The AUC-ROC curve of 5-fold cross-validation of the training dataset (A) and the confusion matrix entries for the training, validation, and test datasets (B).

<p>The AUC-ROC curve of 5-fold cross-validation of the training dataset (A) and the confusion matrix entries for the training, validation, and test datasets (B).</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Muhammad Yasir (3555896) (author)
مؤلفون آخرون:	Jinyoung Park (134860) (author), Eun-Taek Han (620126) (author), Jin-Hee Han (1314552) (author), Won Sun Park (14905755) (author), Mubashir Hassan (614591) (author), Andrzej Kloczkowski (612861) (author), Wanjoo Chun (6752006) (author)
منشور في:	2024
الموضوعات:	Biochemistry Cell Biology Pharmacology Biotechnology Cancer Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified tnf -&# 945 active soluble form 7 cell confirmed predictive model based learning model followed graphconvmol model within molecular dynamics simulation extracted molecular features reference tace inhibitor key tace residues deep learning models tace inhibitory potential potential tace inhibitor decoy compounds specific trained model repurposing potential molecular docking tace ). specific targets integrated deep xlink "> using rdkit using bms therapeutic intervention subsequently used rheumatoid arthritis raw 264 promising target inflammatory response inflammatory mediators increasing utilization holds promise highly efficient e datasets e database drug repositioning deepchem framework crucial role converting pro converting enzyme computational results cheminformatics toolkit biological evaluation approved drugs approved drug also known
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author	Muhammad Yasir (3555896)
author2	Jinyoung Park (134860) Eun-Taek Han (620126) Jin-Hee Han (1314552) Won Sun Park (14905755) Mubashir Hassan (614591) Andrzej Kloczkowski (612861) Wanjoo Chun (6752006)
author2_role	author author author author author author author
author_facet	Muhammad Yasir (3555896) Jinyoung Park (134860) Eun-Taek Han (620126) Jin-Hee Han (1314552) Won Sun Park (14905755) Mubashir Hassan (614591) Andrzej Kloczkowski (612861) Wanjoo Chun (6752006)
author_role	author
dc.creator.none.fl_str_mv	Muhammad Yasir (3555896) Jinyoung Park (134860) Eun-Taek Han (620126) Jin-Hee Han (1314552) Won Sun Park (14905755) Mubashir Hassan (614591) Andrzej Kloczkowski (612861) Wanjoo Chun (6752006)
dc.date.none.fl_str_mv	2024-12-27T18:23:54Z
dc.identifier.none.fl_str_mv	10.1371/journal.pone.0315245.g004
dc.relation.none.fl_str_mv	https://figshare.com/articles/figure/The_AUC-ROC_curve_of_5-fold_cross-validation_of_the_training_dataset_A_and_the_confusion_matrix_entries_for_the_training_validation_and_test_datasets_B_/28100759
dc.rights.none.fl_str_mv	CC BY 4.0 info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv	Biochemistry Cell Biology Pharmacology Biotechnology Cancer Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified tnf -&# 945 active soluble form 7 cell confirmed predictive model based learning model followed graphconvmol model within molecular dynamics simulation extracted molecular features reference tace inhibitor key tace residues deep learning models tace inhibitory potential potential tace inhibitor decoy compounds specific trained model repurposing potential molecular docking tace ). specific targets integrated deep xlink "> using rdkit using bms therapeutic intervention subsequently used rheumatoid arthritis raw 264 promising target inflammatory response inflammatory mediators increasing utilization holds promise highly efficient e datasets e database drug repositioning deepchem framework crucial role converting pro converting enzyme computational results cheminformatics toolkit biological evaluation approved drugs approved drug also known
dc.title.none.fl_str_mv	The AUC-ROC curve of 5-fold cross-validation of the training dataset (A) and the confusion matrix entries for the training, validation, and test datasets (B).
dc.type.none.fl_str_mv	Image Figure info:eu-repo/semantics/publishedVersion image
description	<p>The AUC-ROC curve of 5-fold cross-validation of the training dataset (A) and the confusion matrix entries for the training, validation, and test datasets (B).</p>
eu_rights_str_mv	openAccess
id	Manara_5463da2f70b81e81ce00a52b9077fb64
identifier_str_mv	10.1371/journal.pone.0315245.g004
network_acronym_str	Manara
network_name_str	ManaraRepo
oai_identifier_str	oai:figshare.com:article/28100759
publishDate	2024
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
rights_invalid_str_mv	CC BY 4.0
spelling	The AUC-ROC curve of 5-fold cross-validation of the training dataset (A) and the confusion matrix entries for the training, validation, and test datasets (B).Muhammad Yasir (3555896)Jinyoung Park (134860)Eun-Taek Han (620126)Jin-Hee Han (1314552)Won Sun Park (14905755)Mubashir Hassan (614591)Andrzej Kloczkowski (612861)Wanjoo Chun (6752006)BiochemistryCell BiologyPharmacologyBiotechnologyCancerBiological Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedtnf -&# 945active soluble form7 cell confirmedpredictive model basedlearning model followedgraphconvmol model withinmolecular dynamics simulationextracted molecular featuresreference tace inhibitorkey tace residuesdeep learning modelstace inhibitory potentialpotential tace inhibitordecoy compounds specifictrained modelrepurposing potentialmolecular dockingtace ).specific targetsintegrated deepxlink ">using rdkitusing bmstherapeutic interventionsubsequently usedrheumatoid arthritisraw 264promising targetinflammatory responseinflammatory mediatorsincreasing utilizationholds promisehighly efficiente datasetse databasedrug repositioningdeepchem frameworkcrucial roleconverting proconverting enzymecomputational resultscheminformatics toolkitbiological evaluationapproved drugsapproved drugalso known<p>The AUC-ROC curve of 5-fold cross-validation of the training dataset (A) and the confusion matrix entries for the training, validation, and test datasets (B).</p>2024-12-27T18:23:54ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0315245.g004https://figshare.com/articles/figure/The_AUC-ROC_curve_of_5-fold_cross-validation_of_the_training_dataset_A_and_the_confusion_matrix_entries_for_the_training_validation_and_test_datasets_B_/28100759CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/281007592024-12-27T18:23:54Z
spellingShingle	The AUC-ROC curve of 5-fold cross-validation of the training dataset (A) and the confusion matrix entries for the training, validation, and test datasets (B). Muhammad Yasir (3555896) Biochemistry Cell Biology Pharmacology Biotechnology Cancer Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified tnf -&# 945 active soluble form 7 cell confirmed predictive model based learning model followed graphconvmol model within molecular dynamics simulation extracted molecular features reference tace inhibitor key tace residues deep learning models tace inhibitory potential potential tace inhibitor decoy compounds specific trained model repurposing potential molecular docking tace ). specific targets integrated deep xlink "> using rdkit using bms therapeutic intervention subsequently used rheumatoid arthritis raw 264 promising target inflammatory response inflammatory mediators increasing utilization holds promise highly efficient e datasets e database drug repositioning deepchem framework crucial role converting pro converting enzyme computational results cheminformatics toolkit biological evaluation approved drugs approved drug also known
status_str	publishedVersion
title	The AUC-ROC curve of 5-fold cross-validation of the training dataset (A) and the confusion matrix entries for the training, validation, and test datasets (B).
title_full	The AUC-ROC curve of 5-fold cross-validation of the training dataset (A) and the confusion matrix entries for the training, validation, and test datasets (B).
title_fullStr	The AUC-ROC curve of 5-fold cross-validation of the training dataset (A) and the confusion matrix entries for the training, validation, and test datasets (B).
title_full_unstemmed	The AUC-ROC curve of 5-fold cross-validation of the training dataset (A) and the confusion matrix entries for the training, validation, and test datasets (B).
title_short	The AUC-ROC curve of 5-fold cross-validation of the training dataset (A) and the confusion matrix entries for the training, validation, and test datasets (B).
title_sort	The AUC-ROC curve of 5-fold cross-validation of the training dataset (A) and the confusion matrix entries for the training, validation, and test datasets (B).
topic	Biochemistry Cell Biology Pharmacology Biotechnology Cancer Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified tnf -&# 945 active soluble form 7 cell confirmed predictive model based learning model followed graphconvmol model within molecular dynamics simulation extracted molecular features reference tace inhibitor key tace residues deep learning models tace inhibitory potential potential tace inhibitor decoy compounds specific trained model repurposing potential molecular docking tace ). specific targets integrated deep xlink "> using rdkit using bms therapeutic intervention subsequently used rheumatoid arthritis raw 264 promising target inflammatory response inflammatory mediators increasing utilization holds promise highly efficient e datasets e database drug repositioning deepchem framework crucial role converting pro converting enzyme computational results cheminformatics toolkit biological evaluation approved drugs approved drug also known

The AUC-ROC curve of 5-fold cross-validation of the training dataset (A) and the confusion matrix entries for the training, validation, and test datasets (B).

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