The long non-coding RNAs (lncRNAs) are subject of intensive recent studies due to its association with various human diseases. It is desirable to build the artificial intelligence-based models for prediction of diseases or tissues based on the lncRNAs data, which will be useful in disease diagnosis and therapy. The accuracy and robustness of existing models based on the machine learning techniques are subject to further improvement. In this study, we propose a deep learning model, called Multi-Label Classifications with Deep Forest, termed MLCDForest, to address multi-label classification on tissue prediction for a given lncRNA, which can be regarded as an implementation of the deep forest model in multi-label classification. The MLCDForest is a sequential multi-label-grained scanning method, which distinguishes from the standard deep forest model. It is proposed to train in sequential of multi-labels with label correlation considered. A systematic comparison using the lncRNA-disease association datasets demonstrates that our method consistently shows superior performance over the state-of-the-art methods in disease prediction. Considering label correlation in the sequential multi-label-grained scanning, our model provides a powerful tool to make multi-label classification and tissue prediction based on given lncRNAs.

中文翻译：

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MLCDForest：长非编码 RNA 疾病预测中的深森林多标签分类。

长链非编码 RNA (lncRNA) 由于其与各种人类疾病的关联而成为近期深入研究的主题。基于 lncRNA 数据构建基于人工智能的疾病或组织预测模型是可取的，这将有助于疾病的诊断和治疗。基于机器学习技术的现有模型的准确性和鲁棒性有待进一步提高。在这项研究中，我们提出了一种深度学习模型，称为具有深森林的多标签分类，称为 MLCDForest，以解决给定 lncRNA 的组织预测的多标签分类，这可以看作是深度森林模型在多标签分类。MLCDForest 是一种顺序多标签粒度扫描方法，区别于标准的深度森林模型。建议在考虑标签相关性的情况下按多标签顺序进行训练。使用 lncRNA 疾病关联数据集进行的系统比较表明，我们的方法在疾病预测方面始终表现出优于最先进方法的性能。考虑到顺序多标签粒度扫描中的标签相关性，我们的模型提供了一个强大的工具，可以根据给定的 lncRNA 进行多标签分类和组织预测。