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首页> 外文期刊>Carcinogenesis >A deep learning model based on sparse auto-encoder for prioritizing cancer-related genes and drug target combinations
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A deep learning model based on sparse auto-encoder for prioritizing cancer-related genes and drug target combinations

机译：基于稀疏自动编码器优先考虑癌症相关基因和药物目标组合的深度学习模型

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摘要

Prioritization of cancer-related genes from gene expression profiles and proteomic data is vital to improve the targeted therapies research. Although computational approaches have been complementing high-throughput biological experiments on the understanding of human diseases, it still remains a big challenge to accurately discover cancer-related proteins/genes via automatic learning from large-scale protein/gene expression data and protein-protein interaction data. Most of the existing methods are based on network construction combined with gene expression profiles, which ignore the diversity between normal samples and disease cell lines. In this study, we introduced a deep learning model based on a sparse auto-encoder to learn the specific characteristics of protein interactions in cancer cell lines integrated with protein expression data. The model showed learning ability to identify cancer-related proteins/genes from the input of different protein expression profiles by extracting the characteristics of protein interaction information, which could also predict cancer-related protein combinations. Comparing with other reported methods including differential expression and network-based methods, our model got the highest area under the curve value (>0.8) in predicting cancer-related genes. Our study prioritized similar to 500 high-confidence cancer-related genes; among these genes, 211 already known cancer drug targets were found, which supported the accuracy of our method. The above results indicated that the proposed auto-encoder model could computationally prioritize candidate proteins/genes involved in cancer and improve the targeted therapies research.
机译：来自基因表达谱和蛋白质组学数据的癌症相关基因的优先级对改善靶向治疗的研究至关重要。虽然计算方法已经补充了对人类疾病的理解的高通量生物实验，但仍然是通过从大规模蛋白质/基因表达数据和蛋白质 - 蛋白质相互作用的自动学习准确地发现癌症相关蛋白/基因的巨大挑战数据。大多数现有方法基于网络施工结合基因表达谱，这忽略了正常样品和疾病细胞系之间的多样性。在这项研究中，我们引入了基于稀疏自动编码器的深度学习模型，以了解与蛋白质表达数据集成的癌细胞系中蛋白质相互作用的特定特征。该模型通过提取蛋白质相互作用信息的特征，显示了从不同蛋白表达谱的输入中鉴定癌症相关蛋白/基因的学习能力，这也可以预测癌症相关的蛋白质组合。与其他报道的方法相比，包括差异表达和基于网络的方法，我们的模型在预测癌症相关基因时获得了曲线值（> 0.8）下的最高面积。我们的研究优先考虑了与500个高信任癌症相关基因;在这些基因中，发现了211种已知的癌症药物靶标，这支持了我们方法的准确性。上述结果表明，所提出的自动编码器模型可以计算癌症患者的候选蛋白/基因，改善靶向疗法研究。

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  • 来源
    《Carcinogenesis》 |2019年第5期|共9页
  • 作者

    Chang Ji-Wei; Ding Yuduan; Tahir ul Qamar Muhammad; Shen Yin; Gao Junxiang; Chen Ling-Ling;

  • 作者单位

    Huazhong Agr Univ Coll Informat Hubei Key Lab Agr Bioinformat Wuhan 430070 Hubei Peoples R;

    Huazhong Agr Univ Coll Informat Hubei Key Lab Agr Bioinformat Wuhan 430070 Hubei Peoples R;

    Huazhong Agr Univ Coll Informat Hubei Key Lab Agr Bioinformat Wuhan 430070 Hubei Peoples R;

    Huazhong Agr Univ Coll Informat Hubei Key Lab Agr Bioinformat Wuhan 430070 Hubei Peoples R;

    Huazhong Agr Univ Coll Informat Hubei Key Lab Agr Bioinformat Wuhan 430070 Hubei Peoples R;

    Huazhong Agr Univ Coll Informat Hubei Key Lab Agr Bioinformat Wuhan 430070 Hubei Peoples R;

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  • 正文语种 eng
  • 中图分类 肿瘤学;
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