How will we do this?
Projects in the Genomics Collaborative are focused on using computational techniques that integrate phenotypic data into the analysis of human genetics and health. This process, known as “next-generation phenotyping,” or NGP, captures, structures and interprets complex physiological information.
This NGP-generated data is used for the interpretation of patient genomic data to help recognize current and future health risks, as well as to identify therapeutic targets that will maximize quality and length of life.
To accomplish these goals, FDNA has offered collaborators access to its deep learning technologies, which use neural networks to de-identify and analyze patients’ phenotypic information captured in images, voice and video recordings, and clinical notes—to discover correlations between that data and disease.
What Can We Learn?
Unlocking the secrets to our genetic makeup is the key to a healthier world. By uncovering the genetic components of diseases, we can shorten the diagnostic odyssey, enable greater and earlier recognition of these conditions in the medical community, and lead the way to more personalized and effective therapy.
By collecting large amounts of data on each of these unique conditions, we can better understand the presentation of a disease throughout a patient’s lifespan. We’ll know more about what a patient with this condition might look like, feel like, and act like from infancy to adulthood–making it easier for health care providers to recognize an undiagnosed disease and for patients to know what they can expect in the months or years ahead.
The Genomics Collaborative also hopes to learn more about patient behavior, commonly used treatments, frequently visited medical facilities, supporting advocacy organizations, and available resources to physicians, patients and their caregivers.