Genomic Prediction
Biotechnology, Fremont BLVD, North Brunswick, , 94538, New Jersey, 46575, United States, 11-50 Employees
Phone Number: 19*********
Who is GENOMIC PREDICTION
Genomic Prediction provides advanced genomic tests which improve IVF health outcomes. Genomic Prediction Inc. (GP) was incorporated in Delaware, May 1, 2017. The research insights which l...
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- Headquarters: 46575 Fremont BLVD, North Brunswick, New Jersey, 94538, United States
- Date Founded: 2017
- Employees: 11-50
- Revenue: $1 Million to $5 Million
- Active Tech Stack: See technologies
- CEO: Laurent Tellier
Industry: Biotechnology
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Frequently Asked Questions Regarding Genomic Prediction
Answer: Genomic Prediction's headquarters are located at Fremont BLVD, North Brunswick, , 94538, New Jersey, 46575, United States
Answer: Genomic Prediction's phone number is 19*********
Answer: Genomic Prediction's official website is https://lifeview.com
Answer: Genomic Prediction's revenue is $1 Million to $5 Million
Answer: Genomic Prediction has 11-50 employees
Answer: Genomic Prediction is in Biotechnology
Answer: Genomic Prediction contact info: Phone number: 19********* Website: https://lifeview.com
Answer: Genomic Prediction provides advanced genomic tests which improve IVF health outcomes. Genomic Prediction Inc. (GP) was incorporated in Delaware, May 1, 2017. The research insights which led to the creation of GP stretch back much further. Research and data exchange agreements to develop molecular methodology, signed with some of the largest biotechnology vendors in the world, were ongoing as early as 2015. In the decade prior, the founders work in embryology, detection of chromosomal abnormality, computational genomics, algorithms, and polygenic architecture led to GPs superior and innovative methodology for the genetic testing of human embryos. GPs products incorporate fundamental improvements on existing methods. GP offers IVF parents a cost-effective means to evaluate genetic risk due to chromosomal abnormality (ploidy), single-gene mutations, and polygenic diseases. Accurate understanding of the genomic architecture of human disease requires larger training datasets, greater accuracy, and a more rigorous, empirical basis of computational modeling (machine learning) than implemented in the past with simpler technologies and simpler computational methods. GP represents the next step in embryo genetic testing, combining dense, genome-wide genotyping methods with sophisticated validation-focused modeling, suitable for the information age.
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