Evidence map›Paper›PMID 20711346›Full record

ArticlePloS one2010

A genome-wide gene function prediction resource for Drosophila melanogaster.

Han Yan, Kavitha Venkatesan, John E Beaver, Niels Klitgord, Muhammed A Yildirim, Tong Hao, David E Hill, Michael E Cusick, Norbert Perrimon, Frederick P Roth and 1 more

Abstract read
In one paragraph

Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. NewG3 (Bethesda, Md.) · 2019
    Article
  2. deepNF: deep network fusion for protein function prediction.Bioinformatics (Oxford, England) · 2018
    Article
  3. Machine Learning Analysis IdentifiesG3 (Bethesda, Md.) · 2017
    Article
  4. Article
  5. Implications of Big Data for cell biology.Molecular biology of the cell · 2015
    Article
  6. Inferring gene ontologies from pairwise similarity data.Bioinformatics (Oxford, England) · 2014
    Article
  7. Matrix factorization-based data fusion for gene function prediction in baker's yeast and slime mold.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2014
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. 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

11 authors.

Han YanDepartment of Cancer Biology, Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America.
Kavitha Venkatesan
John E Beaver
Niels Klitgord
Muhammed A Yildirim
Tong Hao
David E Hill
Michael E Cusick
Norbert Perrimon
Frederick P Roth
Marc Vidal

Funding

Genomic Analysis of Network Perturbations in Human DiseaseP50HG004233 · NHGRI · DANA-FARBER CANCER INST · PI VIDAL, MARC · 2007 to 2017
$31.6M
The C. elegans Interactome ProjectR01HG001715 · NHGRI · MASSACHUSETTS GENERAL HOSPITAL · PI HILL, DAVID E., ROTH, FREDERICK P · 1998 to 2010
$16.5M
Mapping the first half of the REFERENCE human binary protein interactomeU01HG001715 · NHGRI · DANA-FARBER CANCER INST · PI HILL, DAVID E., ROTH, FREDERICK P · 2012 to 2014
$5.4M
Resource ProjectU41HG001715 · NHGRI · DANA-FARBER CANCER INST · PI HILL, DAVID E. · 2015 to 2017
$4.7M
Network analysis of Signal TransductionR01DK088718 · NIDDK · HARVARD MEDICAL SCHOOL · PI PERRIMON, NORBERT · 2010 to 2014
$2.5M
Computational Methods for Directed Functional GenomicsR01HG003224 · NHGRI · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI ROTH, FREDERICK P · 2005 to 2010
$2.2M
Howard Hughes Medical InstituteNHGRI NIH HHS HG001715NHGRI NIH HHS HG003224NHGRI NIH HHS HG004233NHGRI NIH HHS P50 HG004233NHGRI NIH HHS R01 HG001715NHGRI NIH HHS R01 HG003224NHGRI NIH HHS U01 HG001715NHGRI NIH HHS U41 HG001715NIDDK NIH HHS R01 DK088718
6 · The paper itself

Abstract

Predicting gene functions by integrating large-scale biological data remains a challenge for systems biology. Here we present a resource for Drosophila melanogaster gene function predictions. We trained function-specific classifiers to optimize the influence of different biological datasets for each functional category. Our model predicted GO terms and KEGG pathway memberships for Drosophila melanogaster genes with high accuracy, as affirmed by cross-validation, supporting literature evidence, and large-scale RNAi screens. The resulting resource of prioritized associations between Drosophila genes and their potential functions offers a guide for experimental investigations.

Indexed as

AnimalsDatabases, GeneticDrosophila melanogasterGenes, InsectGenomicsReproducibility of ResultsRNA Interference

Identifiers

PMID20711346
PMCPMC2920829

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.