Evidence map›Paper›PMID 26306243›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2015

Drug Normalization for Cancer Therapeutic and Druggable Genome Target Discovery.

Guoqian Jiang, Sunghwan Sohn, Michael T Zimmermann, Chen Wang, Hongfang Liu, Christopher G Chute

Abstract read
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Guoqian JiangDepartment of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.
Sunghwan SohnDepartment of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.
Michael T ZimmermannDepartment of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.
Chen WangDepartment of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.
Hongfang LiuDepartment of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.
Christopher G ChuteDepartment of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heterogeneous drug data representation among different druggable genome knowledge resources and datasets delays effective cancer therapeutic target discovery within the broad scientific community. The objective of the present paper is to describe the challenges and lessons learned from our efforts in developing and evaluating a standards-based drug normalization framework targeting cancer druggable genome datasets. Our findings suggested that mechanisms need to be established to deal with spelling errors and irregularities in normalizing clinical drug data in The Cancer Genome Atlas (TCGA), whereas the annotations from NCI Thesaurus (NCIt) and PubChem are two layers of normalization that potentially bridge between the clinical phenotypes and the druggable genome knowledge for effective cancer therapeutic target discovery.

Identifiers

PMID26306243
PMCPMC4525232

What OpenQuestion holds

Textmetadata
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.