Thesis

MITNet: A Fusion Transformer and Convolutional Neural Network Architecture Approach for T-Cell Epitope Prediction

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"Classifying epitopes is essential since they can be applied in various fields, including therapeutics,
diagnostics, and peptide-based vaccines. To determine the epitope or peptide against an antibody,
epitope mapping with peptides is the most extensively used method. However, this method is more
time-consuming and inefficient than using present methods. The ability to retrieve data on protein
sequences through laboratory procedures has led to the development of computational models that
predict epitope binding based on machine learning and deep learning. It has also evolved to become a
crucial part of developing effective cancer immunotherapies. This paper proposes an architecture to
generalize this case since various research strives to solve a low-performance classification problem.
A proposed deep learning model is the fusion architecture which combines two architectures:
Transformer architecture and Convolutional Neural Network (CNN), called MITNet and MITNet-Fusion.
Combining these two architectures enriches feature space to correlate epitope labels with the binary
classification method. The selected epitope-TCR interactions are GILG, GLCT, and NLVP, acquired from
three databases: IEDB, VDJdb, and McPAS-TCR. The previous input data was extracted using Amino
Acid Composition (AAC), Dipeptide Composition (DIP), Spectrum Descriptor, and the combination of
all those features called AADIP composition to encode the input data to deep learning architecture.
For ensuring consistency, five cross-validations were performed using the Area Under Curve (AUC)
metric. Results showed that GILG, GLCT, and NLVP received scores of 0.81, 0.69, and 0.73, respectively.
Those results were compared to prior architecture and outperformed four deep learning models. "

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Availability

Detail Information

Series Title

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Call Number

BI 23-005

Publisher

i3L, Jakarta : i3L, Jakarta., 2023

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)

Deep Learning
Epitope Classification
Fusion Architecture
Transformer

Specific Detail Info

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Statement of Responsibility

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