Atul Butte

Summary

Affiliation: Stanford University
Country: USA

Publications

  1. pmc Wiskott-Aldrich syndrome protein is an effector of Kit signaling
    Maheswaran Mani
    Division of Stem Cell Transplantation, Department of Pediatrics, Stanford University School of Medicine, CA 94305 5208, USA
    Blood 114:2900-8. 2009
  2. pmc Autoimmune disease classification by inverse association with SNP alleles
    Marina Sirota
    Stanford Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, California, USA
    PLoS Genet 5:e1000792. 2009
  3. pmc Disease signatures are robust across tissues and experiments
    Joel T Dudley
    Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
    Mol Syst Biol 5:307. 2009
  4. pmc Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets
    Silpa Suthram
    Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA
    PLoS Comput Biol 6:e1000662. 2010
  5. pmc Systematic evaluation of environmental factors: persistent pollutants and nutrients correlated with serum lipid levels
    Chirag J Patel
    Department of Pediatrics, Division of Systems Medicine, Stanford University School of Medicine, Stanford, CA, USA
    Int J Epidemiol 41:828-43. 2012
  6. pmc Multiplex meta-analysis of RNA expression to identify genes with variants associated with immune dysfunction
    Alexander A Morgan
    Biomedical Informatics Graduate Training Program and Division of Systems Medicine, Department of Pediatrics, Stanford University, Stanford, California 94305 5415, USA
    J Am Med Inform Assoc 19:284-8. 2012
  7. pmc Extreme evolutionary disparities seen in positive selection across seven complex diseases
    Erik Corona
    Lucile Packard Children s Hospital, Stanford, California, United States of America
    PLoS ONE 5:e12236. 2010
  8. pmc Data-driven integration of epidemiological and toxicological data to select candidate interacting genes and environmental factors in association with disease
    Chirag J Patel
    Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA
    Bioinformatics 28:i121-6. 2012
  9. pmc Differentially expressed RNA from public microarray data identifies serum protein biomarkers for cross-organ transplant rejection and other conditions
    Rong Chen
    Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States of America
    PLoS Comput Biol 6:. 2010
  10. pmc A common rejection module (CRM) for acute rejection across multiple organs identifies novel therapeutics for organ transplantation
    Purvesh Khatri
    Department of Pediatrics 2 Stanford Cardiovascular Institute 3 Department of Cardiothoracic Surgery 4 Stanford Center for Biomedical Informatics Research, Department of Medicine and 5 Institute for Immunity, Transplant, and Infection Stanford University, Stanford, CA 94305
    J Exp Med 210:2205-21. 2013

Collaborators

Detail Information

Publications73

  1. pmc Wiskott-Aldrich syndrome protein is an effector of Kit signaling
    Maheswaran Mani
    Division of Stem Cell Transplantation, Department of Pediatrics, Stanford University School of Medicine, CA 94305 5208, USA
    Blood 114:2900-8. 2009
    ....
  2. pmc Autoimmune disease classification by inverse association with SNP alleles
    Marina Sirota
    Stanford Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, California, USA
    PLoS Genet 5:e1000792. 2009
    ..While recognizing similarities between diseases might lead to identifying novel treatment options, detecting differences between diseases previously thought to be similar may point to key novel disease-specific genes and pathways...
  3. pmc Disease signatures are robust across tissues and experiments
    Joel T Dudley
    Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
    Mol Syst Biol 5:307. 2009
    ....
  4. pmc Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets
    Silpa Suthram
    Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA
    PLoS Comput Biol 6:e1000662. 2010
    ....
  5. pmc Systematic evaluation of environmental factors: persistent pollutants and nutrients correlated with serum lipid levels
    Chirag J Patel
    Department of Pediatrics, Division of Systems Medicine, Stanford University School of Medicine, Stanford, CA, USA
    Int J Epidemiol 41:828-43. 2012
    ....
  6. pmc Multiplex meta-analysis of RNA expression to identify genes with variants associated with immune dysfunction
    Alexander A Morgan
    Biomedical Informatics Graduate Training Program and Division of Systems Medicine, Department of Pediatrics, Stanford University, Stanford, California 94305 5415, USA
    J Am Med Inform Assoc 19:284-8. 2012
    ..We demonstrate a genome-wide method for the integration of many studies of gene expression of phenotypically similar disease processes, a method of multiplex meta-analysis. We use immune dysfunction as an example disease process...
  7. pmc Extreme evolutionary disparities seen in positive selection across seven complex diseases
    Erik Corona
    Lucile Packard Children s Hospital, Stanford, California, United States of America
    PLoS ONE 5:e12236. 2010
    ..These results inform the current understanding of disease etiology, shed light on potential benefits associated with the genetic-basis of disease, and aid in the efforts to identify causal genetic factors underlying complex disease...
  8. pmc Data-driven integration of epidemiological and toxicological data to select candidate interacting genes and environmental factors in association with disease
    Chirag J Patel
    Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA
    Bioinformatics 28:i121-6. 2012
    ..Next, we search for evidence of toxicological relationships between these genetic and environmental factors that may have an etiological role in the disease. We illustrate our method by selecting candidate interacting factors for T2D...
  9. pmc Differentially expressed RNA from public microarray data identifies serum protein biomarkers for cross-organ transplant rejection and other conditions
    Rong Chen
    Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States of America
    PLoS Comput Biol 6:. 2010
    ....
  10. pmc A common rejection module (CRM) for acute rejection across multiple organs identifies novel therapeutics for organ transplantation
    Purvesh Khatri
    Department of Pediatrics 2 Stanford Cardiovascular Institute 3 Department of Cardiothoracic Surgery 4 Stanford Center for Biomedical Informatics Research, Department of Medicine and 5 Institute for Immunity, Transplant, and Infection Stanford University, Stanford, CA 94305
    J Exp Med 210:2205-21. 2013
    ..In conclusion, we identified a CRM in transplantation that provides new opportunities for diagnosis, drug repositioning, and rational drug design. ..
  11. pmc Analysis of the genetic basis of disease in the context of worldwide human relationships and migration
    Erik Corona
    Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
    PLoS Genet 9:e1003447. 2013
    ..We anticipate that our findings will enable detailed analysis pertaining to the driving forces behind genetic risk differentiation...
  12. pmc Relating genes to function: identifying enriched transcription factors using the ENCODE ChIP-Seq significance tool
    Raymond K Auerbach
    Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, 1265 Welch Road, Room X 163 MS 5415, Stanford, CA 94305, USA
    Bioinformatics 29:1922-4. 2013
    ..We present the ENCODE ChIP-Seq Significance Tool, a flexible web application leveraging public ENCODE data to identify enriched transcription factors in a gene or transcript list for comparative analyses...
  13. pmc Type 2 diabetes risk alleles demonstrate extreme directional differentiation among human populations, compared to other diseases
    Rong Chen
    Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States of America
    PLoS Genet 8:e1002621. 2012
    ..Our results indicate that the differential frequencies of T2D risk alleles may contribute to the observed disparity in T2D incidence rates across ethnic populations...
  14. pmc Immune response profiling identifies autoantibodies specific to Moyamoya patients
    Tara K Sigdel
    California Pacific Medical Center Research Institute, San Francisco, CA 94107, USA
    Orphanet J Rare Dis 8:45. 2013
    ..Moyamoya Disease is typically diagnosed by angiography after clinical presentation of cerebral hemorrhage or ischemia. Despite unclear etiology, previous reports suggest there may be an immunological component...
  15. pmc Systematic identification of interaction effects between genome- and environment-wide associations in type 2 diabetes mellitus
    Chirag J Patel
    Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, 1265 Welch Road, Room X 163 MS 5415, Stanford, CA 94305, USA
    Hum Genet 132:495-508. 2013
    ..Unbiased consideration of environmental and genetic factors may help identify larger and more relevant effect sizes for disease associations...
  16. pmc Sequencing and analysis of a South Asian-Indian personal genome
    Ravi Gupta
    SciGenom Labs Pvt Ltd, Kakkanad, Cochin, Kerala, India
    BMC Genomics 13:440. 2012
    ..In this study we have sequenced and analyzed the genome of a South Asian Indian female (SAIF) from the Indian state of Kerala...
  17. pmc Altering physiological networks using drugs: steps towards personalized physiology
    Adam D Grossman
    Department of Bioengineering, Stanford University, Stanford, CA, USA
    BMC Med Genomics 6:S7. 2013
    ..We constructed correlation networks from physiologic data to demonstrate changes associated with pressor use in the intensive care unit...
  18. pmc Systematic identification of DNA variants associated with ultraviolet radiation using a novel Geographic-Wide Association Study (GeoWAS)
    Irving Hsu
    Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, 1265 Welch Road, MS 5415, Stanford, CA 94305, USA
    BMC Med Genet 14:62. 2013
    ..A systematic approach utilizing bioinformatics to identify associations among environmental variables, genetic variation, and diseases across various geographical locations is needed but has been lacking...
  19. doi Translational bioinformatics: data-driven drug discovery and development
    A J Butte
    1Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
    Clin Pharmacol Ther 91:949-52. 2012
    ..There are challenges, including lack of access to some data sources and software, but there are also overwhelming doses of hopes and expectations...
  20. pmc Integration of disease-specific single nucleotide polymorphisms, expression quantitative trait loci and coexpression networks reveal novel candidate genes for type 2 diabetes
    H P Kang
    Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, 1265 Welch Road, Room X163, Stanford, CA 94305, USA
    Diabetologia 55:2205-13. 2012
    ..We hypothesised that integrating SNPs known to be associated with type 2 diabetes with eQTLs and coexpression networks would enable the discovery of novel candidate genes for type 2 diabetes...
  21. pmc Likelihood ratios for genome medicine
    Alexander A Morgan
    Department of Pediatrics and the Department of Medicine, Stanford University School of Medicine, 251 Campus Drive, MS 5415, Stanford, CA 94305 5479, USA
    Genome Med 2:30. 2010
    ..By using well-established methods of evidence based medicine, these very many parallel tests may be combined using likelihood ratios to report a post-test probability of disease for use in patient assessment...
  22. pmc Translational bioinformatics in the cloud: an affordable alternative
    Joel T Dudley
    Program in Biomedical Informatics, Stanford University School of Medicine, 251 Campus Drive, Stanford, CA 94305, USA
    Genome Med 2:51. 2010
    ....
  23. pmc FitSNPs: highly differentially expressed genes are more likely to have variants associated with disease
    Rong Chen
    Stanford Center for Biomedical Informatics Research, 251 Cmpus Drive, Stanford, CA 94305, USA
    Genome Biol 9:R170. 2008
    ..We propose to use the more than 200,000 microarray studies in the Gene Expression Omnibus to systematically prioritize candidate SNPs from GWASs...
  24. pmc Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis
    Mitchell J Cohen
    Department of Surgery, University of California, 505 Parnassus Avenue, San Francisco, CA 94143, USA
    Crit Care 14:R10. 2010
    ..We hypothesized that processing of multivariate data using hierarchical clustering techniques would allow identification of otherwise hidden patient physiologic patterns that would be predictive of outcome...
  25. pmc Predicting environmental chemical factors associated with disease-related gene expression data
    Chirag J Patel
    Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA
    BMC Med Genomics 3:17. 2010
    ..Integrating freely-available environment-gene interaction and disease phenotype data would allow hypothesis generation for potential environmental associations to disease...
  26. pmc Computational prediction and experimental validation associating FABP-1 and pancreatic adenocarcinoma with diabetes
    Ravi N Sharaf
    Department of Gastroenterology and Hepatology, Stanford University School of Medicine, Alway Building, Room M211, 300 Pasteur Drive, MC 5187, Stanford, CA 94305 5187, USA
    BMC Gastroenterol 11:5. 2011
    ..We identified fatty acid binding protein-1 (FABP-1) as one of several candidates. The primary aim of this pilot study was to experimentally validate the predicted association between FABP-1 with PaC and PaC with diabetes...
  27. pmc Systematic survey reveals general applicability of "guilt-by-association" within gene coexpression networks
    Cecily J Wolfe
    Children s Hospital Informatics Program and Harvard MIT Division of Health Sciences and Technology, 300 Longwood Avenue, Boston, MA 02115, USA
    BMC Bioinformatics 6:227. 2005
    ..This has informed the guilt-by-association (GBA) heuristic, widely invoked in functional genomics. Yet although the idea of GBA is accepted, the breadth of GBA applicability is uncertain...
  28. pmc GeneChaser: identifying all biological and clinical conditions in which genes of interest are differentially expressed
    Rong Chen
    Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
    BMC Bioinformatics 9:548. 2008
    ....
  29. pmc The "etiome": identification and clustering of human disease etiological factors
    Yueyi I Liu
    Stanford Medical Informatics, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
    BMC Bioinformatics 10:S14. 2009
    ..Though genetic contributions are relatively well characterized for some monogenetic diseases, there has been no effort at curating the extensive list of environmental etiological factors...
  30. pmc Infection in the intensive care unit alters physiological networks
    Adam D Grossman
    Department of Bioengineering, Stanford University, Stanford, California 94305, USA
    BMC Bioinformatics 10:S4. 2009
    ..However, medical sources of clinical physiological data are only now starting to find use in bioinformatics research...
  31. pmc Latent physiological factors of complex human diseases revealed by independent component analysis of clinarrays
    David P Chen
    Program in Biomedical Informatics, Stanford University School of Medicine, Stanford, CA 94305, USA
    BMC Bioinformatics 11:S4. 2010
    ..Knowledge of these factors could be used to improve assessment of disease severity and help to refine strategies for diagnosis and monitoring disease progression...
  32. pmc Content-based microarray search using differential expression profiles
    Jesse M Engreitz
    Department of Bioengineering, Stanford University School of Medicine, CA, USA
    BMC Bioinformatics 11:603. 2010
    ....
  33. pmc Creation and implications of a phenome-genome network
    Atul J Butte
    Stanford Medical Informatics, Department of Medicine, Stanford University School of Medicine, 251 Campus Drive, Room X 215, Stanford, California 94305 5479, USA
    Nat Biotechnol 24:55-62. 2006
    ..We identify novel genes related to concepts such as aging. Comprehensively identifying genes related to phenotype and environment is a step toward the Human Phenome Project...
  34. pmc Translational bioinformatics: coming of age
    Atul J Butte
    Stanford Center for Biomedical Informatics, Department of Medicine and Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
    J Am Med Inform Assoc 15:709-14. 2008
    ..I end with the significant challenges we face in building a community of future investigators in Translational Bioinformatics...
  35. pmc Protein microarrays discover angiotensinogen and PRKRIP1 as novel targets for autoantibodies in chronic renal disease
    Atul J Butte
    Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA
    Mol Cell Proteomics 10:M110.000497. 2011
    ....
  36. pmc Medicine. The ultimate model organism
    Atul J Butte
    Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
    Science 320:325-7. 2008
  37. pmc Translational bioinformatics applications in genome medicine
    Atul J Butte
    Stanford Center for Biomedical Informatics, Department of Medicine and Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA, and Lucile Packard Children s Hospital, Palo Alto, CA 94304, USA
    Genome Med 1:64. 2009
    ..I also list four recommendations for bioinformaticians wishing to get more involved in translational research...
  38. pmc A Classifier-based approach to identify genetic similarities between diseases
    Marc A Schaub
    Department of Computer Science, Stanford University, Stanford, CA 94305, USA
    Bioinformatics 25:i21-9. 2009
    ..We repeat these classification and comparison steps so that each disease is used once as reference disease...
  39. pmc Expression of complement components differs between kidney allografts from living and deceased donors
    Maarten Naesens
    Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA
    J Am Soc Nephrol 20:1839-51. 2009
    ..In addition, complement gene expression at the time of implantation was associated with both early and late graft function. These data suggest that complement-modulating therapy may improve graft outcomes in renal transplantation...
  40. pmc Non-synonymous and synonymous coding SNPs show similar likelihood and effect size of human disease association
    Rong Chen
    Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States of America
    PLoS ONE 5:e13574. 2010
    ..Our results suggest that sSNPs are just as likely to be involved in disease mechanisms, so we recommend that sSNPs discovered from GWAS should also be examined with functional studies...
  41. pmc Clinical assessment incorporating a personal genome
    Euan A Ashley
    Center for Inherited Cardiovascular Disease, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
    Lancet 375:1525-35. 2010
    ..The cost of genomic information has fallen steeply, but the clinical translation of genetic risk estimates remains unclear. We aimed to undertake an integrated analysis of a complete human genome in a clinical context...
  42. pmc An integrative method for scoring candidate genes from association studies: application to warfarin dosing
    Nicholas P Tatonetti
    Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA, USA
    BMC Bioinformatics 11:S9. 2010
    ..We then define a summary score for each gene based on allele frequencies and train linear and logistic regression classifiers to predict drug response phenotypes...
  43. doi Drug discovery in a multidimensional world: systems, patterns, and networks
    Joel T Dudley
    Program in Biomedical Informatics, Stanford University School of Medicine, Stanford, CA, USA
    J Cardiovasc Transl Res 3:438-47. 2010
    ..When available, specific applications to cardiovascular drug discovery are highlighted and discussed...
  44. pmc Cell type-specific gene expression differences in complex tissues
    Shai S Shen-Orr
    Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
    Nat Methods 7:287-9. 2010
    ....
  45. pmc Use of Bayesian networks to probabilistically model and improve the likelihood of validation of microarray findings by RT-PCR
    Sangeeta B English
    Stanford Center for Biomedical Informatics Research BMIR, Stanford University School of Medicine, 251 Campus Drive, Stanford, CA 94305, USA
    J Biomed Inform 42:287-95. 2009
    ..In conclusion, in this study, we have successfully added a new automated step to determine the contributory sources of noise that determine successful or unsuccessful downstream biological validation...
  46. pmc Ontology-driven indexing of public datasets for translational bioinformatics
    Nigam H Shah
    Centre for Biomedical Informatics, School of Medicine, Stanford University, Stanford, CA 94305, USA
    BMC Bioinformatics 10:S1. 2009
    ..The key functionality of this system is to enable users to locate biomedical data resources related to particular ontology concepts...
  47. doi Protein microarrays identify antibodies to protein kinase Czeta that are associated with a greater risk of allograft loss in pediatric renal transplant recipients
    Scott M Sutherland
    Division of Nephrology, Department of Pediatrics, Stanford University Medical Center, Stanford, CA 94305, USA
    Kidney Int 76:1277-83. 2009
    ..Prospective assessment of serum anti-PKCzeta levels at allograft rejection will be needed to confirm these results...
  48. pmc MicroRNA profiling of human-induced pluripotent stem cells
    Kitchener D Wilson
    Department of Bioengineering, Stanford University School of Medicine, Stanford, CA 94305, USA
    Stem Cells Dev 18:749-58. 2009
    ....
  49. pmc Dynamism in gene expression across multiple studies
    Alexander A Morgan
    Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA
    Physiol Genomics 40:128-40. 2010
    ....
  50. pmc Evaluation and integration of 49 genome-wide experiments and the prediction of previously unknown obesity-related genes
    Sangeeta B English
    Department of Medicine, Stanford Medical Informatics, Stanford University School of Medicine, Lucile Packard Children s Hospital, Stanford, CA 94305, USA
    Bioinformatics 23:2910-7. 2007
    ..The approach described here can include any number and type of genome-wide experiments and might be useful for other complex polygenic disorders as well...
  51. pmc Relationship of differential gene expression profiles in CD34+ myelodysplastic syndrome marrow cells to disease subtype and progression
    Kunju Sridhar
    Hematology Division, Stanford University Medical Center, 875 Blake Wilbur Drive, Stanford, CA 94305, USA
    Blood 114:4847-58. 2009
    ....
  52. pmc Identifying compartment-specific non-HLA targets after renal transplantation by integrating transcriptome and "antibodyome" measures
    Li Li
    Department of Pediatrics, Blood and Marrow Transplantation Division, Stanford University, 300 Pasteur Drive, Stanford, CA 94304, USA
    Proc Natl Acad Sci U S A 106:4148-53. 2009
    ..Correlation of the most significant non-HLA antibody responses with transplant health and dysfunction are currently underway...
  53. ncbi Analysis of matched mRNA measurements from two different microarray technologies
    Winston Patrick Kuo
    Children s Hospital Informatics Program and Division of Endocrinology, Department of Medicine, Children s Hospital, Brigham and Women s Hospital, Harvard Medical School, Boston, MA 02115, USA
    Bioinformatics 18:405-12. 2002
    ..Cross-platform utilization of data from different technologies has the potential to reduce the need to duplicate experiments but requires corresponding measurements to be comparable...
  54. pmc Validating pathophysiological models of aging using clinical electronic medical records
    David P Chen
    Center for Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
    J Biomed Inform 43:358-64. 2010
    ..The integration of both epidemiological and clinical data promises to create more robust models that shed new light on physiological processes...
  55. pmc Hematopoietic stem cell quiescence is maintained by compound contributions of the retinoblastoma gene family
    Patrick Viatour
    Department of Pediatrics, Stanford Medical School, Stanford, CA 94305, USA
    Cell Stem Cell 3:416-28. 2008
    ..The presence of a single p107 allele is sufficient to largely rescue these defects. Thus, Rb family members collectively maintain HSC quiescence and the balance between lymphoid and myeloid cell fates in the hematopoietic system...
  56. pmc Exploiting drug-disease relationships for computational drug repositioning
    Joel T Dudley
    Stanford University, Stanford, CA, USA
    Brief Bioinform 12:303-11. 2011
    ..Newer algorithms for computational drug repositioning will likely span these two axes, will take advantage of newer types of molecular measurements, and will certainly play a role in reducing the global burden of disease...
  57. pmc Antibodies specifically target AML antigen NuSAP1 after allogeneic bone marrow transplantation
    Persis P Wadia
    Department of Medicine, Stanford University Medical Center, CA 94305 5623, USA
    Blood 115:2077-87. 2010
    ..Thus, NuSAP1 is recognized as an immunogenic antigen in 65% of patients with AML following allogeneic HCT and suggests a tumor antigen role...
  58. pmc Tissue- and age-specific changes in gene expression during disease induction and progression in NOD mice
    Keiichi Kodama
    Department of Medicine, Division of Immunology and Rheumatology, Stanford University School of Medicine, 269 West Campus Drive, Stanford, CA 94305, USA
    Clin Immunol 129:195-201. 2008
    ..These studies identified tissue- and age-specific changes in gene expression that may play an important role in the inductive or destructive events of T1D...
  59. pmc An Environment-Wide Association Study (EWAS) on type 2 diabetes mellitus
    Chirag J Patel
    Department of Pediatrics and Medicine, Stanford University School of Medicine, Stanford, California, USA
    PLoS ONE 5:e10746. 2010
    ..We conducted a pilot Environmental-Wide Association Study (EWAS), in which epidemiological data are comprehensively and systematically interpreted in a manner analogous to a Genome Wide Association Study (GWAS)...
  60. ncbi Multiplexed protein array platforms for analysis of autoimmune diseases
    Imelda Balboni
    Department of Medicine, Division of Immunology and Rheumatology, Stanford University School of Medicine, Stanford, California 94305, USA
    Annu Rev Immunol 24:391-418. 2006
    ..We conclude by reviewing advances in biomedical informatics that will eventually allow the human proteome to be deciphered...
  61. pmc FoxO3 regulates neural stem cell homeostasis
    Valérie M Renault
    Department of Genetics, Stanford University, CA 94305, USA
    Cell Stem Cell 5:527-39. 2009
    ..The ability of FoxO3 to prevent the premature depletion of NSCs might have important implications for counteracting brain aging in long-lived species...
  62. pmc Coordinated reduction of genes of oxidative metabolism in humans with insulin resistance and diabetes: Potential role of PGC1 and NRF1
    Mary Elizabeth Patti
    Research Division, Joslin Diabetes Center, Boston, MA 02215, USA
    Proc Natl Acad Sci U S A 100:8466-71. 2003
    ..Decreased PGC1 expression may be responsible for decreased expression of NRF-dependent genes, leading to the metabolic disturbances characteristic of insulin resistance and DM...
  63. pmc Reproducibility of gene expression across generations of Affymetrix microarrays
    Ashish Nimgaonkar
    Informatics Program, Children s Hospital, Harvard Medical School, Boston, MA, USA
    BMC Bioinformatics 4:27. 2003
    ..In this study the reproducibility of gene expression levels across two generations of Affymetrix GeneChips (HuGeneFL and HG-U95A) was measured...
  64. ncbi PGAGENE: integrating quantitative gene-specific results from the NHLBI programs for genomic applications
    Kyungjoon Lee
    Children s Hospital Informatics Program, 300 Longwood Ave, Boston, MA 02115, USA
    Bioinformatics 19:778-9. 2003
    ..The PGAGENE indexing agent periodically maps all publicly available gene-specific PGA data onto LocusLink using dynamically generated cross-referencing tables...
  65. ncbi Comparing expression profiles of genes with similar promoter regions
    Peter J Park
    Informatics Program and Division of Endocrinology, Children s Hospital, 300 Longwood Avenue, Boston, MA 02115, USA
    Bioinformatics 18:1576-84. 2002
    ..We consider the problem in the opposite direction: we seek to find the genes that have similar promoter regions and determine the extent to which these genes have similar expression profiles...
  66. pmc Quantifying the relationship between co-expression, co-regulation and gene function
    Dominic J Allocco
    Informatics Program, Children s Hospital, Boston, MA, USA
    BMC Bioinformatics 5:18. 2004
    ....
  67. pmc Conserved mechanisms across development and tumorigenesis revealed by a mouse development perspective of human cancers
    Alvin T Kho
    Children s Hospital Informatics Program, Children s Hospital Boston, MA 02115, USA
    Genes Dev 18:629-40. 2004
    ....
  68. ncbi Genome-scale expression profiling of Hutchinson-Gilford progeria syndrome reveals widespread transcriptional misregulation leading to mesodermal/mesenchymal defects and accelerated atherosclerosis
    Antonei B Csoka
    Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI 02912, USA
    Aging Cell 3:235-43. 2004
    ..The identification of a large number of genes implicated in atherosclerosis is especially valuable, because it provides clues to pathological processes that can now be investigated in HGPS patients or animal models...
  69. ncbi A computational model to define the molecular causes of type 2 diabetes mellitus
    Jack Pollard
    Genstruct, Inc, Cambridge, Massachusetts 02140, USA
    Diabetes Technol Ther 7:323-36. 2005
    ..Whether these reductions are a consequence or a cause of impaired insulin sensitivity remains an open question...
  70. ncbi Genome-wide analysis of host responses to the Pseudomonas aeruginosa type III secretion system yields synergistic effects
    Jeffrey K Ichikawa
    Department of Microbiology and Molecular Genetics, Harvard Medical School, Boston, MA 02115, USA
    Cell Microbiol 7:1635-46. 2005
    ..This study shows that the individual components of the TTSS define an integrated system and that a systems biology approach is required to fully understand the complex interplay between pathogen and host...
  71. pmc AILUN: reannotating gene expression data automatically
    Rong Chen
    Nat Methods 4:879. 2007
  72. ncbi Prediction of preadipocyte differentiation by gene expression reveals role of insulin receptor substrates and necdin
    Yu Hua Tseng
    Research Division, Joslin Diabetes Center, Children s Hospital, Harvard Medical School, Boston, MA 02215, USA
    Nat Cell Biol 7:601-11. 2005
    ..Together these define a key signalling network that is involved in brown preadipocyte determination...

Research Grants8

  1. Enabling new discoveries in pharmacogenomics through a genomic date-driven nosolo
    Atul Butte; Fiscal Year: 2006
    ....
  2. Enabling new discoveries in pharmacogenomics through a genomic date-driven nosolo
    Atul Butte; Fiscal Year: 2007
    ....
  3. Enabling new discoveries in pharmacogenomics through a genomic date-driven nosolo
    Atul Butte; Fiscal Year: 2009
    ....
  4. Integrating Microarray and Proteomic Data by Ontology-based Annotation
    Atul Butte; Fiscal Year: 2009
    ..Through our advisory committee of world-renowned NIH-funded investigators, we will ensure that our findings will have broad applicability and are useful to a wide variety of biomedical researchers. ..
  5. Enabling new discoveries in pharmacogenomics through a genomic date-driven nosolo
    Atul J Butte; Fiscal Year: 2010
    ....
  6. Integrating Microarray and Proteomic Data by Ontology-based Annotation
    Atul J Butte; Fiscal Year: 2010
    ..Through our advisory committee of world-renowned NIH-funded investigators, we will ensure that our findings will have broad applicability and are useful to a wide variety of biomedical researchers. ..