A member of the AI Horizons Network
Artificial Intelligence for Healthy Living
The Center for Microbiome Innovation and IBM Research have partnered to leverage machine learning methods to generate novel findings that implicate the human microbiome. As a response to the ongoing global pandemic, the AIHL group pivoted our existing focus to work on front-line COVID-19 research.
Publications
SARS-CoV-2 detection status associates with bacterial community composition in patients and the hospital environment
Microbiome | June 8, 2021
Challenges in benchmarking metagenomic profilers
Nature Methods | June 6, 2021
Compositional and genetic alterations in Graves’ disease gut microbiome reveal specific diagnostic biomarkers
The ISME Journal | June 2, 2021
Utilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data
Biometric Practice | April 29, 2021
EMPress Enables Tree-Guided, Interactive, and Exploratory Analyses of Multi-omic Data Sets
mSystems | March 16, 2021
Normalization of Predominant and Long-tail Bacterial Entities with a Hybrid CNN-LSTM and Knowledge-Driven Model, SciNLP workshop, Automated Knowledge Base Construction
AKBC 2020 | June 15, 2020
Visualizing 'omic feature rankings and log-ratios using Qurro
NAR Genom Bioinform | June 2, 2020
Human Skin, Oral, and Gut Microbiomes Predict Chronological Age
mSystems | February 11, 2020
Latest Press & News Releases
Coronavirus can live on surfaces – but surface transmission is rare
While the coronavirus may be able to survive on beds, floors, and other surfaces near COVID patients, it’s unlikely to be passed to another person, a new study finds.
SARS-CoV-2 Detectable — Though Likely Not Transmissible — on Hospital Surfaces
The coronavirus that causes COVID-19 also tends to co-locate with one particular type of bacteria
SARS-CoV-2 detectable — though likely not transmissible — on hospital surfaces
The coronavirus that causes COVID-19 also tends to co-locate with one particular type of bacteria
IBM and UC San Diego to pivot AI for Healthy Living collaboration to take on COVID-19 pandemic
The University of California San Diego and IBM are building on the existing AI for Healthy Living (AIHL) collaboration in order to help tackle the COVID-19 pandemic.
Your skin microbiome can be used to predict your age
As we age, our skin changes, and so too do the bugs that live there
AI developed to predict age based on gut, skin and mouth microbiomes
A new tool has been developed that can effectively predict a person’s chronological age based on a microbiome sample. The tool, developed in collaboration between UC San Diego and IBM researchers, is most accurate at predicting a person’s age when using a skin microbiome sample.
More than Just a Carnival Trick: Researchers Can Guess Your Age Based on Your Microbes
New understanding of how our microbiomes change as we age sets the stage for future research on the role microbes play in accelerating or decelerating the aging process and influencing age-related diseases
Predicting your age from your skin, mouth and gut microbiome
Just say aah: The bugs living on your skin, and in your mouth and gut could reveal your age.
Teams
Healthy Aging
Dilip Jeste, M.D.
Professor of Psychiatry
Researchers
Colin Depp, Ph.D., Camille Nebeker, Ph.D., Xin Tu, Ph.D., Beth Twamley, Ph.D., Ellen Lee, M.D./Ph.D., Anthony Molina, Ph.D., Lina M. Scandalis, Ph.D., Danielle Glorioso, LCSW, Nicole Danie, Rebecca Daly, Ruth Rodriguez, Jaclyn Calkins, Nick Macias, Molly Patapoff, Cynthia Ibarra, Alexa Hernandez, Frankie Sullivan, Ryan Van Patten, Ph.D., Morris (Tsung-Chin) Wu, Emily Treichler, PhD, Chen Du
Undergraduate Researchers
Rita Hedo, Jackie Pascual, Yadira Maldonado Aina Guatno, Mara Scalcione, Alice Biaggi, David Carlson, B.S., Sarah Graham, Ph.D., Marina Ramsey, Linjun Li, Zhe Tang, Himanshu Gupta, Zanjbeel Mahmood, Bethany Weisberg, Christine Xue, Federica Klaus, M.D./Ph.D., Shenghi Wang
Computational Biology
Rob Knight, Ph.D.
Professor of Pediatrics, Bioengineering, and Computer Science and Engineering
Researchers
Shi Huang, Ph.D., Alex Richter, MSc, Kalen Cantrell, and George Armstrong
Undergraduate Researchers
Ritik Raina
Natural Language Processing
Chun-Nan Hsu, Ph.D.
Associate Adjunct Professor, Medicine
Researchers
Canlin Zhang, Ph.D., Jiacheng Li, and Bill Hogan
Undergraduate Researchers
Molly Huang
Past Contributors
Zech Xu, Ph.D., Tomasz Kościółek, Ph.D., Erfan Sayyari, Ph.D., James Morton, Ph.D., Seren Jiang, Ph.D., Imran McGrath, Marcus Fedarko, Yimeng Yang, Xuhan Yang, Denghui Chen, Ethan Hsu, Dang Le, Devon Do, Omar Mendoza, Varsha Dave Badal, Ph.D., Suprabha Somenshekhar, Dustin Wright, Rghav Mehta, Goo Gu
