Thesis

Machine Learning Approaches to Identify Genetic Predictors of Rheumatoid Arthritis.

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Rheumatoid 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 explored.

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Availability

Detail Information

Series Title

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Call Number

BI 22-003

Publisher

i3L, Jakarta : i3L, Jakarta., 2022

Collation

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Language

English

ISBN/ISSN

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Classification

NONE

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Machine learning
Rheumatoid Arthritis
Supervised Learning
SNPs
Prediction

Specific Detail Info

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Statement of Responsibility

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