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SUMMARY:Analyzing Large Genomic Data Collections
DTSTART:20090316T161500
DTSTAMP:20260407T051717Z
UID:c44490ed72767e0f2ceb322d0bf2f4483dda101320c64fd57661eedb
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr. Curtis Huttenhower\, Princeton University\, USA\nModern bi
 ology has developed a wealth of high-throughput experimental techniques.  
 Many of these\, particularly microarrays and deep sequencing\, produce mea
 surements simultaneously for every gene in an organism's genome.  As the n
 umber of such genome-scale datasets reaches the thousands for many organis
 ms\, new opportunities arise to understand systems-level biology and human
  disease by means of very large scale data integration and analysis.\n\nTh
 is diversity of genomic data presents excellent opportunities for the deve
 lopment of machine learning methods and for the discovery of new biology. 
  Previous work has been particularly successful in integrating relatively 
 small numbers of datasets to predict the roles and interactions of protein
 s in unicellular organisms.  With large genomic data collections\, new dat
 a mining techniques can extend the depth of this process\; this allows the
  analysis of biological activity induced by specific environmental conditi
 ons and of associations and regulatory cross-talk between entire cellular 
 pathways and processes.\n\nLarge scale data integration also offers the br
 eadth to analyze systems biology in complex higher organisms.  This allows
  important areas such as human disease to be explored from the perspective
  of biomolecular interaction networks.  It also calls out new challenges\,
  such as the need to incorporate knowledge of tissue types and of developm
 ental stages into integrative models of metazoan biology.  Finally\, heter
 ogeneous data integration can also be applied to predict specific genetic 
 interaction types such as transcriptional regulation\, a precursor to the 
 inference of detailed and accurate pathway models from large collections o
 f diverse genomic data.\n\nC. Huttenhower's homepage
LOCATION:INM202
STATUS:CONFIRMED
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