Evidence map›Paper›PMID 30936678›Full record

ArticleEvolutionary bioinformatics online2019

Vector Quantized Spectral Clustering Applied to Whole Genome Sequences of Plants.

Aditya A Shastri, Kapil Ahuja, Milind B Ratnaparkhe, Aditya Shah, Aishwary Gagrani, Anant Lal

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Evolutionary bioinformatics online, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Aditya A ShastriComputer Science and Engineering, Indian Institute of Technology Indore, Indore, India.
Kapil AhujaComputer Science and Engineering, Indian Institute of Technology Indore, Indore, India.ORCID https://orcid.org/0000-0001-9640-4437
Milind B RatnaparkheICAR-Indian Institute of Soybean Research, Indore, India.
Aditya ShahComputer Science and Engineering, Indian Institute of Technology Indore, Indore, India.
Aishwary GagraniComputer Science and Engineering, Indian Institute of Technology Indore, Indore, India.
Anant LalComputer Science and Engineering, Indian Institute of Technology Indore, Indore, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We develop a Vector Quantized Spectral Clustering (VQSC) algorithm that is a combination of spectral clustering (SC) and vector quantization (VQ) sampling for grouping genome sequences of plants. The inspiration here is to use SC for its accuracy and VQ to make the algorithm computationally cheap (the complexity of SC is cubic in terms of the input size). Although the combination of SC and VQ is not new, the novelty of our work is in developing the crucial similarity matrix in SC as well as use of

Indexed as

alignment scorek-medoidssamplingsimilarity matrixsingle nucleotide polymorphism (SNP)spectral clusteringvector quantizationwhole genome sequence (WGS)

Identifiers

PMID30936678
PMCPMC6435876

What OpenQuestion holds

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LicenceCC BY-NC
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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.