%0 Journal Article %1 chereda2019utilizing %A Chereda, Hryhorii %A Bleckmann, Annalen %A Kramer, Frank %A Leha, Andreas %A Beissbarth, Tim %C Netherlands %D 2019 %J Studies in health technology and informatics %K background bki gnn graph informed integration knowledge learning machine ml networks neural %P 181--186 %R 10.3233/SHTI190824 %T Utilizing Molecular Network Information via Graph Convolutional Neural Networks to Predict Metastatic Event in Breast Cancer %U https://pubmed.ncbi.nlm.nih.gov/31483271 %V 267 %X Gene expression data is commonly available in cancer research and provides a snapshot of the molecular status of a specific tumor tissue. This high-dimensional data can be analyzed for diagnoses, prognoses, and to suggest treatment options. Machine learning based methods are widely used for such analysis. Recently, a set of deep learning techniques was successfully applied in different domains including bioinformatics. One of these prominent techniques are convolutional neural networks (CNN). Currently, CNNs are extending to non-Euclidean domains like graphs. Molecular networks are commonly represented as graphs detailing interactions between molecules. Gene expression data can be assigned to the vertices of these graphs, and the edges can depict interactions, regulations and signal flow. In other words, gene expression data can be structured by utilizing molecular network information as prior knowledge. Here, we applied graph CNN to gene expression data of breast cancer patients to predict the occurrence of metastatic events. To structure the data we utilized a protein-protein interaction network. We show that the graph CNN exploiting the prior knowledge is able to provide classification improvements for the prediction of metastatic events compared to existing methods.

@article{chereda2019utilizing, abstract = {Gene expression data is commonly available in cancer research and provides a snapshot of the molecular status of a specific tumor tissue. This high-dimensional data can be analyzed for diagnoses, prognoses, and to suggest treatment options. Machine learning based methods are widely used for such analysis. Recently, a set of deep learning techniques was successfully applied in different domains including bioinformatics. One of these prominent techniques are convolutional neural networks (CNN). Currently, CNNs are extending to non-Euclidean domains like graphs. Molecular networks are commonly represented as graphs detailing interactions between molecules. Gene expression data can be assigned to the vertices of these graphs, and the edges can depict interactions, regulations and signal flow. In other words, gene expression data can be structured by utilizing molecular network information as prior knowledge. Here, we applied graph CNN to gene expression data of breast cancer patients to predict the occurrence of metastatic events. To structure the data we utilized a protein-protein interaction network. We show that the graph CNN exploiting the prior knowledge is able to provide classification improvements for the prediction of metastatic events compared to existing methods.}, added-at = {2024-02-09T08:22:43.000+0100}, address = {Netherlands}, author = {Chereda, Hryhorii and Bleckmann, Annalen and Kramer, Frank and Leha, Andreas and Beissbarth, Tim}, biburl = {https://www.bibsonomy.org/bibtex/26fc1033c65d0a8c5891a2b79092190c4/becker}, comment = {31483271[pmid]}, description = {Utilizing Molecular Network Information via Graph Convolutional Neural Networks to Predict Metastatic Event in Breast Cancer - PubMed}, doi = {10.3233/SHTI190824}, interhash = {dacac4e99e4c43c35854c87d6538e8bf}, intrahash = {6fc1033c65d0a8c5891a2b79092190c4}, issn = {18798365}, journal = {Studies in health technology and informatics}, keywords = {background bki gnn graph informed integration knowledge learning machine ml networks neural}, month = sep, pages = {181--186}, timestamp = {2024-02-09T08:22:43.000+0100}, title = {Utilizing Molecular Network Information via Graph Convolutional Neural Networks to Predict Metastatic Event in Breast Cancer}, url = {https://pubmed.ncbi.nlm.nih.gov/31483271}, volume = 267, year = 2019 }