Biomonitoring and precision health in deep space supported by artificial intelligence

Scott, Ryan T.; Sanders, Lauren; Antonsen, Erik L.; Hastings, Jaden J. A.; Park, Seung Min; Mackintosh, Graham; Reynolds, Robert J.; Hoarfrost, Adrienne; Sawyer, Aenor; Greene, Casey S.; Glicksberg, Benjamin S.; Theriot, Corey A.; Berrios, Daniel C.; Babdor, Joël; Barker, Richard; Baranzini, Sergio E.; Beheshti, Afshin; Chalk, Stuart; Delgado-Aparicio, Guillermo M.; Haendel, Melissa; Hamid, Arif; Heller, Philip; Jamieson, Daniel; Jarvis, Katelyn; Kalantari, John; Khezeli, Kia; Komarova, Svetlana V.; Komorowski, Matthieu; Kothiyal, Prachi; Mahabal, A.; Manor, Uri; Martín, Héctor García; Mason, Christopher E.; Matar, Mona; Mias, George I.; Myers, Jerry G.; Nelson, Charlotte; Oribello, Jonathan; Parsons‐Wingerter, Patricia; Prabhu, Raj; Qutub, Amina A.; Rask, Jon; Saravia-Butler, Amanda; Saria, Suchi; Singh, Nitin Kumar; Snyder, M; Soboczenski, Frank; Soman, Karthik; Venkateswaran, Kasthuri; Warren, Liz; Worthey, Liz; Yang, Jason H.; Žitnik, Marinka; Costes, Sylvain V.

doi:10.1038/s42256-023-00617-5

Cited by 16 publications

(11 citation statements)

References 121 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, a portion of the workshop discussion focused on development of AI and ML algorithms specifically designed for data collected in novel space constraints and environments. A parallel problem is limited computational resources in spaceflight, and there is a detailed discussion of recommendations for this problem in the companion review article from this workshop 12 .…”

Section: Ai and ML Methods For Space Researchmentioning

confidence: 99%

“…Due to limited bandwidth in space, our efforts should focus on developing multi-faceted solutions including pre-training lightweight models on larger Earth-bound datasets, federated training 166 , edge computing 167 and onboard processing. A more in-depth discussion of these solutions is in the accompanying workshop review article 12 .…”

Section: Adapting and Developing Ai And ML Methods For Space Biologymentioning

confidence: 99%

“…On the basis of mathematical principles and computer science, AI and ML methodology trains algorithms to predict outcomes and probabilities of interest [9][10][11] . A parallel review article reviews the workshop participants' recommendations regarding the roles of AI and ML for astronaut, ecosystem and precision space health 12 .…”

Section: Automated Experiments In Space Supported By Aimentioning

confidence: 99%

“…A central goal of developing and employing autonomous, AI-supported bioexperimentation systems such as self-driving laboratories should be to generate data that can inform autonomous precision space health systems that provide decision support for crew health management during LEO, cis-lunar and Mars missions 12 . As automated experimentation becomes more widely available, the space biology field should shift to conducting longitudinal studies 99 , characterizing physiological changes over the duration of an entire mission.…”

Section: Review Articlementioning

confidence: 99%

See 3 more Smart Citations

Biological research and self-driving labs in deep space supported by artificial intelligence

et al. 2023

Self Cite

View full text Add to dashboard Cite

Space biology research aims to understand fundamental spaceflight effects on organisms, develop foundational knowledge to support deep space exploration and, ultimately, bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data and model organisms from both spaceborne and ground-analogue studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally automated, light, agile and intelligent to accelerate knowledge discovery. Here we present a summary of decadal recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning and modelling applications that offer solutions to these space biology challenges. The integration of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modelling and analytics, support maximally automated and reproducible experiments, and efficiently manage spaceborne data and metadata, ultimately to enable life to thrive in deep space.Space biology research focuses on answering fundamental mechanistic questions about how molecular, cellular, tissue and whole organismal life responds to the space environment. Biological stressors of spaceflight include ionizing radiation, altered gravitational fields, accelerated day-night cycles, confined isolation, hostile closed environments, distance-duration from Earth 1 , planetary dust regolith 2,3 , and extreme temperatures and atmospheres 4,5 . Moreover, spaceflight stressors are probably compounded and amplified with increasing

show abstract

Section: Ai and ML Methods For Space Researchmentioning

confidence: 99%

Section: Adapting and Developing Ai And ML Methods For Space Biologymentioning

confidence: 99%

Section: Automated Experiments In Space Supported By Aimentioning

confidence: 99%

Section: Review Articlementioning

confidence: 99%

See 2 more Smart Citations

Biological research and self-driving labs in deep space supported by artificial intelligence

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…OSDR’s dedication to comprehensive and unambiguous metadata, coupled with the utilization of the GLOpenAPI, underscores its readiness for analysis and artificial intelligence applications. The data query method illustrated here marks an initial step toward realizing the goal of Precision Space Health 10 , 11 . In cases where users seek to employ private medical datasets such as those collected on I4 for research purposes, an application process is in place that mandates approval from an institutional review board (IRB).…”

Section: Technical Validationmentioning

confidence: 99%

Inspiration4 data access through the NASA Open Science Data Repository

Sanders,

Grigorev,

Scott

et al. 2024

npj Microgravity

Self Cite

View full text Add to dashboard Cite

The increasing accessibility of commercial and private space travel necessitates a profound understanding of its impact on human health. The NASA Open Science Data Repository (OSDR) provides transparent and FAIR access to biological studies, notably the SpaceX Inspiration4 (I4) mission, which amassed extensive data from civilian astronauts. This dataset encompasses omics and clinical assays, facilitating comprehensive research on space-induced biological responses. These data allow for multi-modal, longitudinal assessments, bridging the gap between human and model organism studies. Crucially, community-driven data standards established by NASA’s OSDR Analysis Working Groups empower artificial intelligence and machine learning to glean invaluable insights, guiding future mission planning and health risk mitigation. This article presents a concise guide to access and analyze I4 data in OSDR, including programmatic access through GLOpenAPI. This pioneering effort establishes a precedent for post-mission health monitoring programs within space agencies, propelling research in the burgeoning field of commercial space travel’s impact on human physiology.

show abstract

Sustainable Application of Artificial Intelligence in Biomonitoring for Environmental Sustainability: Challenges and Prospects

Hait,

Sahu,

Biswas

et al. 2024

Biomonitoring of Pollutants in the Global South

View full text Add to dashboard Cite

Biomonitoring and precision health in deep space supported by artificial intelligence

Cited by 16 publications

References 121 publications

Biological research and self-driving labs in deep space supported by artificial intelligence

Biological research and self-driving labs in deep space supported by artificial intelligence

Inspiration4 data access through the NASA Open Science Data Repository

Sustainable Application of Artificial Intelligence in Biomonitoring for Environmental Sustainability: Challenges and Prospects

Contact Info

Product

Resources

About