Publications

Recent Publications and Commentaries by Sage Bionetworks Scientists and Collaborators.

• Developing Predictive Molecular Maps of Human Disease through Community-based Modeling
  Derry J., Mangravite L., Suver C., Furia M., Henderson D., Schildwachter X., Izant JG., Sieberts SK., Kellen MR. and Friend SH. Nature Precedings 2011: doi:10.1038/npre

• Impact of gene expression noise on organismal fitness and the efficacy of natural selection
  Wang Z. and Zhang J. Proc Natl Acad Sci U S A. 2011. Apr 19;108(16):E67-76.

• The precompetitive Space: Time to Move the Yardsticks
  Norman, T., Edwards A.., Bountra C.and Friend SH. Sci Transl Med 2011: Vol. 3, Issue 76, p. 76

• Biomedical technology and the clinic of the future- POINT: Are we prepared for the future doctor visit?
  Friend, SH. and Ideker T. Nat Biotechnol. 2011 Mar;29(3):215

• Predictive, personalized, preventative, participatory cancer medicine
  Hood L. and Friend SH. Nature Reviews Clinical Oncology 2011. 8, 184-187

• An accelerated pathway for targeted cancer therapies
  McClellan M., Benner J., Schilsky R., Epstein D., Woosley R., Friend SH., Sidransky D., Geoghegan C. and Kessler D. Nature Reviews Drug Discovery 2011 10:1-2

• Forkhead Transcription Factor Foxq1 Promotes Epithelial–Mesenchymal Transition and Breast Cancer Metastasis Zhang H., et al. Cancer Res. 2011 Feb 15;71(4):1292-301

• Tumor-derived Jagged1 Promotes Osteolytic Bone Metastasis of Breast Cancer by Engaging Notch Signaling in Bone Cells Sethi N., Dai X., Winter C. and Kang Y. Cancer Cell 2011. 19, 1–14

• Identification of Genes and Networks Driving Cardiovascular and Metabolic Phenotypes in a Mouse F2 Intercross Derry J., et al. PLoS One. 2010 Dec 14;5(12):e14319

• A 10-Gene Progenitor Cell Signature Predicts Poor Prognosis in Lung Adenocarcnioma
  Onaitis M., Rawlins E., D’Amico TA., Guinney J., Harpole D. and Hogan B. Ann Thorac Surg. 2011 Apr;91(4):1046-50

• Estimating variable structure and dependence in multitask learning via gradients
  Guinney J, Wu Q., Mukherjee, S. Machine Learning 2011. 83; 265-287

• Bayesian Models for Detecting Epistatic Interactions from Genetic Data.
  Zhang Y, Jiang B, Zhu J, Liu JS. Ann Hum Genet. 2010 Nov 22. doi: 10.1111

• Opening Up to Precompetitive Collaboration
  Altshuler, JS. et al. Sci Transl Med 2010 Vol. 2, Issue 52, p. 52

• An Integrative Multi-Network and Multi-Classifier Approach to Predict Genetic Interactions
  Pandey G., Zhang B., Aaron N., Chang N., Myers CL., Zhu J., Kumar V., and Schadt EE. PLoS Comput Biol. 2010 September; 6(9): e1000928.

• Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence
  Wu Q., Guinney J., Maggioni M., and Mukherjee S. J. Machine Learning Research 11(Aug):2175−2198, 2010.

• Identification and validation of genes affecting aortic lesions in mice
  Yang X. et al. J Clin Invest. 2010;120(7):2414–2422. doi:10.1172/JCI42742.

• Something in Common
  Friend, SH. Sci Transl Med 2010 2, Issue 40, p. 40

• Systematic genetic and genomic analysis of cytochrome P450 enzyme activities in human liver.
  Yang X. et al. Genome Res. 2010 Aug;20(8):1020-36. Epub 2010 Jun 10.

• Liver and adipose expression associated SNPs are enriched for association to type 2 diabetes.
  Zhong H. et al. PLoS Genet. 2010 May 6;6:e1000932.

• A Bayesian Partition Method for Detecting Pleiotropic and Epistatic eQTL Module.
  Zhang W., Zhu J., Schadt EE., Liu J. PLoS Computational Biology 2010. 6: 1-10.

• Simultaneous Clustering of Multiple Gene Expression and Physical Interaction Datasets.
  Narayanan M., Vetta A., Schadt EE., Zhu J. PLoS Comput Biol. 2010. 6(4): e1000742.

• The need for precompetitive integrative bionetwork disease model building.
  Friend SH. Clin Pharmacol Ther. 2010 May;87(5):536-9.

• Assessing the prospects of genome-wide association studies performed in inbred mice.
  Su WL., Sieberts SK., Kleinhanz RR., Lux K., Millstein J., Molony C., Schadt EE. Mamm. Genome. 2010. 21(3-4):143-52.

• Characterizing Dynamic Changes in the Human Blood Transcriptional Network.
  Zhu J., Chen Y., Leonardson A., Wang K., Lamb JR., Emilsson V. and Schadt EE. PLoS Computational Biology 2010. 6(2).

• Integrating Pathway Analysis and Genetics of Gene Expression for Genome-wide Association Studies.
  Zhong J., Yang X., Kaplan L., Molony C., Schadt, EE. Am. Journal. Hum. 2010. 86(4):581-91.

• Molecular Networks as sensors and drivers of common human diseases.
  Schadt EE. Nature 2009. 461: 218-223.

• A network view of disease and compound screening.
  Schadt EE., Friend S., Shaywitz D. Nat Rev Drug Discov. 2009. 8(4):286-95.

• Meta-analysis of Inter-species Liver Co-expression Networks Elucidates Traits Associated with Common Human Diseases. Wang K, Narayanan M, Zhong H, Tompa M, Schadt EE, Zhu J.. PLoS Comput Biol. 2008. 5(12): e1000616.

• Validation of Candidate Causal Genes for Obesity Which Affect Shared Metabolic Pathways and Networks.
  Yang X., et al., Nature Genetics 2009. 41, 415-423.

• Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks.
  Zhu J., et al., Nat Genet. 2008. 40(7):854-61.

• Mapping the genetic architecture of gene expression in human liver.
  Schadt EE., et al., PLoS Biol. 2008. 6(5):e107.

• Genetics of gene expression and its effect on disease.
  Emilsson V., et al., Nature. 2008. 452(7186):423-8.

• Variations in DNA elucidate molecular networks that cause disease.
  Chen Y., et al., Nature. 2008. 452(7186):429-35.

 

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