Parameter settings for deep learning and machine learning models used in this study.

<p>Parameter settings for deep learning and machine learning models used in this study.</p>

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Rui-Si Hu (5337227) (author)
مؤلفون آخرون: Kui Gu (12335571) (author), Muhammad Ehsan (2561170) (author), Sayed Haidar Abbas Raza (8452353) (author), Chun-Ren Wang (128559) (author)
منشور في: 2025
الموضوعات:
الوسوم: إضافة وسم
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_version_ 1852020915243581440
author Rui-Si Hu (5337227)
author2 Kui Gu (12335571)
Muhammad Ehsan (2561170)
Sayed Haidar Abbas Raza (8452353)
Chun-Ren Wang (128559)
author2_role author
author
author
author
author_facet Rui-Si Hu (5337227)
Kui Gu (12335571)
Muhammad Ehsan (2561170)
Sayed Haidar Abbas Raza (8452353)
Chun-Ren Wang (128559)
author_role author
dc.creator.none.fl_str_mv Rui-Si Hu (5337227)
Kui Gu (12335571)
Muhammad Ehsan (2561170)
Sayed Haidar Abbas Raza (8452353)
Chun-Ren Wang (128559)
dc.date.none.fl_str_mv 2025-04-29T18:14:39Z
dc.identifier.none.fl_str_mv 10.1371/journal.pntd.0012985.s004
dc.relation.none.fl_str_mv https://figshare.com/articles/dataset/Parameter_settings_for_deep_learning_and_machine_learning_models_used_in_this_study_/28898031
dc.rights.none.fl_str_mv CC BY 4.0
info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Biochemistry
Microbiology
Biotechnology
Immunology
Infectious Diseases
Virology
Space Science
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
therapeutic antibody development
superior predictive power
seven synthetic peptides
offer efficient solutions
fasciola hepatica </
blot immunoassays confirmed
12 handcrafted features
predict linear bces
enhance prediction accuracy
cell epitope prediction
based vaccine design
xlink "> deepbce
xlink ">
cell epitopes
based vaccines
valuable tool
structural complexity
specific binding
proteomic data
present deepbce
positive sera
peptide sequences
parasitic pathogens
machine learning
leucine aminopeptidase
independent testing
igg reactivity
high cost
fold cross
diagnostic applications
deep learning
concept study
comparative analyses
case study
attention mechanism
artificial intelligence
art self
approximately 81
dc.title.none.fl_str_mv Parameter settings for deep learning and machine learning models used in this study.
dc.type.none.fl_str_mv Dataset
info:eu-repo/semantics/publishedVersion
dataset
description <p>Parameter settings for deep learning and machine learning models used in this study.</p>
eu_rights_str_mv openAccess
id Manara_12ae33b0e910ea2faebacdadfa8108a3
identifier_str_mv 10.1371/journal.pntd.0012985.s004
network_acronym_str Manara
network_name_str ManaraRepo
oai_identifier_str oai:figshare.com:article/28898031
publishDate 2025
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
rights_invalid_str_mv CC BY 4.0
spelling Parameter settings for deep learning and machine learning models used in this study.Rui-Si Hu (5337227)Kui Gu (12335571)Muhammad Ehsan (2561170)Sayed Haidar Abbas Raza (8452353)Chun-Ren Wang (128559)BiochemistryMicrobiologyBiotechnologyImmunologyInfectious DiseasesVirologySpace ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedtherapeutic antibody developmentsuperior predictive powerseven synthetic peptidesoffer efficient solutionsfasciola hepatica </blot immunoassays confirmed12 handcrafted featurespredict linear bcesenhance prediction accuracycell epitope predictionbased vaccine designxlink "> deepbcexlink ">cell epitopesbased vaccinesvaluable toolstructural complexityspecific bindingproteomic datapresent deepbcepositive serapeptide sequencesparasitic pathogensmachine learningleucine aminopeptidaseindependent testingigg reactivityhigh costfold crossdiagnostic applicationsdeep learningconcept studycomparative analysescase studyattention mechanismartificial intelligenceart selfapproximately 81<p>Parameter settings for deep learning and machine learning models used in this study.</p>2025-04-29T18:14:39ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pntd.0012985.s004https://figshare.com/articles/dataset/Parameter_settings_for_deep_learning_and_machine_learning_models_used_in_this_study_/28898031CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/288980312025-04-29T18:14:39Z
spellingShingle Parameter settings for deep learning and machine learning models used in this study.
Rui-Si Hu (5337227)
Biochemistry
Microbiology
Biotechnology
Immunology
Infectious Diseases
Virology
Space Science
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
therapeutic antibody development
superior predictive power
seven synthetic peptides
offer efficient solutions
fasciola hepatica </
blot immunoassays confirmed
12 handcrafted features
predict linear bces
enhance prediction accuracy
cell epitope prediction
based vaccine design
xlink "> deepbce
xlink ">
cell epitopes
based vaccines
valuable tool
structural complexity
specific binding
proteomic data
present deepbce
positive sera
peptide sequences
parasitic pathogens
machine learning
leucine aminopeptidase
independent testing
igg reactivity
high cost
fold cross
diagnostic applications
deep learning
concept study
comparative analyses
case study
attention mechanism
artificial intelligence
art self
approximately 81
status_str publishedVersion
title Parameter settings for deep learning and machine learning models used in this study.
title_full Parameter settings for deep learning and machine learning models used in this study.
title_fullStr Parameter settings for deep learning and machine learning models used in this study.
title_full_unstemmed Parameter settings for deep learning and machine learning models used in this study.
title_short Parameter settings for deep learning and machine learning models used in this study.
title_sort Parameter settings for deep learning and machine learning models used in this study.
topic Biochemistry
Microbiology
Biotechnology
Immunology
Infectious Diseases
Virology
Space Science
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
therapeutic antibody development
superior predictive power
seven synthetic peptides
offer efficient solutions
fasciola hepatica </
blot immunoassays confirmed
12 handcrafted features
predict linear bces
enhance prediction accuracy
cell epitope prediction
based vaccine design
xlink "> deepbce
xlink ">
cell epitopes
based vaccines
valuable tool
structural complexity
specific binding
proteomic data
present deepbce
positive sera
peptide sequences
parasitic pathogens
machine learning
leucine aminopeptidase
independent testing
igg reactivity
high cost
fold cross
diagnostic applications
deep learning
concept study
comparative analyses
case study
attention mechanism
artificial intelligence
art self
approximately 81