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DMCM: a Data-adaptive Mutation Clustering Method to identify cancer-related mutation clusters
Author(s) -
Xinguo Lu,
Xin Qian,
Xing Li,
Qiumai Miao,
Shaoliang Peng
Publication year - 2018
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bty624
Subject(s) - cluster analysis , mutation , adaptive mutation , computational biology , computer science , genetics , biology , artificial intelligence , machine learning , genetic algorithm , gene
Functional somatic mutations within coding amino acid sequences confer growth advantage in pathogenic process. Most existing methods for identifying cancer-related mutations focus on the single amino acid or the entire gene level. However, gain-of-function mutations often cluster in specific protein regions instead of existing independently in the amino acid sequences. Some approaches for identifying mutation clusters with mutation density on amino acid chain have been proposed recently. But their performance in identification of mutation clusters remains to be improved.

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