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arXiv 1805.09673q-bio.QM
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基于仿射网络融合和半监督学习的癌症患者聚类

Affinity Network Fusion and Semi-supervised Learning for Cancer Patient Clustering

Tianle Ma, Aidong Zhang

备注：
Accepted to Methods (https://www.journals.elsevier.com/methods). arXiv admin note: text overlap with arXiv:1708.07136
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摘要：

定义癌症等复杂疾病的亚型，并对患有相同疾病但不同亚型的患者群体进行分层，以进行靶向治疗，这对于个性化和精准医疗非常重要。结合多组学数据的方法比仅使用一种数据类型进行患者聚类和疾病亚型发现的方法更有利。然而，它是具有挑战性的集成多组学数据，因为它们是异构和噪声。在本文中，我们提出了亲和网络融合（ANF）整合多组学数据的患者聚类。ANF首先为每个组学数据类型构建患者亲和网络，然后计算融合网络进行谱聚类。我们将ANF应用于从GDC数据门户网站下载的由2193名患者组成的经过处理的协调的癌症数据集，并将患者聚类为正确的疾病类型，产生了有希望的结果。此外，我们开发了一个半监督模型结合ANF和神经网络的Few-Shot学习。在某些情况下，该模型可以在训练不到1%的数据的情况下在测试集上实现超过90%的准确率。这证明了ANF在学习患者的良好表示方面的能力，并显示了半监督学习在癌症患者聚类中的巨大潜力。

英文摘要：

Defining subtypes of complex diseases such as cancer and stratifying patient groups with the same disease but different subtypes for targeted treatments is important for personalized and precision medicine. Approaches that incorporate multi-omic data are more advantageous to those using only one data type for patient clustering and disease subtype discovery. However, it is challenging to integrate multi-omic data as they are heterogeneous and noisy. In this paper, we present Affinity Network Fusion (ANF) to integrate multi-omic data for patient clustering. ANF first constructs patient affinity networks for each omic data type, and then calculates a fused network for spectral clustering. We applied ANF to a processed harmonized cancer dataset downloaded from GDC data portal consisting of 2193 patients, and generated promising results on clustering patients into correct disease types. Moreover, we developed a semi-supervised model combining ANF and neural network for few-shot learning. In several cases, the model can achieve greater than 90% acccuracy on test set with training less than 1% of the data. This demonstrates the power of ANF in learning a good representation of patients, and shows the great potential of semi-supervised learning in cancer patient clustering.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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