Evidence map›Paper›PMID 31251324›Full record

ArticleGigaScience2019

Scalable biclustering - the future of big data exploration?

Patryk Orzechowski, Krzysztof Boryczko, Jason H Moore

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in GigaScience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Biclustering data analysis: a comprehensive survey.Briefings in bioinformatics · 2024
    Review
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.

Patryk OrzechowskiInstitute for Biomedical Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104, USA.
Krzysztof BoryczkoDepartment of Computer Science, AGH University of Science and Technology, al. A. Mickiewicza 30, Kraków 30-059, Poland.
Jason H MooreInstitute for Biomedical Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104, USA.

Funding

Biomedical Computing and Informatics Strategies for Precision MedicineR01LM012601 · NLM · UNIVERSITY OF PENNSYLVANIA · PI HUANG, XIUZHEN, MOORE, JASON H. · 2017 to 2020
$1.8M
NLM NIH HHS R01 LM012601
6 · The paper itself

Abstract

Biclustering is a technique of discovering local similarities within data. For many years the complexity of the methods and parallelization issues limited its application to big data problems. With the development of novel scalable methods, biclustering has finally started to close this gap. In this paper we discuss the caveats of biclustering and present its current challenges and guidelines for practitioners. We also try to explain why biclustering may soon become one of the standards for big data analytics.

Indexed as

Big DataCluster AnalysisData MiningGenome, HumanGenomicsHumansSequence AlignmentSequence Analysis, DNASoftwarebiclusteringbig databiomarker detectionco-clusteringdata miningdisease subtype identificationgene-drug interactionparallel algorithmsprecision medicine

Identifiers

PMID31251324
PMCPMC6598466

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.