Evidence map›Paper›PMID 29875994›Full record

ArticleOncotarget2018

Computational modeling of methionine cycle-based metabolism and DNA methylation and the implications for anti-cancer drug response prediction.

Mengying Zhang, Christian Saad, Lien Le, Kathrin Halfter, Bernhard Bauer, Ulrich R Mansmann, Jian Li

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Oncotarget, 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
0.4field-weighted citation impact, top 41% 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, 7 citations in OpenAlex.

  1. Article
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  5. AutoAnalyze in Systems Biology.Bioinformatics and biology insights · 2019
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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

7 authors at 3 institutions in 1 country.

Mengying ZhangInstitute for Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians University of München, Munich, Germany.
Christian SaadDepartment of Computational Science, University of Augsburg, Augsburg, Germany.
Lien LeInstitute for Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians University of München, Munich, Germany.
Kathrin HalfterInstitute for Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians University of München, Munich, Germany.
Bernhard BauerDepartment of Computational Science, University of Augsburg, Augsburg, Germany.
Ulrich R MansmannInstitute for Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians University of München, Munich, Germany.
Jian LiInstitute for Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians University of München, Munich, Germany.
Ludwig-Maximilians-Universität München · DEUniversity of Augsburg · DEDeutschen Konsortium für Translationale Krebsforschung · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The relationship between metabolism and methylation is considered to be an important aspect of cancer development and drug efficacy. However, it remains poorly defined how to apply this aspect to improve preclinical disease characterization and clinical treatment outcome. Using available molecular information from Kyoto Encyclopedia of Genes and Genomes (KEGG) and literature, we constructed a large-scale knowledge-based metabolic

Indexed as

chemotherapymetabolismmethylationmolecular modellingtreatment prediction

Identifiers

PMID29875994
PMCPMC5989406
OpenAlexW2790420249

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.