Evidence map›Paper›PMID 30094994›Full record

ArticleJournal of proteome research2018

A Cloud-Based Metabolite and Chemical Prioritization System for the Biology/Disease-Driven Human Proteome Project.

Kun-Hsing Yu, Tsung-Lu Michael Lee, Yu-Ju Chen, Christopher Ré, Samuel C Kou, Jung-Hsien Chiang, Michael Snyder, Isaac S Kohane

Abstract read
In one paragraph

Article in Journal of proteome research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

8 authors.

Kun-Hsing YuDepartment of Biomedical Informatics , Harvard Medical School , Boston , Massachusetts 02115 , United States.ORCID 0000-0001-9892-8218
Tsung-Lu Michael LeeDepartment of Information Engineering , Kun Shan University , Tainan City 710 , Taiwan.
Yu-Ju ChenInstitute of Chemistry , Academia Sinica , Taipei City 115 , Taiwan.ORCID 0000-0002-3178-6697
Christopher RéDepartment of Computer Science , Stanford University , Stanford , California 94305 , United States.
Samuel C KouDepartment of Statistics , Harvard University , Cambridge , Massachusetts 02138 , United States.
Jung-Hsien ChiangDepartment of Computer Science and Information Engineering , National Cheng Kung University , Tainan City 701 , Taiwan.
Michael SnyderDepartment of Genetics, School of Medicine , Stanford University , Stanford , California 94305 , United States.
Isaac S KohaneDepartment of Biomedical Informatics , Harvard Medical School , Boston , Massachusetts 02115 , United States.

Funding

Stanford Islet Research CoreP30DK116074 · NIDDK · STANFORD UNIVERSITY · PI Seung K Kim · 2017 to 2026
$19.5M
Special EquipmentP50HG007735 · NHGRI · STANFORD UNIVERSITY · PI CHANG, HOWARD Y · 2014 to 2018
$15.2M
Proteome Characterization Center: A Genoproteomics Pipeline for Cance ResearchU24CA160036 · NCI · JOHNS HOPKINS UNIVERSITY · PI CHAN, DANIEL WANYUI, ZHANG, HUI · 2011 to 2016
$10.7M
NCI NIH HHS U24 CA160036NHGRI NIH HHS P50 HG007735NIDDK NIH HHS P30 DK116074
6 · The paper itself

Abstract

Targeted metabolomics and biochemical studies complement the ongoing investigations led by the Human Proteome Organization (HUPO) Biology/Disease-Driven Human Proteome Project (B/D-HPP). However, it is challenging to identify and prioritize metabolite and chemical targets. Literature-mining-based approaches have been proposed for target proteomics studies, but text mining methods for metabolite and chemical prioritization are hindered by a large number of synonyms and nonstandardized names of each entity. In this study, we developed a cloud-based literature mining and summarization platform that maps metabolites and chemicals in the literature to unique identifiers and summarizes the copublication trends of metabolites/chemicals and B/D-HPP topics using Protein Universal Reference Publication-Originated Search Engine (PURPOSE) scores. We successfully prioritized metabolites and chemicals associated with the B/D-HPP targeted fields and validated the results by checking against expert-curated associations and enrichment analyses. Compared with existing algorithms, our system achieved better precision and recall in retrieving chemicals related to B/D-HPP focused areas. Our cloud-based platform enables queries on all biological terms in multiple species, which will contribute to B/D-HPP and targeted metabolomics/chemical studies.

Indexed as

Cloud ComputingMetabolomicsProteomeAlgorithmsData MiningHumansSearch EngineProteomeBiology/Disease-Driven Human Proteome ProjectBiomedical Entity Search Tool (BEST)chemicalsFinding Associated Concepts with Text Analysis (FACTA+)literature miningmetabolomicsProtein Universal Reference Publication-Originated Search Engine (PURPOSE)

Identifiers

PMID30094994
PMCPMC6599684

What OpenQuestion holds

Textmetadata
LicenceTDM
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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.