A disease network-based deep learning approach for characterizing melanoma

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作者

Xin Lai¹

1. Tampere University

会议/活动

The German Conference on Bioinformatics 2023, September 2023 (Hamburg, Germany)

海报摘要

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.

关键词

Melanoma, Artifical intelligence, Systems medicine, Deep learning, TCGA, Network medicine

研究领域

Bioinformatics and Genomics, Medicine

参考文献

1. 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.

基金

暂无数据

补充材料

暂无数据

附加信息

利益冲突

No competing interests were disclosed.

数据可用性声明

The datasets generated during and / or analyzed during the current study are available elsewhere (e.g., repository).
doi.org/10.5281/zenodo.5556063

知识共享许可协议

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.