Article

SAEG: A Novel Deep Learning Architecture for Somatic Alterations

2025-02-27

Abstract excerpt

Somatic alterations, like mutations and copy number changes, driver oncogenesis and cancer progression. Their inhibition has been exploited in the clinic, with several targeted therapies approved for patients with specific mutations or amplifications. However, the response rate of these treatments remains low. The causes are several, ranging from clonal heterogeneity to off target binding. For this reason, CRISPR...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: b9004440-da69-5f32-a2a8-2cdb4843d37d
DOI: 10.1101/2025.02.21.639424

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

SAEG: A Novel Deep Learning Architecture for Somatic AlterationsDOI 10.1101/2025.02.21.639424Read

A benchmarking of deep neural network models for cancer subtyping using single point mutationsSemantic proximity Read

DeepCNA: an explainable deep learning method for cancer diagnosis and cancer-specific patterns of copy number aberrationsSemantic proximity Read

Deep convolutional neural networks for accurate somatic mutation detectionSemantic proximity Read

Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtypingSemantic proximity Read

Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtyping.Semantic proximity Read

Robust Cancer Mutation Detection with Deep Learning Models Derived from Tumor-Normal Sequencing DataSemantic proximity Read

Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample.Semantic proximity Read

A new deep learning technique reveals the exclusive functional contributions of individual cancer mutations.Semantic proximity Read

Deep learning discerns cancer mutation exclusivitySemantic proximity Read

Sparse input neural networks to differentiate 32 primary cancer types based on somatic point mutationsSemantic proximity Read

Adaptive Somatic Mutations Calls with Deep Learning and Semi-Simulated DataSemantic proximity Read

A deep learning solution to predict tumor mutational status : an evaluation as a first-line diagnostic toolSemantic proximity Read