Evidence map›Paper›PMID 27899558›Full record

ArticleNucleic acids research2017

pathDIP: an annotated resource for known and predicted human gene-pathway associations and pathway enrichment analysis.

Sara Rahmati, Mark Abovsky, Chiara Pastrello, Igor Jurisica

Abstract read
In one paragraph

Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
60citing papers in PubMed, 1 pooled it
–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

60 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  16. Molecular Mimicry Mapping inPathogens (Basel, Switzerland) · 2023
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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

4 authors.

Sara RahmatiDepartment of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada.
Mark AbovskyPrincess Margaret Cancer Centre, University Health Network, 101 College Street, TMDT, Room 11-314, Toronto, ON M5G 1L7, Canada.
Chiara PastrelloPrincess Margaret Cancer Centre, University Health Network, 101 College Street, TMDT, Room 11-314, Toronto, ON M5G 1L7, Canada.
Igor JurisicaDepartment of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada juris@ai.utoronto.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Molecular pathway data are essential in current computational and systems biology research. While there are many primary and integrated pathway databases, several challenges remain, including low proteome coverage (57%), low overlap across different databases, unavailability of direct information about underlying physical connectivity of pathway members, and high fraction of protein-coding genes without any pathway annotations, i.e. 'pathway orphans'. In order to address all these challenges, we developed pathDIP, which integrates data from 20 source pathway databases, 'core pathways', with physical protein-protein interactions to predict biologically relevant protein-pathway associations, referred to as 'extended pathways'. Cross-validation determined 71% recovery rate of our predictions. Data integration and predictions increase coverage of pathway annotations for protein-coding genes to 86%, and provide novel annotations for 5732 pathway orphans. PathDIP (http://ophid.utoronto.ca/pathdip) annotates 17 070 protein-coding genes with 4678 pathways, and provides multiple query, analysis and output options.

Indexed as

SoftwareComputational BiologyDatabases, GeneticGene Expression ProfilingGene Regulatory NetworksHumansProtein Interaction MappingSignal TransductionSystems Biology

Identifiers

PMID27899558
PMCPMC5210562

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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.