Evidence map›Paper›PMID 27780226›Full record

ArticlePloS one2016

The Use of Gene Ontology Term and KEGG Pathway Enrichment for Analysis of Drug Half-Life.

Yu-Hang Zhang, Chen Chu, Shaopeng Wang, Lei Chen, Jing Lu, XiangYin Kong, Tao Huang, HaiPeng Li, Yu-Dong Cai

Open access · goldAbstract read
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Article in PloS one, 2016. 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
3.6field-weighted citation impact, top 7% of its field
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, 18 citations in OpenAlex.

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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

9 authors at 4 institutions in 1 country.

Yu-Hang ZhangSchool of Life Sciences, Shanghai University, Shanghai 200444, People's Republic of China.
Chen ChuInstitute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China.
Shaopeng WangSchool of Life Sciences, Shanghai University, Shanghai 200444, People's Republic of China.
Lei ChenCollege of Information Engineering, Shanghai Maritime University, Shanghai 201306, People's Republic of China.
Jing LuSchool of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai 264005, People's Republic of China.
XiangYin KongInstitute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China.
Tao HuangInstitute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China.
HaiPeng LiCAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China.
Yu-Dong CaiSchool of Life Sciences, Shanghai University, Shanghai 200444, People's Republic of China.ORCID http://orcid.org/0000-0001-5664-7979
Chinese Academy of Sciences · CNShanghai University · CNShanghai Maritime University · CNYantai University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A drug's biological half-life is defined as the time required for the human body to metabolize or eliminate 50% of the initial drug dosage. Correctly measuring the half-life of a given drug is helpful for the safe and accurate usage of the drug. In this study, we investigated which gene ontology (GO) terms and biological pathways were highly related to the determination of drug half-life. The investigated drugs, with known half-lives, were analyzed based on their enrichment scores for associated GO terms and KEGG pathways. These scores indicate which GO terms or KEGG pathways the drug targets. The feature selection method, minimum redundancy maximum relevance, was used to analyze these GO terms and KEGG pathways and to identify important GO terms and pathways, such as sodium-independent organic anion transmembrane transporter activity (GO:0015347), monoamine transmembrane transporter activity (GO:0008504), negative regulation of synaptic transmission (GO:0050805), neuroactive ligand-receptor interaction (hsa04080), serotonergic synapse (hsa04726), and linoleic acid metabolism (hsa00591), among others. This analysis confirmed our results and may show evidence for a new method in studying drug half-lives and building effective computational methods for the prediction of drug half-lives.

Indexed as

Computational BiologyGene OntologyHalf-LifeHumansLinoleic AcidMetabolic Networks and PathwaysOrganic Anion TransportersPharmaceutical PreparationsSynapsesSynaptic TransmissionVesicular Monoamine Transport ProteinsLinoleic AcidOrganic Anion TransportersPharmaceutical PreparationsVesicular Monoamine Transport Proteins

Identifiers

PMID27780226
PMCPMC5079577
OpenAlexW2532618909

What OpenQuestion holds

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LicenceCC BY
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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.