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Next-Generation Personal Genomic Studies: Extending Social Intelligence Genomics to Cognitive Performance Genomics in Quantified Creativity and Thinking Fast and Slow

AAAI Conferences

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.


Go behind the scenes of one of the world's most advanced genomics labs

MIT Technology Review

For the first time ever, EmTech MIT, our flagship event on emerging technologies and trends, will be held virtually. Don't miss these engaging new sessions.


Genomic: Combining Genetic Algorithms and Corpora to Evolve Sound Treatments

AAAI Conferences

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.


Diagnomics – Diagnostic Tests and Genomic Sequencing Tools

#artificialintelligence

Diagnomics offers high quality innovative R&D and CLIA services to global pharmaceutical, biotech, and medical device companies as well as academic institutions. Diagnomics provides comprehensive consumer genomics solutions based on several platforms including but not limited to DNA microarrays, Next Generation Sequencing and real-time PCR. We strive to deliver confidence and reliability by offering comprehensive genetic solutions for the development of personalized healthcare and precision medicine in the genomic market. Our strength comes from a highly qualified team of scientists including early pioneers in sequencing the human genome. We have cutting edge genome presentation software as well as genome discovery expertise to guide the development of novel diagnostic tools and new therapeutic targets.


Lessons from an Online Massive Genomics Computer Game

AAAI Conferences

Crowdsourcing through human-computing games is an increasingly popular practice for classifying and analyzing scientific data. Early contributions such as Phylo have now been running for several years. The analysis of the performance of these systems enables us to identify patterns that contributed to their successes, but also possible pitfalls. In this paper, we review the results and user statistics collected since 2010 by our platform Phylo, which aims to engage citizens in comparative genome analysis through a casual tile matching computer game. We also identify features that allow predicting a task difficulty, which is essential for channeling them to human players with the appropriate skill level. Finally, we show how our platform has been used to quickly improve a reference alignment of Ebola virus sequences.