School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, 40132, Indonesia; Department of Informatics Engineering, Universal University, Batam, Indonesia.
School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, 40132, Indonesia.
Comput Biol Med. 2023 Sep;163:107241. doi: 10.1016/j.compbiomed.2023.107241. Epub 2023 Jul 8.
Predicting DNA-binding proteins (DBPs) based solely on primary sequences is one of the most challenging problems in genome annotation. DBPs play a crucial role in various biological processes, including DNA replication, transcription, repair, and splicing. Some DBPs are essential in pharmaceutical research on various human cancers and autoimmune diseases. Existing experimental methods for identifying DBPs are time-consuming and costly. Therefore, developing a rapid and accurate computational technique is necessary to address the issue. This study introduces BiCaps-DBP, a deep learning-based method that improves DBP prediction performance by combining bidirectional long short-term memory with a 1D-capsule network. This study uses three training and independent datasets to evaluate the proposed model's generalizability and robustness. Based on three independent datasets, BiCaps-DBP achieved 1.05%, 5.79% and 0.40% higher accuracies than an existing predictor for PDB2272, PDB186 and PDB20000, respectively. These outcomes indicate that the proposed method is a promising DBP predictor.
仅基于一级序列预测 DNA 结合蛋白（DBP）是基因组注释中最具挑战性的问题之一。DBP 在各种生物过程中发挥着关键作用，包括 DNA 复制、转录、修复和剪接。一些 DBP 在各种人类癌症和自身免疫性疾病的药物研究中是必不可少的。现有的用于鉴定 DBP 的实验方法既耗时又昂贵。因此，开发一种快速而准确的计算技术是必要的，以解决这个问题。本研究介绍了 BiCaps-DBP，这是一种基于深度学习的方法，通过将双向长短期记忆与一维胶囊网络相结合，提高了 DBP 预测性能。本研究使用三个训练和独立数据集来评估所提出模型的泛化能力和稳健性。基于三个独立数据集，BiCaps-DBP 在 PDB2272、PDB186 和 PDB20000 上的准确率分别比现有预测器高出 1.05%、5.79%和 0.40%。这些结果表明，该方法是一种很有前途的 DBP 预测器。
