Align-GVGD

• Sean V. Tavtigian <sean.tavtigian at hci.utah.edu>

International Agency for Research on Cancer Lyon, France; Department of Bioinformatics and Computational Biology, George Mason University Manassas, VA, USA; Department of Clinical Oncology, Institute of Development Aging and Cancer, Tohoku University Sendai 980-8575, Japan

• (Mathe et al., 2006) Computational approaches for predicting the biological effect of p53 missense mutations: a comparison of three sequence analysis based methods. Nucleic acids research.
  PMID: 16522644

• Animals
  • Homo sapiens

• (Thusberg et al., 2011) Performance of mutation pathogenicity prediction methods on missense variants. Human mutation.
  PMID: 21412949
• (Bendl et al., 2014) PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations. PLoS computational biology.
  PMID: 24453961
• (Martelotto et al., 2014) Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations. Genome biology.
  PMID: 25348012
• (Gonzalez-Perez et al., 2013) Computational approaches to identify functional genetic variants in cancer genomes. Nature methods.
  PMID: 23900255
• (Gnad et al., 2013) Assessment of computational methods for predicting the effects of missense mutations in human cancers. BMC genomics.
  PMID: 23819521