Millions of people are suffering from cancers, but accurate early diagnosis and effective treatment are still tough for all doctors. Common ways against cancer include surgical operation, radiotherapy and chemotherapy. However, they are all very harmful for patients. Recently, the anticancer peptides (ACPs) have been discovered to be a potential way to treat cancer. Since ACPs are natural biologics, they are safer than other methods. However, the experimental technology is an expensive way to find ACPs so we purpose a new machine learning method to identify the ACPs. Firstly, we extracted the feature of ACPs in two aspects: sequence and chemical characteristics of amino acids. For sequence, average 20 amino acids composition was extracted. For chemical characteristics, we classified amino acids into six groups based on the patterns of hydrophobic and hydrophilic residues. Then, deep belief network has been used to encode the features of ACPs. Finally, we purposed Random Relevance Vector Machines to identify the true ACPs. We call this method ‘DRACP’ and tested the performance of it on two independent datasets. Its AUC and AUPR are higher than 0.9 in both datasets. We developed a novel method named ‘DRACP’ and compared it with some traditional methods. The cross-validation results showed its effectiveness in identifying ACPs.

中文翻译：

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DRACP：一种鉴定抗癌肽的新方法

数以百万计的人正罹患癌症，但是对于所有医生而言，准确的早期诊断和有效的治疗仍然很困难。对抗癌症的常见方法包括外科手术，放射疗法和化学疗法。但是，它们对患者都是非常有害的。最近，已发现抗癌肽（ACP）是治疗癌症的潜在方法。由于ACP是天然生物制剂，因此它们比其他方法更安全。但是，实验技术是查找ACP的昂贵方法，因此我们打算采用一种新的机器学习方法来识别ACP。首先，我们从氨基酸的序列和化学特征两个方面提取了ACP的特征。对于序列，平均提取20个氨基酸组成。对于化学特性，我们根据疏水和亲水残基的模式将氨基酸分为六类。然后，深度信任网络已被用来编码ACP的特征。最后，我们打算使用随机相关向量机来识别真正的ACP。我们将此方法称为“ DRACP”，并在两个独立的数据集上测试了其性能。在两个数据集中，其AUC和AUPR均高于0.9。我们开发了一种名为“ DRACP”的新颖方法，并将其与一些传统方法进行了比较。交叉验证结果显示了其在识别ACP方面的​​有效性。在两个数据集中，其AUC和AUPR均高于0.9。我们开发了一种名为“ DRACP”的新颖方法，并将其与一些传统方法进行了比较。交叉验证结果显示了其在识别ACP方面的​​有效性。在两个数据集中，其AUC和AUPR均高于0.9。我们开发了一种名为“ DRACP”的新颖方法，并将其与一些传统方法进行了比较。交叉验证结果显示了其在识别ACP方面的​​有效性。