Evidence map›Paper›PMID 34545335›Full record

ArticleArXiv2021

ASGARD: A Single-cell Guided pipeline to Aid Repurposing of Drugs.

Bing He, Yao Xiao, Haodong Liang, Qianhui Huang, Yuheng Du, Yijun Li, David Garmire, Duxin Sun, Lana X Garmire

Open access · greenAbstract readPreprint
In one paragraph

Article in ArXiv, 2021. 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
1 · What the graph read from it

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 registry

The 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 literature

Who cites it

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Bing HeDepartment of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA.
Yao XiaoDepartment of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA.
Haodong LiangDepartment of Statistics, College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, USA.
Qianhui HuangDepartment of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA.
Yuheng DuDepartment of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA.
Yijun LiDepartment of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA.
David GarmireDepartment of Electrical Engineering and Computer Science, College of Engineering, University of Michigan, Ann Arbor, MI, USA.
Duxin SunDepartment of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI, USA.
Lana X GarmireDepartment of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA.
University of Michigan–Ann Arbor · US

Funding

An Integrative Omics Approach to Identify Biomarkers Related to Preeclampsia and Breast Cancer RisksR01HD084633 · NICHD · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GARMIRE, LANA X · 2016 to 2020
$3.0M
Cancer precision medicine through spatially informative single cell image and transcriptomics data analysisR01LM012373 · NLM · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GARMIRE, LANA X · 2016 to 2024
$2.4M
DR. EPS: Drug Repurposing for Extended Patient SurvivalR01LM012907 · NLM · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GARMIRE, LANA X · 2019 to 2022
$1.0M
An Integrative Bioinformatics Approach to Study Single Cancer Cell HeterogeneityK01ES025434 · NIEHS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GARMIRE, LANA X · 2014 to 2018
$905k
NICHD NIH HHS R01 HD084633NIEHS NIH HHS K01 ES025434NLM NIH HHS R01 LM012373NLM NIH HHS R01 LM012907
6 · The paper itself

Abstract

Intercellular heterogeneity is a major obstacle to successful precision medicine. Single-cell RNA sequencing (scRNA-seq) technology has enabled in-depth analysis of intercellular heterogeneity in various diseases. However, its full potential for precision medicine has yet to be reached. Towards this, we propose a new drug recommendation system called: A Single-cell Guided Pipeline to Aid Repurposing of Drugs (ASGARD). ASGARD defines a novel drug score predicting drugs by considering all cell clusters to address the intercellular heterogeneity within each patient. We tested ASGARD on multiple diseases, including breast cancer, acute lymphoblastic leukemia, and coronavirus disease 2019 (COVID-19). On single-drug therapy, ASGARD shows significantly better average accuracy (AUC of 0.92) compared to two other bulk-cell-based drug repurposing methods (AUC of 0.80 and 0.76). It is also considerably better (AUC of 0.82) than other cell cluster level predicting methods (AUC of 0.67 and 0.55). In addition, ASGARD is also validated by the drug response prediction method TRANSACT with Triple-Negative-Breast-Cancer patient samples. Many top-ranked drugs are either approved by FDA or in clinical trials treating corresponding diseases. In silico cell-type specific drop-out experiments using triple-negative breast cancers show the importance of T cells in the tumor microenvironment in affecting drug predictions. In conclusion, ASGARD is a promising drug repurposing recommendation tool guided by single-cell RNA-seq for personalized medicine. ASGARD is free for educational use at https://github.com/lanagarmire/ASGARD.

Identifiers

PMID34545335
PMCPMC8452105
OpenAlexW3200118734

What Socratic holds

Textmetadata
LicenceCC BY-NC-SA
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.