Motivation: 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.
Results: 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

提供一种针对多层网络的有层级感知无监督节点特征学习方法，可在不同人体组织中学习蛋白质的特征，从而更准确地预测细胞功能。通过分层建模以及运用类似的特征来推断网络邻域和激活组织中的蛋白质，该算法可以预测多细胞系统中的多种表型，包括对功能未知组织的有效细胞功能转移。

通过多层组织网络预测多细胞功能

Predicting multicellular function through multi-layer tissue networks

We consider the problem of estimating a consensus community structure by
combining information from multiple layers of a multi-layer network using
methods based on the spectral clustering or a low-rank matrix factorization. As
a general theme, these "intermediate fusion" methods involve obtaining a low
column rank matrix by optimizing an objective function and then using the
columns of the matrix for clustering. However, the theoretical properties of
these methods remain largely unexplored. In the absence of statistical
guarantees on the objective functions, it is difficult to determine if the
algorithms optimizing the objectives will return good community structures. We
investigate the consistency properties of the global optimizer of some of these
objective functions under the multi-layer stochastic blockmodel. For this
purpose, we derive several new asymptotic results showing consistency of the
intermediate fusion techniques along with the spectral clustering of mean
adjacency matrix under a high dimensional setup, where the number of nodes, the
number of layers and the number of communities of the multi-layer graph grow.
Our numerical study shows that the intermediate fusion techniques outperform
late fusion methods, namely spectral clustering on aggregate spectral kernel
and module allegiance matrix in sparse networks, while they outperform the
spectral clustering of mean adjacency matrix in multi-layer networks that
contain layers with both homophilic and heterophilic communities.

本文探讨了利用基于谱聚类或低秩矩阵分解的中间融合方法，结合多层网络的信息来估计共识社区结构的问题。在高维设置下，它们可以有效地一致且精确地估计社区结构。

多层网络一致社区检测的谱和矩阵分解方法

Spectral and matrix factorization methods for consistent community  detection in multi-layer networks

The style of an image plays a significant role in how it is viewed, but style
has received little attention in computer vision research. We describe an
approach to predicting style of images, and perform a thorough evaluation of
different image features for these tasks. We find that features learned in a
multi-layer network generally perform best -- even when trained with object
class (not style) labels. Our large-scale learning methods results in the best
published performance on an existing dataset of aesthetic ratings and
photographic style annotations. We present two novel datasets: 80K Flickr
photographs annotated with 20 curated style labels, and 85K paintings annotated
with 25 style/genre labels. Our approach shows excellent classification
performance on both datasets. We use the learned classifiers to extend
traditional tag-based image search to consider stylistic constraints, and
demonstrate cross-dataset understanding of style.

本研究探讨了计算机视觉中图像风格对于图像感知的影响，通过使用多层网络训练得出最佳效果的图像特征进行分类，并成功将这些分类器扩展到 tag-based image search 中，以提高搜索的效果。