TY  - GEN
CY  - UNIVERSITAS LAMPUNG
ID  - eprints77446
UR  - http://digilib.unila.ac.id/77446/
A1  - Ardella Dean, Awalia
Y1  - 2023/11/23/
N2  - Crotonylation merupakan salah satu jenis modifikasi pascatranslasi (PTM) berupa
penambahan gugus asil pada asam amino lisin. Modifikasi ini memiliki kemampuan
dalam mengatur ekspresi gen dan ditemukan terlibat pada beberapa penyakit,
seperti depresi, ginjal kronis, hingga kanker. Identifikasi situs PTM menjadi hal
krusial mengingat perannya dalam siklus sel. Metode machine learning untuk
klasifikasi situs PTM dapat digunakan sebagai alternatif dalam mengenali situs
PTM, namun membutuhkan data yang relatif banyak agar dapat memberikan hasil
yang andal. Penelitian ini dilakukan menggunakan metode klasifikasi LightGBM
dan teknik oversampling ADASYN untuk menangani ketersediaan data protein
crotonylation yang cukup sedikit. Data yang digunakan diperoleh dari situs UniProt
terdiri dari 159 data positif dan 847 data negatif. Ekstraksi fitur menggunakan
binary encoding, position weight amino acid, encoding based on grouped weight,
k-nearest neighbors, dan pseudo-position specific scoring matrix menghasilkan 833
fitur. 5-fold cross-validation digunakan pada proses training untuk mencari
kombinasi hyperparameter terbaik. Hasil penelitian menunjukkan bahwa
pembagian data menggunakan 90% data sebagai data latih dan 10% data sebagai
data uji memberikan hasil tertinggi dengan nilai accuracy sebesar 96,04%,
sensitivity sebesar 87,50%, specificity sebesar 97,65%, MCC sebesar 85,15%, dan
AUC sebesar 98,90%.
Kata kunci: modifikasi pascatranslasi, crotonylation, ADASYN, LightGBM

Crotonylation is a type of post-translational modification (PTM). It is an addition
of acyl group to the lysine residues. This modification has the ability to regulate
gene expression and has been found to be involved in several diseases, such as
depression, chronic kidney disease, and cancer. Identification of PTM sites is
crucial considering their role in the cell cycle. Machine learning methods for
classifying PTM sites can be used as an alternative for recognizing PTM sites, but
they require relatively large amounts of data to provide reliable results. This
research was carried out using the LightGBM classification method and the
ADASYN oversampling technique to handle the limited availability of crotonylated
protein sequences. The data was obtained from the UniProt website consisting of
159 positive data (crotonylated) and 847 negative data (noncrotonylated). Feature
extraction by using binary encoding, position weight amino acid, encoding based
on grouped weight, k-nearest neighbors, and pseudo-position specific scoring
matrix produced 833 features. 5-fold cross validation was used in the training
process to find best hyperparameter combinations. The results showed that the
highest result was obtained by using 90% of the data as training data with the
application of ADASYN oversampling (n_neighbors=9) and 10% of the data as test
data with 96,04% accuracy, 87,50% sensitivity, 97.65% specificity, 85,15% MCC,
and 98,90% AUC.
Keywords: post-translational modification, crotonylation, ADASYN, LightGBM
PB  - FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM
TI  - KLASIFIKASI IMBALANCED DATA UNTUK POST-TRANSLATIONAL
MODIFICATION (PTM) PADA SEQUENCES PROTEIN LISIN
MENGGUNAKAN ADAPTIVE SYNTHETIC (ADASYN) SAMPLING
DENGAN METODE LIGHT GRADIENT BOOSTING MACHINE
(LightGBM)
AV  - restricted
ER  -