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Multi-omics integration for neuroblastoma clinical endpoint prediction.
Biology Direct ( IF 5.7 ) Pub Date : 2018-04-05 , DOI: 10.1186/s13062-018-0207-8
Margherita Francescatto 1 , Marco Chierici 1 , Setareh Rezvan Dezfooli 1 , Alessandro Zandonà 1, 2, 3 , Giuseppe Jurman 1 , Cesare Furlanello 1
Affiliation  

BACKGROUND 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. RESULTS 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. CONCLUSIONS 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. REVIEWERS This article was reviewed by Djork-Arné Clevert and Tieliu Shi.

中文翻译:

用于神经母细胞瘤临床终点预测的多组学集成。

背景技术诸如芯片和下一代测序之类的高通量方法通常用于癌症研究中,从而在不同的组学层产生复杂的数据。组学数据的有效整合可以为癌症生物学机制提供更广泛的见解,帮助研究人员和临床医生开发个性化疗法。结果在CAMDA 2017神经母细胞瘤数据集成挑战的背景下,我们探索了集成网络融合(INF)的使用,这是一种将相似性网络融合与机器学习相结合的生物信息学框架,用于集成多个组学数据。我们将INF框架应用于神经母细胞瘤患者预后的预测,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。结论INF方法可有效集成多个数据源,从而为患者分类提供紧凑的特征签名,并且性能可与其他方法媲美。自动编码器方法提供的集成数据的潜在空间表示,通过改善生存终点的分类,并通过提供手段来发现以独特的总体生存(OS)曲线为特征的两组患者,均提供了可喜的结果。审阅者本文由Djork-ArnéClevert和Tieliu Shi审阅。结论INF方法可有效集成多个数据源,从而为患者分类提供紧凑的特征签名,并且性能可与其他方法媲美。自动编码器方法提供的集成数据的潜在空间表示,通过改善生存终点的分类,并通过提供手段来发现以独特的总体生存(OS)曲线为特征的两组患者,均提供了可喜的结果。审阅者本文由Djork-ArnéClevert和Tieliu Shi审阅。结论INF方法可有效集成多个数据源,从而为患者分类提供紧凑的特征签名,并且性能可与其他方法媲美。自动编码器方法提供的集成数据的潜在空间表示,通过改善生存终点的分类,并通过提供手段来发现以独特的总体生存(OS)曲线为特征的两组患者,均提供了可喜的结果。审阅者本文由Djork-ArnéClevert和Tieliu Shi审阅。通过改善生存终点的分类,以及通过提供手段发现两组具有不同总体生存(OS)曲线特征的患者,都可以做到这一点。审阅者本文由Djork-ArnéClevert和Tieliu Shi审阅。通过改善生存终点的分类,以及通过提供手段发现两组具有不同总体生存(OS)曲线特征的患者,都可以做到这一点。审阅者本文由Djork-ArnéClevert和Tieliu Shi审阅。
更新日期:2019-11-01
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