BNA-M 2012 - 1st Biological Network Analysis and Applications in Translational and Personalized Medicine (BNA-M)

Systematic study of complex interactions among molecular components (i.e. DNA, RNA, microRNA, proteins and small molecules) is a new paradigm for discovering molecular functions or functional pathways in a global scale. Modeling and analysis of the inherent, dynamic, and structural behaviors of biological networks in a topological perspective have been recently a primary issue in Bioinformatics research. However, these studies have been challenging because of the large scale and complex connectivity of current biological networks, structured by high-throughput experimental data. A variety of computational techniques might be applied for effective analysis of the large-scale, complex biological networks. Recently, the application of such techniques applied to personalized and translational medicine is becoming a main area. In fact the discovery of such mechanism may conduct to the development of novel drugs that are targeted to the involved cellular mechanisms.
In particular, many research problems in current high-throughput protein-protein interactome data are widely open such as: the integration of existing databases in a large map of interactions, the introduction of semantic technologies to manage, query, and analyze data, the introduction of novel models to represent the spatio-temporal variation of interactome, and the integration of multiple data sources (i.e. protein, DNA, RNA and microRNA) .
This workshop aims to bring together researchers in bioinformatics with focus on analysis of biological networks analysis and in particular with application to bio-medicine. It will provide a forum to discuss state-of-the-art approaches and opportunities for interdisciplinary collaborations.
Topics
Topics of interest include, but not limited to:
Biological network modeling
Function prediction from biological networks
Network motif analysis
Functional pathway identification
Protein complex or functional module detection
Molecular interaction prediction
Network dynamics and evolution analysis
Network data management and integration
Graph mining algorithms
Querying and retrieval of PPI data
Semantic web technologies for Interactomics
Parallel or grid-based approaches for Interactomics
Biomarker discovery (identification of molecular targets for early detection, prognosis and treatment of diseases)
Integration and analysis of genomic, proteomic, and interactomic data for medical applications
Applications of Biological Network Analysis in Clinical Practice
o Biomarker discovery (identification of molecular targets for early detection, prognosis and treatment of diseases)
o Technologies and data models for phenotype, genotype and proteotype data
o Integration and analysis of omics and medical data for clinical applications