Evidence map›Paper›PMID 30547447›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2019

A Machine-Learning-Based Drug Repurposing Approach Using Baseline Regularization.

Zhaobin Kuang, Yujia Bao, James Thomson, Michael Caldwell, Peggy Peissig, Ron Stewart, Rebecca Willett, David Page

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Review
  2. The use of real-world data in drug repurposing.Translational and clinical pharmacology · 2021
    Review
  3. Article
  4. Review
  5. Article
  6. Adverse Drug Reaction Discovery from Electronic Health Records with Deep Neural Networks.Proceedings of the ACM Conference on Health, Inference, and Learning · 2020
    Article
  7. Review
  8. Review
  9. Artificial Intelligence for Drug Toxicity and Safety.Trends in pharmacological sciences · 2019
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Zhaobin KuangThe University of Wisconsin, Madison, WI, USA. zkuang@wisc.edu.
Yujia BaoThe Massachusetts Institute of Technology, Cambridge, MA, USA.
James ThomsonThe Morgridge Institute for Research, Madison, WI, USA.
Michael CaldwellThe Marshfield Clinic, Marshfield, WI, USA.
Peggy PeissigThe Marshfield Clinic, Marshfield, WI, USA.
Ron StewartThe Morgridge Institute for Research, Madison, WI, USA.
Rebecca WillettThe University of Wisconsin, Madison, WI, USA.
David PageThe University of Wisconsin, Madison, WI, USA.

Funding

University of Wisconsin Institute for Clinical and Translational ResearchUL1TR002373 · NCATS · UNIVERSITY OF WISCONSIN-MADISON · PI ELIZABETH S BURNSIDE, Allan R. Brasier · 2017 to 2026
$75.9M
TrainingU54AI117924 · NIAID · UNIVERSITY OF WISCONSIN-MADISON · PI CRAVEN, MARK W. · 2014 to 2018
$11.3M
Machine Learning for Identifying Adverse Drug EventsR01GM097618 · NIGMS · UNIVERSITY OF WISCONSIN-MADISON · PI CALDWELL, MICHAEL DEFOIX, HANSEN, RICHARD A. · 2011 to 2018
$3.8M
NCATS NIH HHS UL1 TR002373NIAID NIH HHS U54 AI117924NIGMS NIH HHS R01 GM097618
6 · The paper itself

Abstract

We present the baseline regularization model for computational drug repurposing using electronic health records (EHRs). In EHRs, drug prescriptions of various drugs are recorded throughout time for various patients. In the same time, numeric physical measurements (e.g., fasting blood glucose level) are also recorded. Baseline regularization uses statistical relationships between the occurrences of prescriptions of some particular drugs and the increase or the decrease in the values of some particular numeric physical measurements to identify potential repurposing opportunities.

Indexed as

Machine LearningAlgorithmsComputational BiologyDrug RepositioningElectronic Health RecordsHumansComputational drug repurposingElectronic health recordsLongitudinal dataSelf-controlled case seriesSilico repurposing

Identifiers

PMID30547447
PMCPMC6296259

What OpenQuestion holds

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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 OpenQuestion graph.