Evidence map›Paper›PMID 30547078›Full record

ArticleSmart health (Amsterdam, Netherlands)2018

Drug-Drug Interaction Discovery: Kernel Learning from Heterogeneous Similarities.

Devendra Singh Dhami, Gautam Kunapuli, Mayukh Das, David Page, Sriraam Natarajan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Smart health (Amsterdam, Netherlands), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Article
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

5 authors.

Devendra Singh DhamiErik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, United States.
Gautam KunapuliErik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, United States.
Mayukh DasErik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, United States.
David PageDepartment of Biostatistics and Medical Informatics, University of Wisconsin-Madison, United States.
Sriraam NatarajanErik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, United States.

Funding

Machine Learning for Identifying Adverse Drug EventsR01GM097618 · NIGMS · UNIVERSITY OF WISCONSIN-MADISON · PI CALDWELL, MICHAEL DEFOIX, HANSEN, RICHARD A. · 2011 to 2018
$3.8M
NIGMS NIH HHS R01 GM097618
6 · The paper itself

Abstract

We develop a pipeline to mine complex drug interactions by combining different similarities and interaction types (molecular, structural, phenotypic, genomic etc). Our goal is to learn an optimal kernel from these heterogeneous similarities in a supervised manner. We formulate an extensible framework that can easily integrate new interaction types into a rich model. The core of our pipeline features a novel kernel-learning approach that tunes the weights of the heterogeneous similarities, and fuses them into a Similarity-based Kernel for Identifying Drug-Drug interactions and Discovery, or SKID

Indexed as

Drug-Drug interactionsGraph queryKernel learningKnowledge graphRelational random walksSimilarity matrix

Identifiers

PMID30547078
PMCPMC6289266

What OpenQuestion holds

Textmetadata
LicenceTDM
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 OpenQuestion graph.