Living organisms are described by highly complex biological networks which entail the huge number of interactions among molecules, genes and proteins in biological functions and pathways. At a broader level, biological networks are used to represent correlations, extracted from clinical datasets, among diseases, mutations, patients and any kind of clinical or biological feature (e.g. SNPs, treatments, diagnoses, costs). Other uses of biological networks comprise: executable models, neural network computational graphs, connectome graphs. These networks try to mimic the brain for the implementation of machine learining computational tools or represent expression levels of different areas of the brain to explain how it works. Biological networks can represent the state of a set of features in a particular moment, but they can also describe the spatio-temporal distribution of biological and clinical events.
In literature there are many algorithms and techniques able to extract knowledge from network graphs which have shown their effectiveness in a number of classical domains, such as: computational geometry, automated algorithm checking, optimal path search and, more recently, sentiment analysis in social networks applications.
The efficiency, effectiveness and robustness of network algorithms is a must in a challenging domain as the biological and clinical ones, where the outcomes of an algorithm could have a high impact on the health of an individual or even a population. Recently, Genome Wide Association Studies (GWAS) have shown their effectiveness in extracting knwoledge from large clinical databases, where its natural representation is again a set of biological networks, each representing the amount of mutations in common among diseaseas or patients in the dataset.
Understanding the design and unveiling novel techniques and applications for analyzing and extracting new knowledge from biological and clinical networks is a crucial task. They could become one of the most important tools towards the long awaited and widely anticipated Precision Medicine, which promises to adapt diagnoses and treatments to each patient in an olistic approach, according to his overall health status.
This workshop wants to bring together researchers, data scientists, biological experts, computer scientists and network experts interested in: (i) developing algorithms and software tools to be applied in one of the described domains, (ii) assessment, testing and comparison of existing tools and techniques for real and complex applications with the use of biological networks, (iii) definition of novel approaches using networks to solve problems in the areas of bioinformatics, structural and computational biology, executable models, social networks, spatiotemporal analysis in clinical applications.

Topics
------

The Workshop topics include (but are not limited to) the following:
- Biological networks in Computational and Structural Biology
- Social Networks
- Protein-to-Protein interaction networks
- Transcriptional and regulatory networks
- Networks for clinical and biological applications
- Network algorithms assessment
- Biological Networks modelling and management

Paper submission and publication
--------------------------------

All accepted papers will be scheduled for oral presentations and will be printed in the conference proceedings published by Elsevier Science in the open-access Procedia Computer Science series (on-line). Submitted technical papers must be no longer than 6 pages including all figures, tables and references. Please submit your papers on the BioNet Easychair submission page: https://easychair.org/conferences/?conf=bionet2017