MS FutuResearch Group

Palo Alto, CA, United States

MS FutuResearch Group

Palo Alto, CA, United States
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Kido T.,RIKEN | Swan M.,MS FutuResearch Group
AAAI Spring Symposium - Technical Report | Year: 2016

Two big recent revolutions: machine learning technologies; such as "deep learning" in Artificial Intelligence (AI), and personal genome informatics in biomedical science, provide us with new opportunities for understanding human happiness. Our ongoing important challenges are to discover our own truly meaningful personal happiness with the aid of AI and personal genome technologies. We have been developing a personal genome information agent entitled MyFinder, which supports searching for our inherited talents and maximizes our potential for a meaningful life. In the MyFinder project, we have provided a crowd-sourced DIY (Do it yourself) genomics research platform and conducted various "citizen science" projects in health and wellness. In this paper, we discuss how machine learning technologies and personal genome informatics might contribute to happiness sciences. We introduce the "Social Intelligence Genomics and Empathy-Building Study" and report the preliminary results of applying deep learning and six other machine learning algorithms for predicting social intelligence levels from nine SNPs genetic profiles. We discuss the possibilities and limitations of applying machine learning technologies for personal happiness trait prediction. We also discuss future AI challenges in the context of wellbeing computing. Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.


Stem cell research could have a significant near-term public health impact with applications in cell-replacement therapies (treatments are in development for over 50 diseases), disease modeling and drug discovery. Recent advances have been achieved in techniques for reprogramming somatic cells directly to neurons and neural stem cells, and in differentiating pluripotent cells to neurons and neural stem cells. Neurons and neural stem cells are of particular translational interest due to the lack of effective clinical therapies for neurodegenerative disease. This analysis reviews recent reprogramming and differentiation research advances that relate to human neuron and neural stem cell generation. While these emerging techniques are promising, some of the processes are relatively new, and the fidelity and functionality of generated neurons and neural stem cells in clinical application is yet to be confirmed. © 2012 Future Medicine Ltd.


Background: Crowdsourced health research studies are the nexus of three contemporary trends: 1) citizen science (non-professionally trained individuals conducting science-related activities); 2) crowdsourcing (use of web-based technologies to recruit project participants); and 3) medicine 2.0/health 2.0 (active participation of individuals in their health care particularly using web 2.0 technologies). Crowdsourced health research studies have arisen as a natural extension of the activities of health social networks (online health interest communities), and can be researcher-organized or participant-organized. In the last few years, professional researchers have been crowdsourcing cohorts from health social networks for the conduct of traditional studies. Participants have also begun to organize their own research studies through health social networks and health collaboration communities created especially for the purpose of self-experimentation and the investigation of health-related concerns. Objective: The objective of this analysis is to undertake a comprehensive narrative review of crowdsourced health research studies. This review will assess the status, impact, and prospects of crowdsourced health research studies. Methods: Crowdsourced health research studies were identified through a search of literature published from 2000 to 2011 and informal interviews conducted 2008-2011. Keyword terms related to crowdsourcing were sought in Medline/PubMed. Papers that presented results from human health studies that included crowdsourced populations were selected for inclusion. Crowdsourced health research studies not published in the scientific literature were identified by attending industry conferences and events, interviewing attendees, and reviewing related websites. Results: Participatory health is a growing area with individuals using health social networks, crowdsourced studies, smartphone health applications, and personal health records to achieve positive outcomes for a variety of health conditions. PatientsLikeMe and 23 and Me are the leading operators of researcher-organized, crowdsourced health research studies. These operators have published findings in the areas of disease research, drug response, user experience in crowdsourced studies, and genetic association. Quantified Self, Genomera, and DIYgenomics are communities of participant-organized health research studies where individuals conduct self-experimentation and group studies. Crowdsourced health research studies have a diversity of intended outcomes and levels of scientific rigor. Conclusions: Participatory health initiatives are becoming part of the public health ecosystem and their rapid growth is facilitated by Internet and social networking influences. Large-scale parameter-stratified cohorts have potential to facilitate a next-generation understanding of disease and drug response. Not only is the large size of crowdsourced cohorts an asset to medical discovery, too is the near-immediate speed at which medical findings might be tested and applied. Participatory health initiatives are expanding the scope of medicine from a traditional focus on disease cure to a personalized preventive approach. Crowdsourced health research studies are a promising complement and extension to traditional clinical trials as a model for the conduct of health research.


Swan M.,MS FutuResearch Group
Personalized Medicine | Year: 2012

Accessing crowdsourced cohorts for health studies is a significant emerging opportunity that could have a positive impact on public health research, particularly as outcomes shift to the personalized, preventive medicine of the future. Health social networks have grown to become some of the largest aggregate patient registries and offer cost and efficiency benefits for study recruitment and operation by both traditional researchers and citizen scientists. Here, a model is proposed for extending crowdsourced studies beyond small-group experimentation to large-scale intervention-based research studies that are professionally run and scientifically rigorous, in effect creating a new form of contract research organization. © 2012 Future Medicine Ltd.


Stem cell research and related therapies (including regenerative medicine and cellular therapies) could have a significant near-term impact on worldwide public health and aging. One reason is the industry's strong linkage between policy, science, industry, and patient advocacy, as was clear in the attendance and programming at the 7 th annual World Stem Cell Summit held in Pasadena, California, October 3-5, 2011. A special conference session sponsored by the SENS Foundation discussed how stem cell therapies are being used to extend healthy life span. Stem cells are useful not only in cell-replacement therapies, but also in disease modeling, drug discovery, and drug toxicity screening. Stem cell therapies are currently being applied to over 50 diseases, including heart, lung, neurodegenerative, and eye disease, cancer, and human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS). Dozens of companies are developing therapeutic solutions that are in different stages of clinical use and clinical trials. Some high-profile therapies include Dendreon's Provenge for prostate cancer, Geron's first-ever embryonic stem cell trials for spinal cord injury, Fibrocell's laViv cellular therapy for wrinkles, and well-established commercial skin substitutes (Organogenesis' Apligraf and Advanced BioHealing's Dermagraft). Stem cell policy issues under consideration include medical tourism, standards for large-scale stem cell manufacturing, and lingering ethical debates over the use of embryonic stem cells. Contemporary stem cell science advances include a focus on techniques for the direct reprogramming of cells from one lineage to another without returning to pluripotency as an intermediary step, improved means of generating and characterizing induced pluripotent cells, and progress in approaches to neurodegenerative disease. © Copyright 2011, Mary Ann Liebert, Inc. 2011.


The focus of the 2011 American Aging Association meeting was emerging concepts in the mechanisms of aging. Many of the usual topics in aging were covered, such as dietary restriction (DR), inflammation, stress resistance, homeostasis and proteasome activity, sarcopenia, and neural degeneration. There was also discussion of newer methods, such as microRNAs and genome sequencing, that have been employed to investigate gene expression variance with aging and genetic signatures of longevity. Aging as a field continues to mature, including the following areas: Using a systems approach to tracing conserved pathways across organisms; sharpening definitions of sarcopenia, frailty, and health span; and distinguishing interventions by age tier (early-onset versus late-onset). A preconference session on late-onset intervention concluded that there are numerous benefits to deriving such interventions. Conference talks applied the biology of aging in a translational manner to intervention development. Using an individual's own stem cells to regenerate organs for transplantation and as a cell source for cellular therapies could be a powerful near-term solution to disease. Several proposed interventions were pharmaceutical, myostatin inhibition, losartan, Janus kinase (JAK) pathway inhibitors, and enalapril for frailty and sarcopenia, and metformin to promote the Nrf2 antiinflammation response. In DR, protein restriction was found to be better than general calorie restriction. Short-term fasting may be helpful in chemotherapy, surgery, and acute stress, simultaneously increasing the killing of cancer cells by chemotherapy, while improving the survival of normal cells. Immune system interventions remain elusive, although statins may help to improve cellular senescence promoted bacterial infection. Engineered enzymes may be useful in lysosomal catabolism. Dietary restriction mimetics, most promisingly involving target of rapamycin (TOR; TORC1 inhibition and rapamycin), may be more feasible than dietary restriction. © 2011 Mary Ann Liebert, Inc.


Swan M.,MS FutuResearch Group
Genetics in Medicine | Year: 2010

PURPOSE: Gene carrier status and pharmacogenomic data may be detectable from single nucleotide polymorphisms (SNPs), but SNP-based research concerning multigenic common disease such as diabetes, cancers, and cardiovascular disease is an emerging field. The many SNPs and loci that may relate to common disease have not yet been comprehensively identified and understood scientifically. In the interim, direct-to-consumer (DTC) genomic companies have forged ahead in developing composite risk interpretations for multigenic conditions. It is useful to understand how variance in risk interpretation may arise. METHODS: A comprehensive study was conducted to analyze the 213 conditions covered by the 5 identifiable genome-wide DTC genomic companies, and the total SNPs (401) and loci (224) assessed in the 20 common disease conditions with the greatest overlapping coverage. RESULTS: Variance in multigenic condition risk interpretation can be explained by differences in the average lifetime risk assigned to similar underlying populations, the loci and SNPs selected for analysis, and the quantitative risk assignment methodologies used by DTC genomic companies. CONCLUSION: At present, multigenic condition analysis is a complicated process. DTC genomic companies have made laudable efforts to deliver risk predictions, but greater consistency is needed for the long-term validity, utility, and credibility of the sector. © 2010 Lippincott Williams & Wilkins.


A key contemporary trend emerging in big data science is the quantified self (QS)-individuals engaged in the self-tracking of any kind of biological, physical, behavioral, or environmental information as n=1 individuals or in groups. There are opportunities for big data scientists to develop new models to support QS data collection, integration, and analysis, and also to lead in defining open-access database resources and privacy standards for how personal data is used. Next-generation QS applications could include tools for rendering QS data meaningful in behavior change, establishing baselines and variability in objective metrics, applying new kinds of pattern recognition techniques, and aggregating multiple self-tracking data streams from wearable electronics, biosensors, mobile phones, genomic data, and cloud-based services. The long-term vision of QS activity is that of a systemic monitoring approach where an individual's continuous personal information climate provides real-time performance optimization suggestions. There are some potential limitations related to QS activity - barriers to widespread adoption and a critique regarding scientific soundness - but these may be overcome. One interesting aspect of QS activity is that it is fundamentally a quantitative and qualitative phenomenon since it includes both the collection of objective metrics data and the subjective experience of the impact of these data. Some of this dynamic is being explored as the quantified self is becoming the qualified self in two new ways: by applying QS methods to the tracking of qualitative phenomena such as mood, and by understanding that QS data collection is just the first step in creating qualitative feedback loops for behavior change. In the long-term future, the quantified self may become additionally transformed into the extended exoself as data quantification and self-tracking enable the development of new sense capabilities that are not possible with ordinary senses. The individual body becomes a more knowable, calculable, and administrable object through QS activity, and individuals have an increasingly intimate relationship with data as it mediates the experience of reality. © Mary Ann Liebert, Inc. 2013.


Swan M.,MS FutuResearch Group
AAAI Spring Symposium - Technical Report | Year: 2012

The current era of internet-facilitated bigger data, better tools, and collective intelligence community computing is accelerating advances in many areas ranging from artificial intelligence to knowledge generation to public health. In the health sector, data volumes are growing with genomic, phenotypic, microbiomic, metabolomic, self-tracking, and other data streams. Simultaneously, tools are proliferating to allow individuals and groups to make sense of these data in a participatory manner through personal health tracking devices, mobile health applications, and personal electronic medical records. Health community computing models are emerging to support individual activity and mass collaboration through health social networks and crowdsourced health research studies. Participatory health efforts portend important benefits based on both size and speed. Studies can be carried out in cohorts of thousands instead of hundreds, and it could be possible to apply findings from newly-published studies with near-immediate speed. One operator of interventional crowdsourced health research studies, DIYgenomics, has several crowdsourced health research studies in open enrollment as of January 2012 in the areas of vitamin deficiency, aging, mental performance, and epistemology. The farther future of intelligent health community computing could include personal health dashboards, continuous personal health information climates, personal virtual coaches (e.g.; Siri 2.0), and an efficient health frontier of dynamic personalized health recommendations and action-taking. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.


Crowdsourced health research studies are the nexus of three contemporary trends: 1) citizen science (non-professionally trained individuals conducting science-related activities); 2) crowdsourcing (use of web-based technologies to recruit project participants); and 3) medicine 2.0 / health 2.0 (active participation of individuals in their health care particularly using web 2.0 technologies). Crowdsourced health research studies have arisen as a natural extension of the activities of health social networks (online health interest communities), and can be researcher-organized or participant-organized. In the last few years, professional researchers have been crowdsourcing cohorts from health social networks for the conduct of traditional studies. Participants have also begun to organize their own research studies through health social networks and health collaboration communities created especially for the purpose of self-experimentation and the investigation of health-related concerns. The objective of this analysis is to undertake a comprehensive narrative review of crowdsourced health research studies. This review will assess the status, impact, and prospects of crowdsourced health research studies. Crowdsourced health research studies were identified through a search of literature published from 2000 to 2011 and informal interviews conducted 2008-2011. Keyword terms related to crowdsourcing were sought in Medline/PubMed. Papers that presented results from human health studies that included crowdsourced populations were selected for inclusion. Crowdsourced health research studies not published in the scientific literature were identified by attending industry conferences and events, interviewing attendees, and reviewing related websites. Participatory health is a growing area with individuals using health social networks, crowdsourced studies, smartphone health applications, and personal health records to achieve positive outcomes for a variety of health conditions. PatientsLikeMe and 23andMe are the leading operators of researcher-organized, crowdsourced health research studies. These operators have published findings in the areas of disease research, drug response, user experience in crowdsourced studies, and genetic association. Quantified Self, Genomera, and DIYgenomics are communities of participant-organized health research studies where individuals conduct self-experimentation and group studies. Crowdsourced health research studies have a diversity of intended outcomes and levels of scientific rigor. Participatory health initiatives are becoming part of the public health ecosystem and their rapid growth is facilitated by Internet and social networking influences. Large-scale parameter-stratified cohorts have potential to facilitate a next-generation understanding of disease and drug response. Not only is the large size of crowdsourced cohorts an asset to medical discovery, too is the near-immediate speed at which medical findings might be tested and applied. Participatory health initiatives are expanding the scope of medicine from a traditional focus on disease cure to a personalized preventive approach. Crowdsourced health research studies are a promising complement and extension to traditional clinical trials as a model for the conduct of health research.

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