Evidence map›Paper›PMID 24653643›Full record

ArticleCancer informatics2014

INsPeCT: INtegrative Platform for Cancer Transcriptomics.

Piyush B Madhamshettiwar, Stefan R Maetschke, Melissa J Davis, Antonio Reverter, Mark A Ragan

Abstract read
In one paragraph

Article in Cancer informatics, 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
–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

2 citing papers in PubMed.

  1. Article
  2. Review
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

5 authors.

Piyush B MadhamshettiwarThe University of Queensland, Institute for Molecular Bioscience, St. Lucia, Brisbane, Queensland, Australia. ; Australian Research Council Centre of Excellence in Bioinformatics, St. Lucia, Brisbane, Queensland, Australia.
Stefan R MaetschkeThe University of Queensland, Institute for Molecular Bioscience, St. Lucia, Brisbane, Queensland, Australia. ; Australian Research Council Centre of Excellence in Bioinformatics, St. Lucia, Brisbane, Queensland, Australia.
Melissa J DavisThe University of Queensland, Institute for Molecular Bioscience, St. Lucia, Brisbane, Queensland, Australia. ; Australian Research Council Centre of Excellence in Bioinformatics, St. Lucia, Brisbane, Queensland, Australia.
Antonio ReverterCSIRO Animal, Food and Health Sciences, St. Lucia, Brisbane, Queensland, Australia.
Mark A RaganThe University of Queensland, Institute for Molecular Bioscience, St. Lucia, Brisbane, Queensland, Australia. ; Australian Research Council Centre of Excellence in Bioinformatics, St. Lucia, Brisbane, Queensland, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The emergence of transcriptomics, fuelled by high-throughput sequencing technologies, has changed the nature of cancer research and resulted in a massive accumulation of data. Computational analysis, integration, and data visualization are now major bottlenecks in cancer biology and translational research. Although many tools have been brought to bear on these problems, their use remains unnecessarily restricted to computational biologists, as many tools require scripting skills, data infrastructure, and powerful computational facilities. New user-friendly, integrative, and automated analytical approaches are required to make computational methods more generally useful to the research community. Here we present INsPeCT (INtegrative Platform for Cancer Transcriptomics), which allows users with basic computer skills to perform comprehensive in-silico analyses of microarray, ChIP-seq, and RNA-seq data. INsPeCT supports the selection of interesting genes for advanced functional analysis. Included in its automated workflows are (i) a novel analytical framework, RMaNI (regulatory module network inference), which supports the inference of cancer subtype-specific transcriptional module networks and the analysis of modules; and (ii) WGCNA (weighted gene co-expression network analysis), which infers modules of highly correlated genes across microarray samples, associated with sample traits, eg survival time. INsPeCT is available free of cost from Bioinformatics Resource Australia-EMBL and can be accessed at http://inspect.braembl.org.au.

Indexed as

cancerChIP-seqmicroarrayRNA-seqsystems biologytranscriptional module networkstranscriptomics

Identifiers

PMID24653643
PMCPMC3956744

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.