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DeepCNA: an explainable deep learning method for cancer diagnosis and cancer-specific patterns of copy number aberrations - MaRDI portal

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

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DOI10.5281/zenodo.14892622Zenodo14892622MaRDI QIDQ6693374

Dataset published at Zenodo repository.

Author name not available (Why is that?)

Publication date: 19 February 2025

Copyright license: No records found.



Description

Data associated with the manuscript: https://www.biorxiv.org/content/10.1101/2024.03.08.584160v1 A deep learning approach classified samples according to cancer primary site, based only on binned copy number aberations. Guided integrated gradients was then used to calculate an attribute score for each bin, representing how important that genomic location was in the cancer classification. Output of the analysis: Binned copy numbers for 6707 samples from the Genomics England Limited data set (channel 0 = total copy number, channel 1 = minor allele copy number) Attribute scores for how important each genomic bin was for the cancer classification (channel 0 and channel 1) Metadata linking rows in the binned data above to sample information Input data for the analysis DATA_CN.tgz contains each sample's copy number profile as an .npy file DATA.tgz contains each sample's normalised copy number profile as an .npy file. This is the input to the neural network training in the DeepCNA repository






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