Heterogeneous data integration methods for patient similarity networks.
Heterogeneous data integration methods for patient similarity networks.
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DOI：
10.1093/bib/bbac207
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发表时间：
2022-07-18
影响因子：
9.5
通讯作者：
中科院分区：
文献类型：
--
作者：
患者相似性网络（PSN），其中患者表示为节点，其相似性表示为加权边，正越来越多地用于临床研究。这些网络提供了对患者之间关系的深刻总结，并且可以通过归纳或转导学习算法来预测患者结果，表型和疾病风险。PSN也可以很容易地可视化，从而提供了一种自然的方式来检查复杂的异构患者数据，并提供了机器学习算法获得的预测的某种程度的可解释性。高通量技术的出现，使我们能够获得相同患者的高维视图（例如组学数据，实验室数据，成像数据），要求为PSN开发数据融合技术，以利用这种丰富的异构信息。在这篇文章中，我们回顾了现有的方法，整合多个生物医学数据视图，构建PSN，连同不同的患者相似性措施，已被提出。我们还回顾了机器学习文献中出现的但尚未应用于PSN的方法，从而为浏览有关此主题的大量机器学习文献提供了资源。特别是，我们专注于可用于集成非常异构的数据集的方法，包括多组学数据以及来自临床信息和医学成像的数据。
Patient similarity networks (PSNs), where patients are represented as nodes and their similarities as weighted edges, are being increasingly used in clinical research. These networks provide an insightful summary of the relationships among patients and can be exploited by inductive or transductive learning algorithms for the prediction of patient outcome, phenotype and disease risk. PSNs can also be easily visualized, thus offering a natural way to inspect complex heterogeneous patient data and providing some level of explainability of the predictions obtained by machine learning algorithms. The advent of high-throughput technologies, enabling us to acquire high-dimensional views of the same patients (e.g. omics data, laboratory data, imaging data), calls for the development of data fusion techniques for PSNs in order to leverage this rich heterogeneous information. In this article, we review existing methods for integrating multiple biomedical data views to construct PSNs, together with the different patient similarity measures that have been proposed. We also review methods that have appeared in the machine learning literature but have not yet been applied to PSNs, thus providing a resource to navigate the vast machine learning literature existing on this topic. In particular, we focus on methods that could be used to integrate very heterogeneous datasets, including multi-omics data as well as data derived from clinical information and medical imaging.
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影响因子：
9.2
作者：
Zhang H;Zhu F;Dodge HH;Higgins GA;Omenn GS;Guan Y;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
DOI：
10.1109/access.2020.3034032
发表时间：
2020
期刊：
IEEE access : practical innovations, open solutions
影响因子：
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作者：
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通讯作者：
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DOI：
10.1073/pnas.0308531101
发表时间：
2004-03-23
影响因子：
11.1
作者：
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通讯作者：
Mesirov, JP
DOI：
10.1109/tpami.2018.2798607
发表时间：
2019-02-01
影响因子：
23.6
作者：
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通讯作者：
Morency, Louis-Philippe