2,245 research outputs found
Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19
The current pandemic has highlighted the need for methodologies that can
quickly and reliably prioritize clinically approved compounds for their
potential effectiveness for SARS-CoV-2 infections. In the past decade, network
medicine has developed and validated multiple predictive algorithms for drug
repurposing, exploiting the sub-cellular network-based relationship between a
drug's targets and disease genes. Here, we deployed algorithms relying on
artificial intelligence, network diffusion, and network proximity, tasking each
of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To
test the predictions, we used as ground truth 918 drugs that had been
experimentally screened in VeroE6 cells, and the list of drugs under clinical
trial, that capture the medical community's assessment of drugs with potential
COVID-19 efficacy. We find that while most algorithms offer predictive power
for these ground truth data, no single method offers consistently reliable
outcomes across all datasets and metrics. This prompted us to develop a
multimodal approach that fuses the predictions of all algorithms, showing that
a consensus among the different predictive methods consistently exceeds the
performance of the best individual pipelines. We find that 76 of the 77 drugs
that successfully reduced viral infection do not bind the proteins targeted by
SARS-CoV-2, indicating that these drugs rely on network-based actions that
cannot be identified using docking-based strategies. These advances offer a
methodological pathway to identify repurposable drugs for future pathogens and
neglected diseases underserved by the costs and extended timeline of de novo
drug development
Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis
Advances in next-generation sequencing and high-throughput techniques have enabled the generation of vast amounts of diverse omics data. These big data provide an unprecedented opportunity in biology, but impose great challenges in data integration, data mining, and knowledge discovery due to the complexity, heterogeneity, dynamics, uncertainty, and high-dimensionality inherited in the omics data. Network has been widely used to represent relations between entities in biological system, such as protein-protein interaction, gene regulation, and brain connectivity (i.e. network construction) as well as to infer novel relations given a reconstructed network (aka link prediction). Particularly, heterogeneous multi-layered network (HMLN) has proven successful in integrating diverse biological data for the representation of the hierarchy of biological system. The HMLN provides unparalleled opportunities but imposes new computational challenges on establishing causal genotype-phenotype associations and understanding environmental impact on organisms. In this review, we focus on the recent advances in developing novel computational methods for the inference of novel biological relations from the HMLN. We first discuss the properties of biological HMLN. Then we survey four categories of state-of-the-art methods (matrix factorization, random walk, knowledge graph, and deep learning). Thirdly, we demonstrate their applications to omics data integration and analysis. Finally, we outline strategies for future directions in the development of new HMLN models
Graph Representation Learning in Biomedicine
Biomedical networks are universal descriptors of systems of interacting
elements, from protein interactions to disease networks, all the way to
healthcare systems and scientific knowledge. With the remarkable success of
representation learning in providing powerful predictions and insights, we have
witnessed a rapid expansion of representation learning techniques into
modeling, analyzing, and learning with such networks. In this review, we put
forward an observation that long-standing principles of networks in biology and
medicine -- while often unspoken in machine learning research -- can provide
the conceptual grounding for representation learning, explain its current
successes and limitations, and inform future advances. We synthesize a spectrum
of algorithmic approaches that, at their core, leverage graph topology to embed
networks into compact vector spaces, and capture the breadth of ways in which
representation learning is proving useful. Areas of profound impact include
identifying variants underlying complex traits, disentangling behaviors of
single cells and their effects on health, assisting in diagnosis and treatment
of patients, and developing safe and effective medicines
Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent
Prior attacks on graph neural networks have mostly focused on graph poisoning
and evasion, neglecting the network's weights and biases. Traditional
weight-based fault injection attacks, such as bit flip attacks used for
convolutional neural networks, do not consider the unique properties of graph
neural networks. We propose the Injectivity Bit Flip Attack, the first bit flip
attack designed specifically for graph neural networks. Our attack targets the
learnable neighborhood aggregation functions in quantized message passing
neural networks, degrading their ability to distinguish graph structures and
losing the expressivity of the Weisfeiler-Lehman test. Our findings suggest
that exploiting mathematical properties specific to certain graph neural
network architectures can significantly increase their vulnerability to bit
flip attacks. Injectivity Bit Flip Attacks can degrade the maximal expressive
Graph Isomorphism Networks trained on various graph property prediction
datasets to random output by flipping only a small fraction of the network's
bits, demonstrating its higher destructive power compared to a bit flip attack
transferred from convolutional neural networks. Our attack is transparent and
motivated by theoretical insights which are confirmed by extensive empirical
results
Multi-label Node Classification On Graph-Structured Data
Graph Neural Networks (GNNs) have shown state-of-the-art improvements in node
classification tasks on graphs. While these improvements have been largely
demonstrated in a multi-class classification scenario, a more general and
realistic scenario in which each node could have multiple labels has so far
received little attention. The first challenge in conducting focused studies on
multi-label node classification is the limited number of publicly available
multi-label graph datasets. Therefore, as our first contribution, we collect
and release three real-world biological datasets and develop a multi-label
graph generator to generate datasets with tunable properties. While high label
similarity (high homophily) is usually attributed to the success of GNNs, we
argue that a multi-label scenario does not follow the usual semantics of
homophily and heterophily so far defined for a multi-class scenario. As our
second contribution, besides defining homophily for the multi-label scenario,
we develop a new approach that dynamically fuses the feature and label
correlation information to learn label-informed representations. Finally, we
perform a large-scale comparative study with methods and datasets
which also showcase the effectiveness of our approach. We release our benchmark
at \url{https://anonymous.4open.science/r/LFLF-5D8C/}
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