Evidence map›Paper›PMID 33904580›Full record

ArticleBioinformatics (Oxford, England)2021

COBRAC: a fast implementation of convex biclustering with compression.

Haidong Yi, Le Huang, Gal Mishne, Eric C Chi

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2021. 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. Review
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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.

Haidong YiDepartment of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.ORCID 0000-0002-2591-1922
Le HuangDepartment of Genetics, Curriculum in Bioinformatics & Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Gal MishneHalıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA 92093, USA.
Eric C ChiDepartment of Statistics, North Carolina State University, Raleigh, NC 27607, USA.

Funding

Data-driven analysis for neuronal dynamic modelingR01EB026936 · NIBIB · YALE UNIVERSITY · PI MISHNE, GAL · 2018 to 2020
$1.1M
Imputing Single Cell Rna Sequencing Data: Mathematical, Statistical And Computational ChallengesR01GM135928 · NIGMS · RICE UNIVERSITY · PI CHI, ERIC C · 2019 to 2021
$696k
NIBIB NIH HHS R01 EB026936NIGMS NIH HHS R01 GM135928NIH HHS R01EB026936
6 · The paper itself

Abstract

summaryBiclustering is a generalization of clustering used to identify simultaneous grouping patterns in observations (rows) and features (columns) of a data matrix. Recently, the biclustering task has been formulated as a convex optimization problem. While this convex recasting of the problem has attractive properties, existing algorithms do not scale well. To address this problem and make convex biclustering a practical tool for analyzing larger data, we propose an implementation of fast convex biclustering called COBRAC to reduce the computing time by iteratively compressing problem size along with the solution path. We apply COBRAC to several gene expression datasets to demonstrate its effectiveness and efficiency. Besides the standalone version for COBRAC, we also developed a related online web server for online calculation and visualization of the downloadable interactive results. AVAILABILITY AND IMPLEMENTATION: The source code and test data are available at https://github.com/haidyi/cvxbiclustr or https://zenodo.org/record/4620218. The web server is available at https://cvxbiclustr.ericchi.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identifiers

PMID33904580
PMCPMC8545294

What OpenQuestion holds

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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.