34 research outputs found
Zero Shot Learning with the Isoperimetric Loss
We introduce the isoperimetric loss as a regularization criterion for
learning the map from a visual representation to a semantic embedding, to be
used to transfer knowledge to unknown classes in a zero-shot learning setting.
We use a pre-trained deep neural network model as a visual representation of
image data, a Word2Vec embedding of class labels, and linear maps between the
visual and semantic embedding spaces. However, the spaces themselves are not
linear, and we postulate the sample embedding to be populated by noisy samples
near otherwise smooth manifolds. We exploit the graph structure defined by the
sample points to regularize the estimates of the manifolds by inferring the
graph connectivity using a generalization of the isoperimetric inequalities
from Riemannian geometry to graphs. Surprisingly, this regularization alone,
paired with the simplest baseline model, outperforms the state-of-the-art among
fully automated methods in zero-shot learning benchmarks such as AwA and CUB.
This improvement is achieved solely by learning the structure of the underlying
spaces by imposing regularity.Comment: Accepted to AAAI-2
Translational rodent models for research on parasitic protozoa – a review of confounders and possibilities
Rodents, in particular Mus musculus, have a long and invaluable history as models for human diseases in biomedical research, although their translational value has been challenged in a number of cases. We provide some examples in which rodents have been suboptimal as models for human biology and discuss confounders which influence experiments and may explain some of the misleading results. Infections of rodents with protozoan parasites are no exception in requiring close consideration upon model choice. We focus on the significant differences between inbred, outbred and wild animals, and the importance of factors such as microbiota, which are gaining attention as crucial variables in infection experiments. Frequently, mouse or rat models are chosen for convenience, e.g., availability in the institution rather than on an unbiased evaluation of whether they provide the answer to a given question. Apart from a general discussion on translational success or failure, we provide examples where infections with single-celled parasites in a chosen lab rodent gave contradictory or misleading results, and when possible discuss the reason for this. We present emerging alternatives to traditional rodent models, such as humanized mice and organoid primary cell cultures. So-called recombinant inbred strains such as the Collaborative Cross collection are also a potential solution for certain challenges. In addition, we emphasize the advantages of using wild rodents for certain immunological, ecological, and/or behavioral questions. The experimental challenges (e.g., availability of species-specific reagents) that come with the use of such non-model systems are also discussed. Our intention is to foster critical judgment of both traditional and newly available translational rodent models for research on parasitic protozoa that can complement the existing mouse and rat models
Graph Spectral Embedding using the Geodesic Betweeness Centrality
We introduce the Graph Sylvester Embedding (GSE), an unsupervised graph
representation of local similarity, connectivity, and global structure. GSE
uses the solution of the Sylvester equation to capture both network structure
and neighborhood proximity in a single representation. Unlike embeddings based
on the eigenvectors of the Laplacian, GSE incorporates two or more basis
functions, for instance using the Laplacian and the affinity matrix. Such basis
functions are constructed not from the original graph, but from one whose
weights measure the centrality of an edge (the fraction of the number of
shortest paths that pass through that edge) in the original graph. This allows
more flexibility and control to represent complex network structure and shows
significant improvements over the state of the art when used for data analysis
tasks such as predicting failed edges in material science and network alignment
in the human-SARS CoV-2 protein-protein interactome.Comment: arXiv admin note: substantial text overlap with arXiv:2009.1444
Interpretable Network Propagation with Application to Expanding the Repertoire of Human Proteins that Interact with SARS-CoV-2
Background: Network propagation has been widely used for nearly 20 years to
predict gene functions and phenotypes. Despite the popularity of this approach,
little attention has been paid to the question of provenance tracing in this
context, e.g., determining how much any experimental observation in the input
contributes to the score of every prediction.
Results: We design a network propagation framework with two novel components
and apply it to predict human proteins that directly or indirectly interact
with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to
its experimentally validated sources, which in our case are human proteins
experimentally determined to interact with viral proteins. Second, we design a
technique that helps to reduce the manual adjustment of parameters by users. We
find that for every top-ranking prediction, the highest contribution to its
score arises from a direct neighbor in a human protein-protein interaction
network. We further analyze these results to develop functional insights on
SARS-CoV-2 that expand on known biology such as the connection between
endoplasmic reticulum stress, HSPA5, and anti-clotting agents.
Conclusions: We examine how our provenance tracing method can be generalized
to a broad class of network-based algorithms. We provide a useful resource for
the SARS-CoV-2 community that implicates many previously undocumented proteins
with putative functional relationships to viral infection. This resource
includes potential drugs that can be opportunistically repositioned to target
these proteins. We also discuss how our overall framework can be extended to
other, newly-emerging viruses