Please use this identifier to cite or link to this item: http://repository.i3l.ac.id/jspui/handle/123456789/743
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTera, Tyniana Carissa-
dc.date.accessioned2023-05-02T03:35:54Z-
dc.date.available2023-05-02T03:35:54Z-
dc.date.issued2022-07-01-
dc.identifier.urihttp://repository.i3l.ac.id/jspui/handle/123456789/743-
dc.description.abstractRheumatoid Arthritis (RA) is a joint inflammatory autoimmune disease affecting 1% globally. Although not lethal, ~40% of RA patients are subjected to systemic manifestations and clinical complications of various involvements. Being without a cure, the ability to achieve remission of currently available treatments are dependent on immediate intervention. However, the complex nature of RA makes detection a highly personalized and timeconsuming process. Most attempts to unravel the genetic complexities of RA have adopted the genome wide association studies (GWAS) method. However, critics have questioned GWAS’ ability to identify true causal genes that aren’t carried by associations to correlated variants due to linkage disequilibrium. This study proposes a machine learning (ML) approach to identify a small subset of polymorphisms that can discriminate between RA patients and population control. 13 SNPs were identified to show remarkable predictive performances evident by the ability to achieve a consistent >0.9 on all performance metrics upon prediction using a 5-fold cross validation and 3 unseen test sets. This method was able to identify SNPs that were not previously found in associated to RA with various implications of functionality that can be exploreden_US
dc.language.isoenen_US
dc.publisherIndonesia International Institute for Life Sciencesen_US
dc.relation.ispartofseriesBI 22-003;T202206121-
dc.subjectRheumatoid Arthritisen_US
dc.subjectMachine Learningen_US
dc.subjectSupervised Learningen_US
dc.subjectSNPsen_US
dc.subjectPredictionen_US
dc.titleMachine Learning Approaches to Identify Genetic Predictors of Rheumatoid Arthritisen_US
dc.typeThesisen_US
Appears in Collections:Bioinformatics

Files in This Item:
File Description SizeFormat 
Abstract.pdfAbstract54.73 kBAdobe PDFView/Open
BI 22-003 Tyniana Carissa Tera.pdf
  Restricted Access
Full Text2.16 MBAdobe PDFView/Open Request a copy
Chapter 1.pdfChapter 187.74 kBAdobe PDFView/Open
Cover.pdfCover117.91 kBAdobe PDFView/Open
References.pdfReferences132.96 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.