Evidence map›Paper›PMID 15215440›Full record

ArticleNucleic acids research2004

LOCnet and LOCtarget: sub-cellular localization for structural genomics targets.

Rajesh Nair, Burkhard Rost

Abstract read
In one paragraph

Article in Nucleic acids research, 2004. 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. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. The continuing conundrum of the LEA proteins.Die Naturwissenschaften · 2007
    Review
  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

2 authors.

Rajesh NairCUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street BB217, New York, NY 10032, USA. nair@maple.bioc.columbia.edu
Burkhard Rost

Funding

Predictions of structure/function by PredictProteinR01LM007329 · NLM · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI PE'ER, ITSHACK G · 2003 to 2010
$2.6M
NLM NIH HHS R01 LM007329
6 · The paper itself

Abstract

LOCtarget is a web server and database that predicts and annotates sub-cellular localization for structural genomics targets; LOCnet is one of the methods used in LOCtarget that can predict sub-cellular localization for all eukaryotic and prokaryotic proteins. Targets are taken from the central registration database for structural genomics, namely, TargetDB. LOCtarget predicts localization through a combination of four different methods: known nuclear localization signals (PredictNLS), homology-based transfer of experimental annotations (LOChom), inference through automatic text analysis of SWISS-PROT keywords (LOCkey) and de novo prediction through a system of neural networks (LOCnet). Additionally, we report predictions from SignalP. The final prediction is based on the method with the highest confidence. The web server can be used to predict sub-cellular localization of proteins from their amino acid sequence. The LOCtarget database currently contains localization predictions for all eukaryotic proteins from TargetDB and is updated every week. The server is available at http://www.rostlab.org/services/LOCtarget/.

Indexed as

GenomicsSoftwareDatabases, GeneticEukaryotic CellsInternetNeural Networks, ComputerNuclear Localization SignalsProkaryotic CellsProteinsProtein Sorting SignalsSequence Analysis, ProteinSequence Homology, Amino AcidUser-Computer InterfaceNuclear Localization SignalsProteinsProtein Sorting Signals

Identifiers

PMID15215440
PMCPMC441579

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.