Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Disease State Prediction From Single-Cell Data Using Graph Attention
  Networks   Single-cell RNA sequencing (scRNA-seq) has revolutionized biological
discovery, providing an unbiased picture of cellular heterogeneity in tissues.
While scRNA-seq has been used extensively to provide insight into both healthy
systems and diseases, it has not been used for disease prediction or
diagnostics. Graph Attention Networks (GAT) have proven to be versatile for a
wide range of tasks by learning from both original features and graph
structures. Here we present a graph attention model for predicting disease
state from single-cell data on a large dataset of Multiple Sclerosis (MS)
patients. MS is a disease of the central nervous system that can be difficult
to diagnose. We train our model on single-cell data obtained from blood and
cerebrospinal fluid (CSF) for a cohort of seven MS patients and six healthy
adults (HA), resulting in 66,667 individual cells. We achieve 92 % accuracy in
predicting MS, outperforming other state-of-the-art methods such as a graph
convolutional network and a random forest classifier. Further, we use the
learned graph attention model to get insight into the features (cell types and
genes) that are important for this prediction. The graph attention model also
allow us to infer a new feature space for the cells that emphasizes the
differences between the two conditions. Finally we use the attention weights to
learn a new low-dimensional embedding that can be visualized. To the best of
our knowledge, this is the first effort to use graph attention, and deep
learning in general, to predict disease state from single-cell data. We
envision applying this method to single-cell data for other diseases.

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The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
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The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
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The categories of this paper are: eess
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The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys
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The categories of this paper are: math
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The categories of this paper are: supr-con
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The categories of this paper are: hep-lat