Fondazione Bruno Kessler, Via Sommarive 18, Trento, 38123, Italy.
Centre for Integrative Biology, University of Trento, Via Sommarive 9, Trento, 38123, Italy.
Biol Direct. 2018 Apr 3;13(1):5. doi: 10.1186/s13062-018-0207-8.
High-throughput methodologies such as microarrays and next-generation sequencing are routinely used in cancer research, generating complex data at different omics layers. The effective integration of omics data could provide a broader insight into the mechanisms of cancer biology, helping researchers and clinicians to develop personalized therapies.
In the context of CAMDA 2017 Neuroblastoma Data Integration challenge, we explore the use of Integrative Network Fusion (INF), a bioinformatics framework combining a similarity network fusion with machine learning for the integration of multiple omics data. We apply the INF framework for the prediction of neuroblastoma patient outcome, integrating RNA-Seq, microarray and array comparative genomic hybridization data. We additionally explore the use of autoencoders as a method to integrate microarray expression and copy number data.
The INF method is effective for the integration of multiple data sources providing compact feature signatures for patient classification with performances comparable to other methods. Latent space representation of the integrated data provided by the autoencoder approach gives promising results, both by improving classification on survival endpoints and by providing means to discover two groups of patients characterized by distinct overall survival (OS) curves.
This article was reviewed by Djork-Arné Clevert and Tieliu Shi.
高通量方法，如微阵列和下一代测序，在癌症研究中经常被使用，在不同的组学层面上生成复杂的数据。组学数据的有效整合可以更深入地了解癌症生物学的机制，帮助研究人员和临床医生开发个性化的治疗方法。
在 CAMDA 2017 神经母细胞瘤数据整合挑战赛的背景下，我们探索了整合网络融合（INF）的使用，这是一个将相似网络融合与机器学习相结合的生物信息学框架，用于整合多个组学数据。我们应用 INF 框架来预测神经母细胞瘤患者的预后，整合了 RNA-Seq、微阵列和阵列比较基因组杂交数据。我们还探索了使用自动编码器作为整合微阵列表达和拷贝数数据的方法。
INF 方法对于整合多个数据源是有效的，它为患者分类提供了紧凑的特征签名，其性能可与其他方法相媲美。自动编码器方法提供的集成数据的潜在空间表示给出了有希望的结果，不仅通过改善生存终点的分类，而且通过提供发现两组具有不同总体生存（OS）曲线的患者的手段。
本文由 Djork-Arné Clevert 和 Tieliu Shi 审稿。
