Assessment of computational methods for predicting the effects of missense mutations in human cancers
Florian Gnad; Albion Baucom; Kiran Mukhyala; Gerard Manning; Zemin Zhang · 2013 · BMC Genomics
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
BACKGROUND: Recent advances in sequencing technologies have greatly increased the identification of mutations in cancer genomes. However, it remains a significant challenge to identify cancer-driving mutations, since most observed missense changes are neutral passenger mutations. Various computational methods have been developed to predict the effects of amino acid substitutions on protein function and classify mutations as deleterious or benign. These include approaches that rely on evolutionary conservation, structural constraints, or physicochemical attributes of amino acid substitutions. H
Abstract by Florian Gnad; Albion Baucom; Kiran Mukhyala; Gerard Manning; Zemin Zhang, BMC Genomics (2013) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1186/1471-2164-14-s3-s7