Cancer, a significant global public health issue, resulted in about 10 million deaths in 2022. Anticancer peptides (ACPs), as a category of bioactive peptides, have emerged as a focal point in clinical cancer research due to their potential to inhibit tumor cell proliferation with minimal side effects. However, the recognition of ACPs through wet-lab experiments still faces challenges of low efficiency and high cost. Our work proposes a recognition method for ACPs named ACP-DRL based on deep representation learning, to address the challenges associated with the recognition of ACPs in wet-lab experiments. ACP-DRL marks initial exploration of integrating protein language models into ACPs recognition, employing in-domain further pre-training to enhance the development of deep representation learning. Simultaneously, it employs bidirectional long short-term memory networks to extract amino acid features from sequences. Consequently, ACP-DRL eliminates constraints on sequence length and the dependence on manual features, showcasing remarkable competitiveness in comparison with existing methods.

中文翻译：

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ACP-DRL：一种基于深度表示学习的抗癌肽识别方法

癌症是全球重大公共卫生问题，2022年将导致约1000万人死亡。抗癌肽（ACP）作为一类生物活性肽，因其具有抑制肿瘤细胞增殖的潜力而成为临床癌症研究的焦点副作用最小。然而，通过湿实验室实验识别ACP仍面临低效率和高成本的挑战。我们的工作提出了一种基于深度表示学习的 ACP 识别方法，名为 ACP-DRL，以解决湿实验室实验中与 ACP 识别相关的挑战。 ACP-DRL标志着将蛋白质语言模型整合到ACP识别中的初步探索，采用域内进一步预训练来促进深度表示学习的发展。同时，它采用双向长短期记忆网络从序列中提取氨基酸特征。因此，ACP-DRL消除了对序列长度的限制以及对手动特征的依赖，与现有方法相比显示出显着的竞争力。