Evidence map›Paper›PMID 30940832›Full record

ArticleScientific reports2019

Meta-path Based Prioritization of Functional Drug Actions with Multi-Level Biological Networks.

Seyeol Yoon, Doheon Lee

Abstract read
In one paragraph

Article in Scientific reports, 2019. 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

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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.

No citing paper in PubMed yet.

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

2 authors.

Seyeol YoonDepartment of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, 34141, Daejeon, Republic of Korea.ORCID http://orcid.org/0000-0003-4665-6331
Doheon LeeDepartment of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, 34141, Daejeon, Republic of Korea. dhlee@kaist.ac.kr.ORCID http://orcid.org/0000-0001-9070-4316

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Functional drug actions refer to drug-affected GO terms. They aid in the investigation of drug effects that are therapeutic or adverse. Previous studies have utilized the linkage information between drugs and functions in molecular level biological networks. Since the current knowledge of molecular level mechanisms of biological functions is still limited, such previous studies were incomplete. We expected that the multi-level biological networks would allow us to more completely investigate the functional drug actions. We constructed multi-level biological networks with genes, GO terms, and diseases. Meta-paths were utilized to extract the features of each GO term. We trained 39 SVM models to prioritize the functional drug actions of the various 39 drugs. Through the multi-level networks, more functional drug actions were utilized for the 39 models and inferred by the models. Multi-level based features improved the performance of the models, and the average AUROC value in the cross-validation was 0.86. Moreover, 60% of the candidates were true.

Indexed as

Gene Regulatory NetworksAlgorithmsComputational BiologyDrug DevelopmentHumansMolecular Sequence AnnotationSupport Vector Machine

Identifiers

PMID30940832
PMCPMC6445150

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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.