신동관 (국립암센터)

한빛사논문

조회320

국립암센터

CV File 160 kb

Brief. Bioinform., Mar 27 2024, 25 (3) bbae220 | https://doi.org/10.1093/bib/bbae220 Contrastive learning for enhancing feature extraction in anticancer peptides

Byungjo Lee ¹, Dongkwan Shin ^1,2

¹Research Institute, National Cancer Center, 323, Ilsan-ro, Ilsandong-gu, Goyang, 10408, Republic of Korea.

²Department of Cancer Biomedical Science, National Cancer Center Graduate School of Cancer Science and Policy, 323, Ilsan-ro, Ilsandong-gu, Goyang, 10408, Republic of Korea.

Corresponding author: Dongkwan Shin

Abstract

Cancer, recognized as a primary cause of death worldwide, has profound health implications and incurs a substantial social burden. Numerous efforts have been made to develop cancer treatments, among which anticancer peptides (ACPs) are garnering recognition for their potential applications. While ACP screening is time-consuming and costly, in silico prediction tools provide a way to overcome these challenges. Herein, we present a deep learning model designed to screen ACPs using peptide sequences only. A contrastive learning technique was applied to enhance model performance, yielding better results than a model trained solely on binary classification loss. Furthermore, two independent encoders were employed as a replacement for data augmentation, a technique commonly used in contrastive learning. Our model achieved superior performance on five of six benchmark datasets against previous state-of-the-art models. As prediction tools advance, the potential in peptide-based cancer therapeutics increases, promising a brighter future for oncology research and patient care.

논문정보

형식Research article
게재일2024년 03월 (BRIC 등록일 2024-05-13)
연구진국내 연구진
분야 생명과학 > 노화/암생물학

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