Metaheuristic algorithms for solving DNA-related problems.

<p>Metaheuristic algorithms for solving DNA-related problems.</p>

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Eslam Hamouda (5172083) (author)
مؤلفون آخرون: Mayada Tarek (5172080) (author)
منشور في: 2024
الموضوعات:
الوسوم: إضافة وسم
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_version_ 1852026443552260096
author Eslam Hamouda (5172083)
author2 Mayada Tarek (5172080)
author2_role author
author_facet Eslam Hamouda (5172083)
Mayada Tarek (5172080)
author_role author
dc.creator.none.fl_str_mv Eslam Hamouda (5172083)
Mayada Tarek (5172080)
dc.date.none.fl_str_mv 2024-09-23T17:31:41Z
dc.identifier.none.fl_str_mv 10.1371/journal.pone.0310698.t002
dc.relation.none.fl_str_mv https://figshare.com/articles/dataset/Metaheuristic_algorithms_for_solving_DNA-related_problems_/27090637
dc.rights.none.fl_str_mv CC BY 4.0
info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Medicine
Genetics
Space Science
Biological Sciences not elsewhere classified
Mathematical Sciences not elsewhere classified
Information Systems not elsewhere classified
universal rule guides
grey wolf optimizer
vast feature space
first layer applies
exploration ability allows
statlog dna dataset
given dna sequence
reliable feature selection
div >< p
proposed method divides
predicting junction type
classification model trained
feature selection
second layer
method using
junction type
generalization ability
ensemble model
proposed approach
various partitions
using cross
thus leading
thorough examination
test sets
study presents
splicing regulation
selected features
rna splicing
retrieved features
remaining held
proposed gwo
implementation code
hybrid approach
gene structure
finding suggests
ensemble learning
efficiently search
disease causes
computational biology
comprehensively evaluating
classifier ’
96 %.
dc.title.none.fl_str_mv Metaheuristic algorithms for solving DNA-related problems.
dc.type.none.fl_str_mv Dataset
info:eu-repo/semantics/publishedVersion
dataset
description <p>Metaheuristic algorithms for solving DNA-related problems.</p>
eu_rights_str_mv openAccess
id Manara_7ec707fcff128ae8e2a99a5d2a3d9ccf
identifier_str_mv 10.1371/journal.pone.0310698.t002
network_acronym_str Manara
network_name_str ManaraRepo
oai_identifier_str oai:figshare.com:article/27090637
publishDate 2024
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
rights_invalid_str_mv CC BY 4.0
spelling Metaheuristic algorithms for solving DNA-related problems.Eslam Hamouda (5172083)Mayada Tarek (5172080)MedicineGeneticsSpace ScienceBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifieduniversal rule guidesgrey wolf optimizervast feature spacefirst layer appliesexploration ability allowsstatlog dna datasetgiven dna sequencereliable feature selectiondiv >< pproposed method dividespredicting junction typeclassification model trainedfeature selectionsecond layermethod usingjunction typegeneralization abilityensemble modelproposed approachvarious partitionsusing crossthus leadingthorough examinationtest setsstudy presentssplicing regulationselected featuresrna splicingretrieved featuresremaining heldproposed gwoimplementation codehybrid approachgene structurefinding suggestsensemble learningefficiently searchdisease causescomputational biologycomprehensively evaluatingclassifier ’96 %.<p>Metaheuristic algorithms for solving DNA-related problems.</p>2024-09-23T17:31:41ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0310698.t002https://figshare.com/articles/dataset/Metaheuristic_algorithms_for_solving_DNA-related_problems_/27090637CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/270906372024-09-23T17:31:41Z
spellingShingle Metaheuristic algorithms for solving DNA-related problems.
Eslam Hamouda (5172083)
Medicine
Genetics
Space Science
Biological Sciences not elsewhere classified
Mathematical Sciences not elsewhere classified
Information Systems not elsewhere classified
universal rule guides
grey wolf optimizer
vast feature space
first layer applies
exploration ability allows
statlog dna dataset
given dna sequence
reliable feature selection
div >< p
proposed method divides
predicting junction type
classification model trained
feature selection
second layer
method using
junction type
generalization ability
ensemble model
proposed approach
various partitions
using cross
thus leading
thorough examination
test sets
study presents
splicing regulation
selected features
rna splicing
retrieved features
remaining held
proposed gwo
implementation code
hybrid approach
gene structure
finding suggests
ensemble learning
efficiently search
disease causes
computational biology
comprehensively evaluating
classifier ’
96 %.
status_str publishedVersion
title Metaheuristic algorithms for solving DNA-related problems.
title_full Metaheuristic algorithms for solving DNA-related problems.
title_fullStr Metaheuristic algorithms for solving DNA-related problems.
title_full_unstemmed Metaheuristic algorithms for solving DNA-related problems.
title_short Metaheuristic algorithms for solving DNA-related problems.
title_sort Metaheuristic algorithms for solving DNA-related problems.
topic Medicine
Genetics
Space Science
Biological Sciences not elsewhere classified
Mathematical Sciences not elsewhere classified
Information Systems not elsewhere classified
universal rule guides
grey wolf optimizer
vast feature space
first layer applies
exploration ability allows
statlog dna dataset
given dna sequence
reliable feature selection
div >< p
proposed method divides
predicting junction type
classification model trained
feature selection
second layer
method using
junction type
generalization ability
ensemble model
proposed approach
various partitions
using cross
thus leading
thorough examination
test sets
study presents
splicing regulation
selected features
rna splicing
retrieved features
remaining held
proposed gwo
implementation code
hybrid approach
gene structure
finding suggests
ensemble learning
efficiently search
disease causes
computational biology
comprehensively evaluating
classifier ’
96 %.