A significant shift is underway as the fields of health and biology are re-organizing into the larger ecosystems of information sciences and complexity sciences. The era of big data is transforming all economic sectors including health and biology. Three big health data streams are being integrated into a standardized investigative method in the realization of personalized medicine – creating individualized risk profiles and interventions such that medical conditions may be combatted during the 80% of their life-cycle while they are still pre-clinical. These three big health data streams are traditional medical data, ‘omics’ data (genomics, microbiomics, proteomics, etc.), and biometric quantified-self daily analytic data. Sequencing costs have continued to decrease such that consumer ‘omics’ data is increasingly available. Simultaneously, the potentially fast-arriving wearable electronics platform (smartwatches, disposable patches, augmented eyewear, etc.) means that it could become possible to unobtrusively collect vast amounts of previously-unavailable objective metric data for each individual and parlay this into personalized physical and mental health optimization platforms. Two experimental protocols are presented here putting this model of integrated health data streams into action and extending recent social intelligence genomics research into the realm of cognitive performance genomics. The DIYgenomics Quantified Creativity study investigates potential linkage between personal genomics and the creative process of the individual. The DIYgenomics Thinking Fast and Slow study examines cognitive bias in thinking (loss aversion and optimism bias) versus personal genomic profiles. The studies integrate big health data streams including traditional health data, personal genomics, quantified self-reported data, standardized questionnaires, and personalized intervention.
Artificial intelligence (AI) is continuing to make waves in the life sciences industry, with today's announcement that a drug-discovery company called Verge Genomics has landed $32 million in Series A financing. Based in San Francisco, Verge Genomics uses machine learning and sprawling data sets to identify new therapeutics for neurological diseases. Since its founding in 2015, the startup has nurtured "lead therapeutic programs" and built proprietary genomic data sets for Parkinson's disease and amyotrophic lateral sclerosis (ALS). Investors nodded to those advances and Verge Genomics' roster of diverse experts when they announced the windfall. Read: Which Health-Tech Startups Are Making Money in 2018?
Genomic is Python software that evolves sound treatments and produce novel sounds. It offers features that have the potential to serve sound designers and composers, aiding them in their search for new and interesting sounds. This paper lays out the rationale and some design decisions made for Genomic, and proposes several intuitive ways of both using the software and thinking about the techniques that it enables for the modification and design of sound.