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A disease network-based deep learning approach for characterizing melanoma
PUBLISHED June 03, 2024 (DOI: https://doi.org/10.54985/peeref.2406p8080757)
NOT PEER REVIEWED
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Authors
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Xin Lai1
- Tampere University
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Conference / event
- The German Conference on Bioinformatics 2023, September 2023 (Hamburg, Germany)
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Poster summary
- In cutaneous melanoma, genomic aberrations influence prognosis. We developed a method that combines genomics with a disease network and deep learning for patient classification to assess the impact of genomic features. The model's community clusters condense genomics into a score profile, revealing three subtypes with distinct survival outcomes. Machine learning ranked the impact of genomic features on scores, with top features providing insights into mutations, interactions, and pathways such as signaling and immune response. This network-based AI approach provides personalized prognostic scores for melanoma.
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Keywords
- Melanoma, Artifical intelligence, Systems medicine, Deep learning, TCGA, Network medicine
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Research areas
- Bioinformatics and Genomics, Medicine
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References
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- Lai X, Zhou JF, Wessely A, Heppt M, Maier A, Berking C, Vera J, Zhang L. International Journal of Cancer. 2022; 150(6): 1029-1044. DOI: 10.1002/ijc.33860.
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Funding
- No data provided
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Supplemental files
- No data provided
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Additional information
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- Competing interests
- No competing interests were disclosed.
- Data availability statement
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The datasets generated during and / or analyzed during the current study are available elsewhere (e.g., repository).
doi.org/10.5281/zenodo.5556063
- Creative Commons license
- Copyright © 2024 Lai. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Lai, X. A disease network-based deep learning approach for characterizing melanoma [not peer reviewed]. Peeref 2024 (poster).
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