Department of Computer Science, Stanford University, Stanford, CA, USA.
Bioinformatics. 2017 Jul 15;33(14):i190-i198. doi: 10.1093/bioinformatics/btx252.
Understanding functions of proteins in specific human tissues is essential for insights into disease diagnostics and therapeutics, yet prediction of tissue-specific cellular function remains a critical challenge for biomedicine.
Here, we present OhmNet , a hierarchy-aware unsupervised node feature learning approach for multi-layer networks. We build a multi-layer network, where each layer represents molecular interactions in a different human tissue. OhmNet then automatically learns a mapping of proteins, represented as nodes, to a neural embedding-based low-dimensional space of features. OhmNet encourages sharing of similar features among proteins with similar network neighborhoods and among proteins activated in similar tissues. The algorithm generalizes prior work, which generally ignores relationships between tissues, by modeling tissue organization with a rich multiscale tissue hierarchy. We use OhmNet to study multicellular function in a multi-layer protein interaction network of 107 human tissues. In 48 tissues with known tissue-specific cellular functions, OhmNet provides more accurate predictions of cellular function than alternative approaches, and also generates more accurate hypotheses about tissue-specific protein actions. We show that taking into account the tissue hierarchy leads to improved predictive power. Remarkably, we also demonstrate that it is possible to leverage the tissue hierarchy in order to effectively transfer cellular functions to a functionally uncharacterized tissue. Overall, OhmNet moves from flat networks to multiscale models able to predict a range of phenotypes spanning cellular subsystems.
Source code and datasets are available at http://snap.stanford.edu/ohmnet .
理解蛋白质在特定人体组织中的功能对于深入了解疾病的诊断和治疗至关重要，但预测组织特异性细胞功能仍然是生物医学领域的一个关键挑战。
在这里，我们提出了 OhmNet，这是一种具有层次感知能力的无监督节点特征学习方法，用于多层网络。我们构建了一个多层网络，其中每一层代表不同人体组织中的分子相互作用。然后，OhmNet 自动学习将蛋白质（表示为节点）映射到基于神经嵌入的低维特征空间的方法。OhmNet 鼓励具有相似网络邻域的蛋白质之间以及在相似组织中激活的蛋白质之间共享相似的特征。该算法通过使用丰富的多尺度组织层次结构来模拟组织组织，从而概括了先前忽略组织之间关系的工作。我们使用 OhmNet 研究了 107 个人体组织的多层蛋白质相互作用网络中的多细胞功能。在具有已知组织特异性细胞功能的 48 种组织中，OhmNet 提供了比其他方法更准确的细胞功能预测，并且还生成了关于组织特异性蛋白质作用的更准确假设。我们表明，考虑到组织层次结构可以提高预测能力。值得注意的是，我们还证明了可以利用组织层次结构将细胞功能有效地转移到功能未表征的组织中。总体而言，OhmNet 从平面网络发展到能够预测跨越细胞子系统的一系列表型的多尺度模型。
源代码和数据集可在 http://snap.stanford.edu/ohmnet 获得。
