Evidence map›Paper›PMID 30855564›Full record

ArticleJournal of visualized experiments : JoVE2019

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications.

Dibakar Sigdel, Vincent Kyi, Aiden Zhang, Shaun P Setty, David A Liem, Yu Shi, Xuan Wang, Jiaming Shen, Wei Wang, JiaWei Han and 1 more

Abstract readVideo-Audio Media
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of visualized experiments : JoVE, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. The impact of the Social Exposome in Cardiovascular Health and Disease.Journal of precision medicine (Amsterdam, Netherlands) · 2025
    Article
  3. Article
  4. HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting.International ACM SIGIR Conference on Research and Development in Information Retrieval. Annual International ACMSIGIR Conference on Research & Development in Information Retrieval · 2023
    Article
  5. Review
  6. 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.

Dibakar SigdelThe NIH BD2K Center of Excellence in Biomedical Computing, University of California, Los Angeles; Department of Physiology, University of California, Los Angeles.
Vincent KyiThe NIH BD2K Center of Excellence in Biomedical Computing, University of California, Los Angeles; Department of Physiology, University of California, Los Angeles.
Aiden ZhangThe NIH BD2K Center of Excellence in Biomedical Computing, University of California, Los Angeles.
Shaun P SettyDepartment of Pediatric and Adult Congenital Heart Surgery, Miller Children's and Women's Hospital and Long Beach Memorial Hospital.
David A LiemThe NIH BD2K Center of Excellence in Biomedical Computing, University of California, Los Angeles; Department of Physiology, University of California, Los Angeles; Department of Medicine/Cardiology, University of California, Los Angeles.
Yu ShiNIH BD2K Program Centers of Excellence for Big Data Computing -- KnowEng Center, Department of Computer Science, University of Illinois at Urbana-Champaign (UIUC).
Xuan WangNIH BD2K Program Centers of Excellence for Big Data Computing -- KnowEng Center, Department of Computer Science, University of Illinois at Urbana-Champaign (UIUC).
Jiaming ShenNIH BD2K Program Centers of Excellence for Big Data Computing -- KnowEng Center, Department of Computer Science, University of Illinois at Urbana-Champaign (UIUC).
Wei WangThe NIH BD2K Center of Excellence in Biomedical Computing, University of California, Los Angeles; Scalable Analytics Institute (ScAi), University of California, Los Angeles; Department of Computer Science, University of California, Los Angeles.
JiaWei HanNIH BD2K Program Centers of Excellence for Big Data Computing -- KnowEng Center, Department of Computer Science, University of Illinois at Urbana-Champaign (UIUC).
Peipei PingThe NIH BD2K Center of Excellence in Biomedical Computing, University of California, Los Angeles; Department of Physiology, University of California, Los Angeles; Department of Medicine/Cardiology, University of California, Los Angeles; Scalable Analytics Institute (ScAi), University of California, Los Angeles; ppingucla@gmail.com.

Funding

TRAININGU54GM114833 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LINDSEY, MERRY L., PING, PEIPEI · 2014 to 2017
$13.8M
TRAININGU54GM114838 · NIGMS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI HAN, JIAWEI, SINHA, SAURABH · 2014 to 2017
$10.8M
Omics Phenotyping for Identifying Molecular Signatures of the Healthy and Failing Heart: An Integrated Data Science PlatformR35HL135772 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI PING, PEIPEI · 2017 to 2023
$6.4M
NHLBI NIH HHS R35 HL135772NIGMS NIH HHS U54 GM114833NIGMS NIH HHS U54 GM114838
6 · The paper itself

Abstract

The rapid accumulation of biomedical textual data has far exceeded the human capacity of manual curation and analysis, necessitating novel text-mining tools to extract biological insights from large volumes of scientific reports. The Context-aware Semantic Online Analytical Processing (CaseOLAP) pipeline, developed in 2016, successfully quantifies user-defined phrase-category relationships through the analysis of textual data. CaseOLAP has many biomedical applications. We have developed a protocol for a cloud-based environment supporting the end-to-end phrase-mining and analyses platform. Our protocol includes data preprocessing (e.g., downloading, extraction, and parsing text documents), indexing and searching with Elasticsearch, creating a functional document structure called Text-Cube, and quantifying phrase-category relationships using the core CaseOLAP algorithm. Our data preprocessing generates key-value mappings for all documents involved. The preprocessed data is indexed to carry out a search of documents including entities, which further facilitates the Text-Cube creation and CaseOLAP score calculation. The obtained raw CaseOLAP scores are interpreted using a series of integrative analyses, including dimensionality reduction, clustering, temporal, and geographical analyses. Additionally, the CaseOLAP scores are used to create a graphical database, which enables semantic mapping of the documents. CaseOLAP defines phrase-category relationships in an accurate (identifies relationships), consistent (highly reproducible), and efficient manner (processes 100,000 words/sec). Following this protocol, users can access a cloud-computing environment to support their own configurations and applications of CaseOLAP. This platform offers enhanced accessibility and empowers the biomedical community with phrase-mining tools for widespread biomedical research applications.

Indexed as

Biomedical ResearchCloud ComputingPublicationsAlgorithmsDatabases, FactualData MiningHumans

Identifiers

PMID30855564
PMCPMC7075490

What OpenQuestion holds

Textmetadata
LicenceTDM
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.