Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting Potential Drug Targets Using Tensor Factorisation and
  Knowledge Graph Embeddings   The drug discovery and development process is a long and expensive one,
costing over 1 billion USD on average per drug and taking 10-15 years. To
reduce the high levels of attrition throughout the process, there has been a
growing interest in applying machine learning methodologies to various stages
of drug discovery process in the recent decade, including at the earliest stage
- identification of druggable disease genes. In this paper, we have developed a
new tensor factorisation model to predict potential drug targets (i.e.,genes or
proteins) for diseases. We created a three dimensional tensor which consists of
1,048 targets, 860 diseases and 230,011 evidence attributes and clinical
outcomes connecting them, using data extracted from the Open Targets and
PharmaProjects databases. We enriched the data with gene representations
learned from a drug discovery-oriented knowledge graph and applied our proposed
method to predict the clinical outcomes for unseen target and dis-ease pairs.
We designed three evaluation strategies to measure the prediction performance
and benchmarked several commonly used machine learning classifiers together
with matrix and tensor factorisation methods. The result shows that
incorporating knowledge graph embeddings significantly improves the prediction
accuracy and that training tensor factorisation alongside a dense neural
network outperforms other methods. In summary, our framework combines two
actively studied machine learning approaches to disease target identification,
tensor factorisation and knowledge graph representation learning, which could
be a promising avenue for further exploration in data-driven drug discovery.

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The categories of this paper are: ao-sci
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The categories of this paper are: supr-con
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The categories of this paper are: solv-int
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The categories of this paper are: adap-org
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The categories of this paper are: alg-geom
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The categories of this paper are: math-ph
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The categories of this paper are: q-fin
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The categories of this paper are: nlin
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The categories of this paper are: hep-lat
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The categories of this paper are: mtrl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-th
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The categories of this paper are: math
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The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
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The categories of this paper are: econ