082b5f3c176e


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