A vast ocean of "small experiments" data and OMICs datasets is being accumulated in oncology research, and none of the currently available life sciences informatics platforms is capable of a comprehensive handling and meaningful functional analysis of these data. Here we propose to build such a system, MetaMiner (Oncology) on the base of our mature data analysis platform MetaCore. The system will have a comprehensive structured database of cancer domain knowledge and a toolkit for functional analysis (ontology enrichment, interactome, network tools). MetaMiner will be integrated with CaBIG, translational medicine databases and third party software. In Phase I, we developed several novel algorithms for quantitative functional analysis of large multi-patient datasets and offered new methods for cross-analysis of cancer datasets of different type. We have also designed a framework for manual annotation of cancer pathways, assays and gene-disease causative associations. In Phase II, we will implement the algorithms into a robust "rich client" analytical platform and complete annotation of cancer data. PUBLIC HEALTH RELEVANCE: We developed novel algorithms for quantitative functional analysis and cross-analysis of datasets of different type. We designed a framework for manual annotation of cancer pathways, assays and other data. In Phase II, we will implement the algorithms into a robust "rich client" platform and complete the annotation program