6 research outputs found
ncRNA Classification with Graph Convolutional Networks
Non-coding RNA (ncRNA) are RNA sequences which don't code for a gene but
instead carry important biological functions. The task of ncRNA classification
consists in classifying a given ncRNA sequence into its family. While it has
been shown that the graph structure of an ncRNA sequence folding is of great
importance for the prediction of its family, current methods make use of
machine learning classifiers on hand-crafted graph features. We improve on the
state-of-the-art for this task with a graph convolutional network model which
achieves an accuracy of 85.73% and an F1-score of 85.61% over 13 classes.
Moreover, our model learns in an end-to-end fashion from the raw RNA graphs and
removes the need for expensive feature extraction. To the best of our
knowledge, this also represents the first successful application of graph
convolutional networks to RNA folding data
Graph neural networks and attention-based CNN-LSTM for protein classification
This paper focuses on three critical problems on protein classification.
Firstly, Carbohydrate-active enzyme (CAZyme) classification can help people to
understand the properties of enzymes. However, one CAZyme may belong to several
classes. This leads to Multi-label CAZyme classification. Secondly, to capture
information from the secondary structure of protein, protein classification is
modeled as graph classification problem. Thirdly, compound-protein interactions
prediction employs graph learning for compound with sequential embedding for
protein. This can be seen as classification task for compound-protein pairs.
This paper proposes three models for protein classification. Firstly, this
paper proposes a Multi-label CAZyme classification model using CNN-LSTM with
Attention mechanism. Secondly, this paper proposes a variational graph
autoencoder based subspace learning model for protein graph classification.
Thirdly, this paper proposes graph isomorphism networks (GIN) and
Attention-based CNN-LSTM for compound-protein interactions prediction, as well
as comparing GIN with graph convolution networks (GCN) and graph attention
networks (GAT) in this task. The proposed models are effective for protein
classification. Source code and data are available at
https://github.com/zshicode/GNN-AttCL-protein. Besides, this repository
collects and collates the benchmark datasets with respect to above problems,
including CAZyme classification, enzyme protein graph classification,
compound-protein interactions prediction, drug-target affinities prediction and
drug-drug interactions prediction. Hence, the usage for evaluation by benchmark
datasets can be more conveniently