Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Graph Neural Networks for Mass Spectrometry Prediction   Detecting and quantifying products of cellular metabolism using Mass
Spectrometry (MS) has already shown great promise in many biological and
biomedical applications. The biggest challenge in metabolomics is annotation,
where measured spectra are assigned chemical identities. Despite advances,
current methods provide limited annotation for measured spectra. Here, we
explore using graph neural networks (GNNs) to predict the spectra. The input to
our model is a molecular graph. The model is trained and tested on the NIST 17
LC-MS dataset. We compare our results to NEIMS, a neural network model that
utilizes molecular fingerprints as inputs. Our results show that GNN-based
models offer higher performance than NEIMS. Importantly, we show that ranking
results heavily depend on the candidate set size and on the similarity of the
candidates to the target molecule, thus highlighting the need for consistent,
well-characterized evaluation protocols for this domain.

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The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph