Evidence map›Paper›PMID 35584271›Full record

ArticleJournal of computational biology : a journal of computational molecular cell biology2022

Locality-Sensitive Hashing-Based k-Mer Clustering for Identification of Differential Microbial Markers Related to Host Phenotype.

Wontack Han, Haixu Tang, Yuzhen Ye

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of computational biology : a journal of computational molecular cell biology, 2022. 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. Review
  2. 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.

Wontack HanComputer Science Department, Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana, USA.
Haixu TangComputer Science Department, Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana, USA.ORCID 0000-0001-8963-8155
Yuzhen YeComputer Science Department, Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana, USA.

Funding

Graph-centric approaches to metatranscriptomic and metaproteomic data analysisR01AI108888 · NIAID · TRUSTEES OF INDIANA UNIVERSITY · PI YE, YUZHEN · 2014 to 2017
$1.4M
Subtractive assembly approaches for inferring disease-associated microbial genes and pathways from microbiome sequencing dataR01AI143254 · NIAID · TRUSTEES OF INDIANA UNIVERSITY · PI YE, YUZHEN · 2019 to 2023
$1.1M
NIAID NIH HHS R01 AI108888NIAID NIH HHS R01 AI143254
6 · The paper itself

Abstract

Microbial organisms play important roles in many aspects of human health and diseases. Encouraged by the numerous studies that show the association between microbiomes and human diseases, computational and machine learning methods have been recently developed to generate and utilize microbiome features for prediction of host phenotypes such as disease versus healthy cancer immunotherapy responder versus nonresponder. We have previously developed a

Indexed as

MetagenomicsMicrobiotaCluster AnalysisMetagenomePhenotypecomparative analysisdifferential microbial markerslocality-sensitive hashing (LSH)microbiomesubtractive assembly

Identifiers

PMID35584271
PMCPMC9464365

What OpenQuestion holds

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LicenceCC BY-NC
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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.