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Artificial Neural Network as a Classifier for the Identification of Hepatocellular Carcinoma Through Prognosticgene Signatures
Current Genomics ( IF 2.6 ) Pub Date : 2018-07-02 , DOI: 10.2174/1389202919666180215155234
Satya Eswari Jujjavarapu 1 , Saurabh Deshmukh 1
Affiliation  

Background: Artificial Neural Networks (ANNs) can be used to classify tumor of Hepatocellular carcinoma based on their gene expression signatures. The neural network is trained with gene expression profiles of genes that were predictive of recurrence in liver cancer, the ANNs became capable of correctly classifying all samples and distinguishing the genes most suitable for the organization. The ability of the trained ANN models in recognizing the Cancer Genes was tested as we analyzed additional samples that were not used beforehand for the training procedure, and got the correctly classified result in the validation set. Bootstrapping of training and analysis of dataset was made as external justification for more substantial result. Result: The best result achieved when the number of hidden layers was 10. The R2 value with training is 0.99136, R2 value obtained with testing is 0.80515, R2 value obtained after validation is 0.76678 and finally, with the total number of sets the R2 value is 0.93417. Performance was reported on the basis of graph plotted between Mean Squared Error (MSE) and 23 epoch. The value of gradient of the curve was 152 after 6 validation checks and 23 iterations. Conclusion: A successful attempt at developing a method for diagnostic classification of tumors from their gene-expression autographs that efficiently classify tumors and helps in decision making for providing appropriate treatment to the patients suffering from Hepatocellular carcinoma has been carried out.

中文翻译:

人工神经网络作为通过预后基因特征识别肝细胞癌的分类器

背景:人工神经网络 (ANNs) 可用于根据肝细胞癌的基因表达特征对肿瘤进行分类。神经网络使用预测肝癌复发的基因的基因表达谱进行训练,人工神经网络能够正确分类所有样本并区分最适合组织的基因。当我们分析了之前未用于训练过程的额外样本时,测试了训练的 ANN 模型识别癌症基因的能力,并在验证集中获得了正确分类的结果。数据集的训练和分析的自举被作为获得更实质性结果的外部理由。结果:隐藏层数为 10 时取得的最佳结果。训练后的 R2 值为 0.99136,测试得到的 R2 值为 0.80515,验证后得到的 R2 值为 0.76678,最后,总组数的 R2 值为 0.93417。基于均方误差 (MSE) 和 23 时期之间绘制的图表报告了性能。经过 6 次验证检查和 23 次迭代后,曲线的梯度值为 152。结论:成功地尝试开发一种根据基因表达亲笔诊断肿瘤分类的方法,该方法可以有效地对肿瘤进行分类,并有助于为肝细胞癌患者提供适当的治疗做出决策。基于均方误差 (MSE) 和 23 时期之间绘制的图表报告了性能。经过 6 次验证检查和 23 次迭代后,曲线的梯度值为 152。结论:成功地尝试开发一种根据基因表达亲笔诊断肿瘤分类的方法,该方法可以有效地对肿瘤进行分类,并有助于为肝细胞癌患者提供适当的治疗做出决策。基于均方误差 (MSE) 和 23 时期之间绘制的图表报告了性能。经过 6 次验证检查和 23 次迭代后,曲线的梯度值为 152。结论:成功地尝试开发一种根据基因表达亲笔诊断肿瘤分类的方法,该方法可以有效地对肿瘤进行分类,并有助于为肝细胞癌患者提供适当的治疗做出决策。
更新日期:2018-07-02
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