Evidence map›Paper›PMID 25379168›Full record

ArticleJournal of clinical bioinformatics2014

Semi-automated literature mining to identify putative biomarkers of disease from multiple biofluids.

Rick Jordan, Shyam Visweswaran, Vanathi Gopalakrishnan

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of clinical bioinformatics, 2014. 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
0.4field-weighted citation impact, top 35% of its field
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, 6 citations in OpenAlex.

  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

3 authors at 1 institution in 1 country.

Rick JordanDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
Shyam VisweswaranDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA ; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA ; Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
Vanathi GopalakrishnanDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA ; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA ; Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
University of Pittsburgh · US

Funding

Vitamin D3 Modulation of Inflammation and Lung Cancer RiskP50CA090440 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI DACIC, SANJA · 2001 to 2015
$34.6M
Transfer Rule Learning with Functional Mapping for Integrative Modeling of Panomics DataR01GM100387 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI GOPALAKRISHNAN, VANATHI · 2012 to 2018
$1.8M
Bayesian Rule Learning Methods for Disease Prediction and Biomarker DiscoveryR01LM010950 · NLM · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI GOPALAKRISHNAN, VANATHI · 2011 to 2013
$1.1M
NCI NIH HHS P50 CA090440NIGMS NIH HHS R01 GM100387NLM NIH HHS R01 LM010950
6 · The paper itself

Abstract

backgroundComputational methods for mining of biomedical literature can be useful in augmenting manual searches of the literature using keywords for disease-specific biomarker discovery from biofluids. In this work, we develop and apply a semi-automated literature mining method to mine abstracts obtained from PubMed to discover putative biomarkers of breast and lung cancers in specific biofluids. METHODOLOGY: A positive set of abstracts was defined by the terms 'breast cancer' and 'lung cancer' in conjunction with 14 separate 'biofluids' (bile, blood, breastmilk, cerebrospinal fluid, mucus, plasma, saliva, semen, serum, synovial fluid, stool, sweat, tears, and urine), while a negative set of abstracts was defined by the terms '(biofluid) NOT breast cancer' or '(biofluid) NOT lung cancer.' More than 5.3 million total abstracts were obtained from PubMed and examined for biomarker-disease-biofluid associations (34,296 positive and 2,653,396 negative for breast cancer; 28,355 positive and 2,595,034 negative for lung cancer). Biological entities such as genes and proteins were tagged using ABNER, and processed using Python scripts to produce a list of putative biomarkers. Z-scores were calculated, ranked, and used to determine significance of putative biomarkers found. Manual verification of relevant abstracts was performed to assess our method's performance.

resultsBiofluid-specific markers were identified from the literature, assigned relevance scores based on frequency of occurrence, and validated using known biomarker lists and/or databases for lung and breast cancer [NCBI's On-line Mendelian Inheritance in Man (OMIM), Cancer Gene annotation server for cancer genomics (CAGE), NCBI's Genes & Disease, NCI's Early Detection Research Network (EDRN), and others]. The specificity of each marker for a given biofluid was calculated, and the performance of our semi-automated literature mining method assessed for breast and lung cancer.

conclusionsWe developed a semi-automated process for determining a list of putative biomarkers for breast and lung cancer. New knowledge is presented in the form of biomarker lists; ranked, newly discovered biomarker-disease-biofluid relationships; and biomarker specificity across biofluids.

Indexed as

BiofluidBiomarkerBreast cancerLiterature miningLung cancerText mining

Identifiers

PMID25379168
PMCPMC4215335
OpenAlexW2130892224

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.