Publication date
January 2022
DOIAbstract
Code, figures, and data for analyses outlined in "Inferring cancer evolution from single tumour biopsies using synthetic supervised deep learning". Please see README for a description of folders and files
Code, figures, and data for analyses outlined in "Inferring cancer evolution from single tumour biopsies using synthetic supervised deep learning". Please see README for a description of folders and files
automatic reconstruction of cancer evolutionary trees from multi-region next generation sequencin
Heterogeneity is arguably one of the most important hallmarks of cancer which contributes to its dru...
Supplemental data for the paper titled "An unsupervised deep learning framework with variational aut...
Code, figures, and data for analyses outlined in "Inferring cancer evolution from single tumour biop...
Variant allele frequencies (VAF) encode ongoing evolution and subclonal selection in growing tumours...
Dataset access for the paper: A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled...
MOBSTER is an approach for subclonal reconstruction of tumors from cancer genomics data on the basis...
Information about the scripts and data used in this study: Sahraeian SME, et al., Achieving Robust ...
Cancer is a complex and deadly disease that is caused by genetic lesions in somatic cells. Further r...
Source code demonstrating machine learning techniques for classifying mutagenic origins. Written in ...
4siBackground. The large-scale availability of whole-genome sequencing profiles from bulk DNA sequen...
Additional file 1: Data.Extensive description of all the methods and experiments ran with TRaIT, bo...
Supplementary tables and figures for the article “Machine learning techniques for classifying the mu...
Contains fulltext : 84537.pdf (author's version ) (Open Access)Applications of Evo...
This is the accepted iPC deliverable D1.3 „Synthetic data for testing and training patient, cancer, ...
automatic reconstruction of cancer evolutionary trees from multi-region next generation sequencin
Heterogeneity is arguably one of the most important hallmarks of cancer which contributes to its dru...
Supplemental data for the paper titled "An unsupervised deep learning framework with variational aut...
Code, figures, and data for analyses outlined in "Inferring cancer evolution from single tumour biop...
Variant allele frequencies (VAF) encode ongoing evolution and subclonal selection in growing tumours...
Dataset access for the paper: A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled...
MOBSTER is an approach for subclonal reconstruction of tumors from cancer genomics data on the basis...
Information about the scripts and data used in this study: Sahraeian SME, et al., Achieving Robust ...
Cancer is a complex and deadly disease that is caused by genetic lesions in somatic cells. Further r...
Source code demonstrating machine learning techniques for classifying mutagenic origins. Written in ...
4siBackground. The large-scale availability of whole-genome sequencing profiles from bulk DNA sequen...
Additional file 1: Data.Extensive description of all the methods and experiments ran with TRaIT, bo...
Supplementary tables and figures for the article “Machine learning techniques for classifying the mu...
Contains fulltext : 84537.pdf (author's version ) (Open Access)Applications of Evo...
This is the accepted iPC deliverable D1.3 „Synthetic data for testing and training patient, cancer, ...
automatic reconstruction of cancer evolutionary trees from multi-region next generation sequencin
Heterogeneity is arguably one of the most important hallmarks of cancer which contributes to its dru...
Supplemental data for the paper titled "An unsupervised deep learning framework with variational aut...
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