Using Graph Neural Networks for Mass Spectrometry Prediction

• URL: http://arxiv.org/abs/2010.04661v1
• Date: Fri, 9 Oct 2020 16:06:57 GMT
• Title: Using Graph Neural Networks for Mass Spectrometry Prediction
• Authors: Hao Zhu, Liping Liu, Soha Hassoun
• Abstract summary: We explore using graph neural networks (GNNs) to predict measured spectra. The input to our model is a molecular graph. We compare our results to NEIMS, a neural network model that utilizes molecular fingerprints as inputs.
• Score: 11.797657070243716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classification [0.0]
  We introduce a novel model that combines GNNs and edge convolution, leveraging the interconnectedness of RGB channel feature values to strongly represent connections between crucial graph nodes. Our proposed model performs on par with state-of-the-art Deep Neural Networks (DNNs) but does so with 1000 times fewer parameters, resulting in reduced training time and data requirements.
  arXiv  Detail & Related papers  (2023-07-24T13:39:21Z)
• Mass Spectra Prediction with Structural Motif-based Graph Neural Networks [21.71309513265843]
  MoMS-Net is a system that predicts mass spectra using the information derived from structural motifs and the implementation of Graph Neural Networks (GNNs) We have tested our model across diverse mass spectra and have observed its superiority over other existing models.
  arXiv  Detail & Related papers  (2023-06-28T10:33:57Z)
• HiGNN: Hierarchical Informative Graph Neural Networks for Molecular Property Prediction Equipped with Feature-Wise Attention [5.735627221409312]
  We propose a well-designed hierarchical informative graph neural networks framework (termed HiGNN) for predicting molecular property. Experiments demonstrate that HiGNN achieves state-of-the-art predictive performance on many challenging drug discovery-associated benchmark datasets.
  arXiv  Detail & Related papers  (2022-08-30T05:16:15Z)
• MolGraph: a Python package for the implementation of molecular graphs and graph neural networks with TensorFlow and Keras [51.92255321684027]
  MolGraph is a graph neural network (GNN) package for molecular machine learning (ML) MolGraph implements a chemistry module to accommodate the generation of small molecular graphs, which can be passed to a GNN algorithm to solve a molecular ML problem. GNNs proved useful for molecular identification and improved interpretability of chromatographic retention time data.
  arXiv  Detail & Related papers  (2022-08-21T18:37:41Z)
• Graph neural networks for the prediction of molecular structure-property relationships [59.11160990637615]
  Graph neural networks (GNNs) are a novel machine learning method that directly work on the molecular graph. GNNs allow to learn properties in an end-to-end fashion, thereby avoiding the need for informative descriptors. We describe the fundamentals of GNNs and demonstrate the application of GNNs via two examples for molecular property prediction.
  arXiv  Detail & Related papers  (2022-07-25T11:30:44Z)
• Discovering the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions [51.597480162777074]
  Graph neural networks (GNNs) rely on the message passing paradigm to propagate node features and build interactions. Recent works point out that different graph learning tasks require different ranges of interactions between nodes. We study two common graph construction methods in scientific domains, i.e., emphK-nearest neighbor (KNN) graphs and emphfully-connected (FC) graphs.
  arXiv  Detail & Related papers  (2022-05-15T11:38:14Z)
• Ensemble Spectral Prediction (ESP) Model for Metabolite Annotation [10.640447979978436]
  Key challenge in metabolomics is annotating measured spectra from a biological sample with chemical identities. We propose a novel machine learning model, Ensemble Spectral Prediction (ESP), for metabolite annotation.
  arXiv  Detail & Related papers  (2022-03-25T17:05:41Z)
• Weakly-supervised Graph Meta-learning for Few-shot Node Classification [53.36828125138149]
  We propose a new graph meta-learning framework -- Graph Hallucination Networks (Meta-GHN) Based on a new robustness-enhanced episodic training, Meta-GHN is meta-learned to hallucinate clean node representations from weakly-labeled data. Extensive experiments demonstrate the superiority of Meta-GHN over existing graph meta-learning studies.
  arXiv  Detail & Related papers  (2021-06-12T22:22:10Z)
• A Systematic Comparison Study on Hyperparameter Optimisation of Graph Neural Networks for Molecular Property Prediction [8.02401104726362]
  Graph neural networks (GNNs) have been proposed for a wide range of graph-related learning tasks. In recent years there has been an increasing number of GNN systems that were applied to predict molecular properties.
  arXiv  Detail & Related papers  (2021-02-08T15:40:50Z)
• Multi-View Graph Neural Networks for Molecular Property Prediction [67.54644592806876]
  We present Multi-View Graph Neural Network (MV-GNN), a multi-view message passing architecture. In MV-GNN, we introduce a shared self-attentive readout component and disagreement loss to stabilize the training process. We further boost the expressive power of MV-GNN by proposing a cross-dependent message passing scheme.
  arXiv  Detail & Related papers  (2020-05-17T04:46:07Z)
• Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis [49.05541693243502]
  We develop a framework for analyzing fMRI data using the Graph Isomorphism Network (GIN) One of the important contributions of this paper is the observation that the GIN is a dual representation of convolutional neural network (CNN) in the graph space. We exploit CNN-based saliency map techniques for the GNN, which we tailor to the proposed GIN with one-hot encoding.
  arXiv  Detail & Related papers  (2020-01-10T23:40:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.