Evidence map›Paper›PMID 18842678›Full record

ArticleBriefings in bioinformatics2009

Bringing Web 2.0 to bioinformatics.

Zhang Zhang, Kei-Hoi Cheung, Jeffrey P Townsend

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Booly: a new data integration platform.BMC bioinformatics · 2010
    Article
  10. The filamentous fungal gene expression database (FFGED).Fungal genetics and biology : FG & B · 2010
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. 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

3 authors.

Zhang ZhangDepartment of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut 06520, USA.
Kei-Hoi Cheung
Jeffrey P Townsend

Funding

Tools for Functional Analysis and Cell BiologyP01GM068087 · NIGMS · DARTMOUTH COLLEGE · PI DUNLAP, JAY C. · 2004 to 2013
$19.3M
SENSELAB: INTEGRATION OF MULTIDISCIPLINARY SENSORY DATAP01DC004732 · NIDCD · YALE UNIVERSITY · PI SHEPHERD, GORDON · 2000 to 2008
$10.2M
Yale Microarray Center for Research the Nervous SystemU24NS051869 · NINDS · YALE UNIVERSITY · PI MANE, SHRIKANT M · 2005 to 2009
$6.9M
NIDCD NIH HHS P01 DC04732NIGMS NIH HHS GM068087NIGMS NIH HHS P01 GM068087NINDS NIH HHS U24 NS051869
6 · The paper itself

Abstract

Enabling deft data integration from numerous, voluminous and heterogeneous data sources is a major bioinformatic challenge. Several approaches have been proposed to address this challenge, including data warehousing and federated databasing. Yet despite the rise of these approaches, integration of data from multiple sources remains problematic and toilsome. These two approaches follow a user-to-computer communication model for data exchange, and do not facilitate a broader concept of data sharing or collaboration among users. In this report, we discuss the potential of Web 2.0 technologies to transcend this model and enhance bioinformatics research. We propose a Web 2.0-based Scientific Social Community (SSC) model for the implementation of these technologies. By establishing a social, collective and collaborative platform for data creation, sharing and integration, we promote a web services-based pipeline featuring web services for computer-to-computer data exchange as users add value. This pipeline aims to simplify data integration and creation, to realize automatic analysis, and to facilitate reuse and sharing of data. SSC can foster collaboration and harness collective intelligence to create and discover new knowledge. In addition to its research potential, we also describe its potential role as an e-learning platform in education. We discuss lessons from information technology, predict the next generation of Web (Web 3.0), and describe its potential impact on the future of bioinformatics studies.

Indexed as

Computational BiologyInformation DisseminationComputer-Assisted InstructionCooperative BehaviorDatabase Management SystemsDatabases, FactualHumansInternetResearchUser-Computer Interface

Identifiers

PMID18842678
PMCPMC2638627

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.