Evidence map›Paper›PMID 25559210›Full record

ArticleBMC bioinformatics2014

Advances in translational bioinformatics facilitate revealing the landscape of complex disease mechanisms.

Jack Y Yang, A Dunker, Jun S Liu, Xiang Qin, Hamid R Arabnia, William Yang, Andrzej Niemierko, Zhongxue Chen, Zuojie Luo, Liangjiang Wang and 5 more

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 34% 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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. 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

15 authors at 13 institutions in 2 countries.

Jack Y Yang
A Dunker
Jun S Liu
Xiang Qin
Hamid R Arabnia
William Yang
Andrzej Niemierko
Zhongxue Chen
Zuojie Luo
Liangjiang Wang
Yunlong Liu
Dong Xu
Youping Deng
Weida Tong
Mary Yang
Boston University · USUniversity of Missouri · USBaylor Genetics · USClemson University · USGuangxi Medical University · CNHarvard University · USIndiana University Bloomington · USIndiana University – Purdue University Indianapolis · USNational Center for Toxicological Research · USRush University Medical Center · USUniversity of Arkansas at Little Rock · USUniversity of Arkansas for Medical Sciences · USUniversity of Georgia · US

Funding

PHS HHS NIH/NHGRI 5U54HG003273-11PHS HHS NIH/NIGMS 5P20GM10342913
6 · The paper itself

Abstract

Advances of high-throughput technologies have rapidly produced more and more data from DNAs and RNAs to proteins, especially large volumes of genome-scale data. However, connection of the genomic information to cellular functions and biological behaviours relies on the development of effective approaches at higher systems level. In particular, advances in RNA-Seq technology has helped the studies of transcriptome, RNA expressed from the genome, while systems biology on the other hand provides more comprehensive pictures, from which genes and proteins actively interact to lead to cellular behaviours and physiological phenotypes. As biological interactions mediate many biological processes that are essential for cellular function or disease development, it is important to systematically identify genomic information including genetic mutations from GWAS (genome-wide association study), differentially expressed genes, bidirectional promoters, intrinsic disordered proteins (IDP) and protein interactions to gain deep insights into the underlying mechanisms of gene regulations and networks. Furthermore, bidirectional promoters can co-regulate many biological pathways, where the roles of bidirectional promoters can be studied systematically for identifying co-regulating genes at interactive network level. Combining information from different but related studies can ultimately help revealing the landscape of molecular mechanisms underlying complex diseases such as cancer.

Indexed as

Genome, HumanTranscriptomeTranslational Research, BiomedicalComputational BiologyGenomicsHumansNeoplasmsPhenotype

Identifiers

PMID25559210
PMCPMC4304187
OpenAlexW1758032857

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.