Enhancing cancer classification accuracy with a self-attention network using panel capture sequencing data.

Abstract excerpt

Cancer classification is pivotal for precision oncology, yet traditional methods struggle with the molecular heterogeneity of tumors. Our study introduces a self-attention based Conv1D machine learning network designed for panel capture sequencing data, which is more commonly used in clinical settings. Combining clinical capture sequencing data and The Cancer Genome Atlas data, we achieved an overall...

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