Clinical feature-related single-base substitution sequence signatures identified with an unsupervised machine learning approach
Abstract Background Mutation processes leave different signatures in genes. For single-base substitutions, previous studies have suggested that mutation signatures are not only reflected in mutation bases but also in neighboring bases. However, because of the lack of a method to identify features of...
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| Main Authors: | , , , , , , , , , |
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| Format: | Book |
| Published: |
BMC,
2021-12-01T00:00:00Z.
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| Subjects: | |
| Online Access: | Connect to this object online. |
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