Evidence map›Paper›PMID 35049823›Full record

ArticleAnimals : an open access journal from MDPI2022

A Modified Memetic Algorithm with an Application to Gene Selection in a Sheep Body Weight Study.

Maoxuan Miao, Jinran Wu, Fengjing Cai, You-Gan Wang

Abstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Maoxuan MiaoCollege of Mathematics and Physics, Wenzhou University, Wenzhou 325035, China.
Jinran WuSchool of Mathematical Sciences, Queensland University of Technology, Brisbane 4001, Australia.ORCID 0000-0002-2388-3614
Fengjing CaiCollege of Mathematics and Physics, Wenzhou University, Wenzhou 325035, China.
You-Gan WangSchool of Mathematical Sciences, Queensland University of Technology, Brisbane 4001, Australia.ORCID 0000-0003-0901-4671

Funding

the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) CE140100049the Australian Research Council Discovery Project DP160104292Zhejiang Provincial Natural Science Foundation of China LY19A010054
6 · The paper itself

Abstract

Selecting the minimal best subset out of a huge number of factors for influencing the response is a fundamental and very challenging NP-hard problem because the presence of many redundant genes results in over-fitting easily while missing an important gene can more detrimental impact on predictions, and computation is prohibitive for exhaust search. We propose a modified memetic algorithm (MA) based on an improved splicing method to overcome the problems in the traditional genetic algorithm exploitation capability and dimension reduction in the predictor variables. The new algorithm accelerates the search in identifying the minimal best subset of genes by incorporating it into the new local search operator and hence improving the splicing method. The improvement is also due to another two novel aspects: (a) updating subsets of genes iteratively until the no more reduction in the loss function by splicing and increasing the probability of selecting the true subsets of genes; and (b) introducing

Indexed as

gene selectionlocal search operatormemetic algorithmmodificationssheep weight

Identifiers

PMID35049823
PMCPMC8772977

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.