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Hryhorii Chereda on Muck Rack

Hryhorii Chereda

As seen in: Genome Medicine, BioRxiv
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Articles

Deciphering BRCAness Phenotype in Cancer: A Graph Convolutional Neural Network Approach with Layer-wise Relevance Propagation Analysis

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By Jingyu Yang, Hryhorii Chereda, Jurgen Donitz, Annalen Bleckmann
| BioRxiv
Abstract The concept of BRCAness describes tumors that, although lacking BRCA mutations, exhibit similar molecular vulnerabilities, particularly in the context of homologous recombination repair deficiencies. Patients which display BRCAness are often sensitive to treatments like PARP inhibitors. Here we aim to define BRCAness based on gene expression data of the tumors.
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Stability of feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation

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By Hryhorii Chereda, Andreas Leha, Tim Beissbarth
| BioRxiv
New Results doi: https://doi.org/10.1101/2021.12.26.474194 Abstract High-throughput technologies are increasingly important in discovering prognostic molecular signatures and identifying novel drug targets. Molecular signatures can be obtained as a subset of features that are important for the decisions of a Machine Learning (ML) method applied to high-dimensional gene expression data. However, feature selection is inherently unstable in this case.
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Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification

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By Bastian Pfeifer, Hryhorii Chereda, Román Martín, Anna Saranti
| BioRxiv
New Results doi: https://doi.org/10.1101/2023.03.22.533772 Abstract Federated learning enables collaboration in medicine, where data is scattered across multiple centers without the need to aggregate the data in a central cloud. While, in general, machine learning models can be applied to a wide range of data types, graph neural networks (GNNs) are particularly developed for graphs, which are very common in the biomedical domain.
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