2022
DOI: 10.1101/2022.03.15.483816
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Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtyping

Abstract: Cancer genome sequencing enables accurate classification of tumours and tumour subtypes. However, prediction performance is still limited using exome-only sequencing and for tumor types with low somatic mutation burden such as many pediatric tumours. Moreover, the ability to leverage deep representation learning in discovery of tumour entities remains unknown. We introduce here Mutation-Attention (MuAt), a deep neural network to learn representations of simple and complex somatic alterations for prediction of … Show more

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Cited by 4 publications
(5 citation statements)
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References 64 publications
(104 reference statements)
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“…Although MuSiCal solves several problems in the standard workflow, other methods that perform specific tasks – such as joint discovery of different signature types [50], incorporation of epigenetic data [51], and utilization of clinical information [52] – are complementary and valuable additions to the signature analysis toolbox. There are also many other outstanding methodological challenges requiring future developments, which could potentially benefit from recent advances in machine learning and artificial intelligence [53].…”
Section: Discussionmentioning
confidence: 99%
“…Although MuSiCal solves several problems in the standard workflow, other methods that perform specific tasks – such as joint discovery of different signature types [50], incorporation of epigenetic data [51], and utilization of clinical information [52] – are complementary and valuable additions to the signature analysis toolbox. There are also many other outstanding methodological challenges requiring future developments, which could potentially benefit from recent advances in machine learning and artificial intelligence [53].…”
Section: Discussionmentioning
confidence: 99%
“…Machine learning is a rapidly evolving subfield of artificial intelligence, which utilises algorithms and statistical models to analyse and detect patterns in large and complex datasets to aid diagnosis. Machine learning approaches have been applied to digital pathology [38], RNA [39,40], NGS [41 & ,42] or WGS [43][44][45] results to help determine ToO (Table 3 ] developed a CUP prediction algorithm (CUPPA) based on orthogonal information of passenger and driver mutational features, regional mutational density and SNV-96 mutational profile utilising a cancer WGS database developed by the Hartwig Medical Foundation in the Netherlands. When CUPPA results were integrated in the diagnostic work-up, CUPPA could identify a primary tumour type for 49/ 72 CUP patients (68%).…”
Section: Prospective Clinical Trials Evaluating the Clinical Utility ...mentioning
confidence: 99%
“…Machine learning is a rapidly evolving subfield of artificial intelligence, which utilises algorithms and statistical models to analyse and detect patterns in large and complex datasets to aid diagnosis. Machine learning approaches have been applied to digital pathology [38], RNA [39,40], NGS [41 ▪ ,42] or WGS [43–45] results to help determine ToO (Table 3)…”
Section: Introductionmentioning
confidence: 99%
“…https://doi.org/10.1038/s41551-023-01120-3 histology of origin, and these features of somatic mutations have been shown to be capable of classifying cancers 17,[20][21][22] . We were interested in seeing how a model that calculates attention or learns it owns features compared to established approaches.…”
Section: Articlementioning
confidence: 99%