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...
Saved in:
Main Authors: | , , , , , , , , , |
---|---|
Format: | Book |
Published: |
BMC,
2021-12-01T00:00:00Z.
|
Subjects: | |
Online Access: | Connect to this object online. |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Internet
Connect to this object online.3rd Floor Main Library
Call Number: |
A1234.567 |
---|---|
Copy 1 | Available |