Evidence map›Paper›PMID 35161949›Full record

ReviewSensors (Basel, Switzerland)2022

A Review on Planted (

Satarupa Mohanty, Prasant Kumar Pattnaik, Ahmed Abdulhakim Al-Absi, Dae-Ki Kang

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2022. 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

4 authors.

Satarupa MohantySchool of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, India.
Prasant Kumar PattnaikSchool of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, India.ORCID 0000-0002-4566-8077
Ahmed Abdulhakim Al-AbsiDepartment of Smart Computing, Kyungdong University, 46 4-gil, Bongpo, Gosung 24764, Korea.ORCID 0000-0001-6272-7756
Dae-Ki KangDepartment of Computer & Information Engineering, Dongseo University, 47 Jurye-ro, Sasang-gu, Busan 47011, Korea.ORCID 0000-0002-4147-2835

Funding

National Research Foundation 2016R1D1A1B01011908
6 · The paper itself

Abstract

Personalized diagnosis of chronic disease requires capturing the continual pattern across the biological sequence. This repeating pattern in medical science is called "Motif". Motifs are the short, recurring patterns of biological sequences that are supposed signify some health disorder. They identify the binding sites for transcription factors that modulate and synchronize the gene expression. These motifs are important for the analysis and interpretation of various health issues like human disease, gene function, drug design, patient's conditions, etc. Searching for these patterns is an important step in unraveling the mechanisms of gene expression properly diagnose and treat chronic disease. Thus, motif identification has a vital role in healthcare studies and attracts many researchers. Numerous approaches have been characterized for the motif discovery process. This article attempts to review and analyze fifty-four of the most frequently found motif discovery processes/algorithms from different approaches and summarizes the discussion with their strengths and weaknesses.

Indexed as

AlgorithmsDNABinding SitesHumansSequence Analysis, DNATranscription FactorsDNATranscription Factorsevolutionary approachhashinglocal search approachmismatch treeprobabilistic approachsearch treesuffix treetries

Identifiers

PMID35161949
PMCPMC8838483

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.