Integration and Querying of Genomic and Proteomic Semantic Annotations for Biomedical Knowledge Extraction PROJECT TITLE :Integration and Querying of Genomic and Proteomic Semantic Annotations for Biomedical Knowledge ExtractionABSTRACT:Understanding complicated biological phenomena involves answering complex biomedical questions on multiple biomolecular information simultaneously, that are expressed through multiple genomic and proteomic semantic annotations scattered in several distributed and heterogeneous knowledge sources; such heterogeneity and dispersion hamper the biologists' ability of asking global queries and performing world evaluations. To overcome this downside, we developed a software design to create and maintain a Genomic and Proteomic Knowledge Base (GPKB), that integrates many of the most relevant sources of such dispersed data (together with Entrez Gene, UniProt, IntAct, Expasy Enzyme, GO, GOA, BioCyc, KEGG, Reactome, and OMIM). Our solution is general, as it uses a versatile, modular, and multilevel international information schema based mostly on abstraction and generalization of integrated information options, and a group of automatic procedures for alleviating information integration and maintenance, also when the integrated data sources evolve in data content, structure, and range. These procedures conjointly assure consistency, quality, and provenance tracking of all integrated data, and perform the semantic closure of the hierarchical relationships of the integrated biomedical ontologies. At http://www.bioinformatics.deib.polimi.it/GPKB/, a Internet interface permits graphical easy composition of queries, although complicated, on the data base, supporting conjointly semantic query expansion and comprehensive explorative search of the integrated information to better sustain biomedical data extraction. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Automated Transient Input Stimuli Generation for Analog Circuits Predicting Protein Function via Semantic Integration of Multiple Networks