ArticlebioRxiv : the preprint server for biology2024
Predicting metabolite response to dietary intervention using deep learning.
Tong Wang, Hannah D Holscher, Sergei Maslov, Frank B Hu, Scott T Weiss, Yang-Yu Liu
Open access · greenAbstract readPreprint
In one paragraphArticle in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed, 8 citations in OpenAlex.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
6 authors at 2 institutions in 1 country.
Tong WangChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.ORCID 0000-0001-9900-0307 Hannah D HolscherDepartment of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
Sergei MaslovDepartment of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.ORCID 0000-0002-3701-492X Frank B HuChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
Scott T WeissChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.ORCID 0000-0001-7196-303X Yang-Yu LiuChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.ORCID 0000-0003-2728-4907 Brigham and Women's Hospital · USUniversity of Illinois Urbana-Champaign · US
Funding
Therapeutic Control of Aspirin-Exacerbated Respiratory DiseaseU19AI095219 · NIAID · BRIGHAM AND WOMEN'S HOSPITAL · PI Joshua A Boyce · 2011 to 2026
$29.1MGenetic Epidemiology of COPDU01HL089856 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI SILVERMAN, EDWIN K · 2007 to 2021
$20.7MMulti-omic approaches to mechanisms of vitamin D, environmental influences, and the microbiome on asthmaUH3OD023268 · OD · BRIGHAM AND WOMEN'S HOSPITAL · PI LITONJUA, AUGUSTO A, WEISS, SCOTT T · 2018 to 2022
$13.5MProspective Study of the Gut Microbiome in AgingRF1AG067744 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI CHAN, ANDREW T · 2020 to 2020
$11.7MFunctional Redundancy of Human Microbiome and its Implication in Fecal Microbiota TransplantationR01AI141529 · NIAID · BRIGHAM AND WOMEN'S HOSPITAL · PI LIU, YANG-YU · 2019 to 2023
$4.5MLong-term health consequences of birth by cesarean sectionR01HD093761 · NICHD · HARVARD SCHOOL OF PUBLIC HEALTH · PI CHAVARRO, JORGE EDUARDO · 2018 to 2022
$3.3MNHLBI NIH HHS U01 HL089856NIAID NIH HHS R01 AI141529NIAID NIH HHS U19 AI095219NIA NIH HHS RF1 AG067744NICHD NIH HHS R01 HD093761NIH HHS UH3 OD023268
6 · The paper itselfAbstract
Due to highly personalized biological and lifestyle characteristics, different individuals may have different metabolite responses to specific foods and nutrients. In particular, the gut microbiota, a collection of trillions of microorganisms living in the gastrointestinal tract, is highly personalized and plays a key role in the metabolite responses to foods and nutrients. Accurately predicting metabolite responses to dietary interventions based on individuals' gut microbial compositions holds great promise for precision nutrition. Existing prediction methods are typically limited to traditional machine learning models. Deep learning methods dedicated to such tasks are still lacking. Here we develop a method McMLP (
Identifiers
PMID36993761
PMCPMC10054958
OpenAlexW4324386054
What OpenQuestion holds
Textmetadata
LicenceCC BY-NC
Read underepoch 390