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Volume 10 Supplement 9

Proceedings of the 2009 AMIA Summit on Translational Bioinformatics

Proceedings

2009 AMIA Summit on Translational Bioinformatics.

San Francisco, CA, USA15-17 March 2009

  1. Gene interactions play a central role in transcriptional networks. Many studies have performed genome-wide expression analysis to reconstruct regulatory networks to investigate disease processes. Since biologi...

    Authors: Hsun-Hsien Chang and Marco F Ramoni
    Citation: BMC Bioinformatics 2009 10(Suppl 9):S1
  2. Identifying disease causing genes and understanding their molecular mechanisms are essential to developing effective therapeutics. Thus, several computational methods have been proposed to prioritize candidate...

    Authors: Eunjung Lee, Hyunchul Jung, Predrag Radivojac, Jong-Won Kim and Doheon Lee
    Citation: BMC Bioinformatics 2009 10(Suppl 9):S2
  3. Chronic renal diseases are currently classified based on morphological similarities such as whether they produce predominantly inflammatory or non-inflammatory responses. However, such classifications do not r...

    Authors: Suresh K Bhavnani, Felix Eichinger, Sebastian Martini, Paul Saxman, HV Jagadish and Matthias Kretzler
    Citation: BMC Bioinformatics 2009 10(Suppl 9):S3
  4. Physicians use clinical and physiological data to treat patients every day, and it is essential for treating a patient appropriately. However, medical sources of clinical physiological data are only now starti...

    Authors: Adam D Grossman, Mitchell J Cohen, Geoffrey T Manley and Atul J Butte
    Citation: BMC Bioinformatics 2009 10(Suppl 9):S4
  5. The osteocyte is a type of cell that appears to be one of the key endocrine regulators of bone metabolism and a key responder to initiate bone formation and remodeling. Identifying the regulatory networks in o...

    Authors: Angela K Dean, Stephen E Harris, Ivo Kalajzic and Jianhua Ruan
    Citation: BMC Bioinformatics 2009 10(Suppl 9):S5
  6. Current outcome predictors based on "molecular profiling" rely on gene lists selected without consideration for their molecular mechanisms. This study was designed to demonstrate that we could learn about gene...

    Authors: Xinan Yang, Yong Huang, James L Chen, Jianming Xie, Xiao Sun and Yves A Lussier
    Citation: BMC Bioinformatics 2009 10(Suppl 9):S6
  7. The decision environment for cancer care is becoming increasingly complex due to the discovery and development of novel genomic tests that offer information regarding therapy response, prognosis and monitoring...

    Authors: Angel Janevski, Sitharthan Kamalakaran, Nilanjana Banerjee, Vinay Varadan and Nevenka Dimitrova
    Citation: BMC Bioinformatics 2009 10(Suppl 9):S7
  8. Knowledge gained in studies of genetic disorders is reported in a growing body of biomedical literature containing reports of genetic variation in individuals that map to medical conditions and/or response to ...

    Authors: Casey Lynnette Overby, Peter Tarczy-Hornoch and Dina Demner-Fushman
    Citation: BMC Bioinformatics 2009 10(Suppl 9):S8
  9. Large repositories of biomedical research data are most useful to translational researchers if their data can be aggregated for efficient queries and analyses. However, inconsistent or non-existent annotations...

    Authors: Erik Pitzer, Ronilda Lacson, Christian Hinske, Jihoon Kim, Pedro AF Galante and Lucila Ohno-Machado
    Citation: BMC Bioinformatics 2009 10(Suppl 9):S9
  10. This study describes a large-scale manual re-annotation of data samples in the Gene Expression Omnibus (GEO), using variables and values derived from the National Cancer Institute thesaurus. A framework is des...

    Authors: Ronilda Lacson, Erik Pitzer, Christian Hinske, Pedro Galante and Lucila Ohno-Machado
    Citation: BMC Bioinformatics 2009 10(Suppl 9):S10
  11. Many common and chronic diseases are influenced at some level by genetic variation. Research done in population genetics, specifically in the area of single nucleotide polymorphisms (SNPs) is critical to under...

    Authors: Terry H Shen, Christopher S Carlson and Peter Tarczy-Hornoch
    Citation: BMC Bioinformatics 2009 10(Suppl 9):S11
  12. Natural Language Processing (NLP) systems can be used for specific Information Extraction (IE) tasks such as extracting phenotypic data from the electronic medical record (EMR). These data are useful for trans...

    Authors: Brett R South, Shuying Shen, Makoto Jones, Jennifer Garvin, Matthew H Samore, Wendy W Chapman and Adi V Gundlapalli
    Citation: BMC Bioinformatics 2009 10(Suppl 9):S12
  13. The availability of up-to-date, executable, evidence-based medical knowledge is essential for many clinical applications, such as pharmacovigilance, but executable knowledge is costly to obtain and update. Aut...

    Authors: Xiaoyan Wang, George Hripcsak and Carol Friedman
    Citation: BMC Bioinformatics 2009 10(Suppl 9):S13
  14. The National Center for Biomedical Ontology (NCBO) is developing a system for automated, ontology-based access to online biomedical resources. The system's indexing workflow processes the text metadata of dive...

    Authors: Nigam H Shah, Nipun Bhatia, Clement Jonquet, Daniel Rubin, Annie P Chiang and Mark A Musen
    Citation: BMC Bioinformatics 2009 10(Suppl 9):S14
  15. Functional magnetic resonance imaging (fMRI) is a technology used to detect brain activity. Patterns of brain activation have been utilized as biomarkers for various neuropsychiatric applications. Detecting de...

    Authors: Bo Jin, Alvin Strasburger, Steven J Laken, F Andrew Kozel, Kevin A Johnson, Mark S George and Xinghua Lu
    Citation: BMC Bioinformatics 2009 10(Suppl 9):S15
  16. The incorporation of biological knowledge can enhance the analysis of biomedical data. We present a novel method that uses a proteomic knowledge base to enhance the performance of a rule-learning algorithm in ...

    Authors: Jonathan L Lustgarten, Shyam Visweswaran, Robert P Bowser, William R Hogan and Vanathi Gopalakrishnan
    Citation: BMC Bioinformatics 2009 10(Suppl 9):S16

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