Emidio Capriotti

Summary

Affiliation: Stanford University
Country: USA

Publications

  1. pmc A new disease-specific machine learning approach for the prediction of cancer-causing missense variants
    Emidio Capriotti
    Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
    Genomics 98:310-7. 2011
  2. pmc Improving the prediction of disease-related variants using protein three-dimensional structure
    Emidio Capriotti
    Department of Bioengineering, Stanford University, Stanford, CA, USA
    BMC Bioinformatics 12:S3. 2011
  3. pmc Bioinformatics and variability in drug response: a protein structural perspective
    Jennifer L Lahti
    Department of Bioengineering, Stanford University, Stanford, CA, USA
    J R Soc Interface 9:1409-37. 2012
  4. pmc Phased whole-genome genetic risk in a family quartet using a major allele reference sequence
    Frederick E Dewey
    Center for Inherited Cardiovascular Disease, Division of Cardiovascular Medicine, Stanford University, Stanford, California, USA
    PLoS Genet 7:e1002280. 2011
  5. ncbi request reprint Comparative modeling: the state of the art and protein drug target structure prediction
    Tianyun Liu
    Department of Bioengineering, Stanford University, 318 Campus Dr, Room S240 Mail code 5448, Stanford, CA 94305, USA
    Comb Chem High Throughput Screen 14:532-47. 2011
  6. pmc Bioinformatics challenges for personalized medicine
    Guy Haskin Fernald
    Biomedical Informatics Training Program, Stanford University School of Medicine, Department of Bioengineering, Stanford University, Stanford, CA, USA
    Bioinformatics 27:1741-8. 2011

Collaborators

  • Tianyun Liu
  • Sean P David
  • Michael Snyder
  • Rong Chen
  • Joshua W Knowles
  • Heidi L Rehm
  • Russ B Altman
  • Jennifer L Lahti
  • Guy Haskin Fernald
  • Frederick E Dewey
  • Konrad J Karczewski
  • Grace W Tang
  • Joan M Hebert
  • Alexander W Zaranek
  • Jake K Byrnes
  • Matthew T Wheeler
  • Mark Woon
  • Joel T Dudley
  • Sergio P Cordero
  • Caroline F Thorn
  • Teri E Klein
  • Atul J Butte
  • Roxana Daneshjou
  • Aleksandra Pavlovic
  • Anne West
  • Kelly E Ormond
  • Omar E Cornejo
  • Joseph V Thakuria
  • Colleen Caleshu
  • Li Gong
  • George M Church
  • Michelle Whirl-Carrillo
  • John S West
  • Madeleine P Ball
  • Katrin Sangkuhl
  • Carlos D Bustamante
  • Euan A Ashley

Detail Information

Publications6

  1. pmc A new disease-specific machine learning approach for the prediction of cancer-causing missense variants
    Emidio Capriotti
    Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
    Genomics 98:310-7. 2011
    ..86, and area under ROC curve of 0.98. When compared with other previously developed algorithms such as SIFT and CHASM our method results in higher prediction accuracy and correlation coefficient in identifying cancer-causing variants...
  2. pmc Improving the prediction of disease-related variants using protein three-dimensional structure
    Emidio Capriotti
    Department of Bioengineering, Stanford University, Stanford, CA, USA
    BMC Bioinformatics 12:S3. 2011
    ..Although sequence-based predictors have shown good performance, the quality of these predictions can be further improved by introducing new features derived from three-dimensional protein structures...
  3. pmc Bioinformatics and variability in drug response: a protein structural perspective
    Jennifer L Lahti
    Department of Bioengineering, Stanford University, Stanford, CA, USA
    J R Soc Interface 9:1409-37. 2012
    ..Finally, we highlight tools for analysing protein structures and protein-drug interactions and discuss their application for understanding altered drug responses associated with protein structural variants...
  4. pmc Phased whole-genome genetic risk in a family quartet using a major allele reference sequence
    Frederick E Dewey
    Center for Inherited Cardiovascular Disease, Division of Cardiovascular Medicine, Stanford University, Stanford, California, USA
    PLoS Genet 7:e1002280. 2011
    ..These ethnicity-specific, family-based approaches to interpretation of genetic variation are emblematic of the next generation of genetic risk assessment using whole-genome sequencing...
  5. ncbi request reprint Comparative modeling: the state of the art and protein drug target structure prediction
    Tianyun Liu
    Department of Bioengineering, Stanford University, 318 Campus Dr, Room S240 Mail code 5448, Stanford, CA 94305, USA
    Comb Chem High Throughput Screen 14:532-47. 2011
    ..Finally, we discuss relevant applications in the prediction of important drug target proteins, focusing on the G protein-coupled receptor (GPCR) and protein kinase families...
  6. pmc Bioinformatics challenges for personalized medicine
    Guy Haskin Fernald
    Biomedical Informatics Training Program, Stanford University School of Medicine, Department of Bioengineering, Stanford University, Stanford, CA, USA
    Bioinformatics 27:1741-8. 2011
    ..Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics...