ABSTRACT
Introduction............................................................................................................415 What Types of Data and Analyses Do We Need To Track? ....................416 Ontologies and Data Standards .................................................................418 Gene Ontologies and the GO Consortium................................................424 Microarray Ontologies and the MGED Society .......................................425 HUPO and MIAPE....................................................................................428
Mining and Visualization of Protein Array Data ..................................................429 Data Mining and Statistical Approaches to Proteomics ...........................429 Natural language Processing and Biological Data Analysis ....................432 Graph Theory, Petri Nets, and Biological Data Analysis .........................435 Conclusions about Needs for Data Analyses ............................................438
Systems Biology as a Common Platform To Develop and Exchange Biological Models..................................................................................................438 Conclusions............................................................................................................440 References..............................................................................................................441
The generation of proteomic data sets is becoming increasingly common, while the analysis of such data remains in its infancy. Experience in handling genomic data, however, can help guide this challenge. This experience teaches us that efficient analysis, exchange and dissemination of proteomics data will require standardized methods for data storage and representation.
