ACM-BCB 2010 - Call for Paper
ACM-BCB 2010
Workshop on Protein Protein Interaction Data: Management, Querying and Analysis
Call For Papers
Biological macromolecules, such as proteins, play their role by
interacting among them. Nowadays, many biochemical interactions among
biological macromolecules are known, thanks to the application of
different experimental platforms using different technologies, such as
Yeast2Hybrid, Mass Spectrometry, etc. Results of such interactions may
be stored in databases originating a knowledge base for biochemical
reactions among known macromolecules.
A key area in such field is the study of the interactions among
proteins, especially within a cell. Currently, different experiments
have lead to the accumulation of a large amount of data about
proteins, also referred as Protein-Protein Interaction (PPI) data.
Nevertheless, as the analysis of single protein structure requires
large computational efforts, also analysing protein to protein
interactions requires algorithms and software platforms for the
modeling, management and analysis of PPI data.
Moreover, thanks to such an interest in interactions, representing a
large base of protein to protein interactions may generate a very
large network, referred to as Protein Interaction Network (PIN), that
codes biochemical and spatial relations among proteins. The analysis
of such networks allows to discover new knowledge about biological
functionalities.
PINs can be represented by using (direct) graphs where nodes are
associated to proteins, and edges represent interactions among
proteins. Once that an interaction network is modeled by using a
graph, the study of biological properties can be done using
graph-based algorithms and associating biological properties of the
modeled PPI to the topological properties of the underlying graph.
In recent years many research efforts have produced different
databases for storing interactions (PPI databases), as well as many
algorithms for analyzing PINs and software tool for their
visualization. Analysis algorithms can be grouped in three main
classes: (i) algorithms that predict protein complexes, (ii)
algorithms used for the extraction of functional modules (e.g.
pathways), and (iii) algorithms for the alignment of PPI networks
(e.g. belonging to different organisms).
Nevertheless, many research problems are still open such as: the
integration of existing databases in a large map of interaction, the
introduction of semantic technologies to manage, query and analyze
data, the introduction of novel models able to represent the
spatio-temporal variation of interactome, the integration of PPI
information with biochemical pathways, gene (regulatory) pathways, and
transcriptomics.
This Workshop is designed to bring together computer scientists,
biologists and clinicians for exploring the current state-of-the-art
research taking place in all aspects of interactomics, from basic
science to clinical practice. The workshop intends to provide a forum
for the presentation oforiginal research, valuable software tools
(basic algorithms, modelling, analysis, and visualization tools,
databases), and clinical fallouts, on topics of importance to
interactomics.
TOPICS OF INTEREST
The topics of interest will include but will be not limited to:
Data management and querying in Interactomics
* Data management and analysis in Interactomics
* Data models and integration for interactomics
* Querying and retrieval of PPI data
* Semantic web technologies for Interactomics
* Parallel and Grid-based methods for interactomics
Algorithms and software tools for Interactomics
* Computational methods for Interactomics
* Novel interaction identification
* Protein interaction prediction
* Alignment of PINs
* Applications of Data Mining, Neural Networks, Soft Computing for
interactomics
* Exploration and visualization of PPI data
Interactomics and biomedical research
* Biomarker discovery (identification of molecular targets for
early detection, prognosis and treatment of diseases
* Integration and analysis of genomics, proteomic, and
interactomics data for medical applications
LINK
Workshop on Protein Protein Interaction Data: Management, Querying and Analysis
Call For Papers
Biological macromolecules, such as proteins, play their role by
interacting among them. Nowadays, many biochemical interactions among
biological macromolecules are known, thanks to the application of
different experimental platforms using different technologies, such as
Yeast2Hybrid, Mass Spectrometry, etc. Results of such interactions may
be stored in databases originating a knowledge base for biochemical
reactions among known macromolecules.
A key area in such field is the study of the interactions among
proteins, especially within a cell. Currently, different experiments
have lead to the accumulation of a large amount of data about
proteins, also referred as Protein-Protein Interaction (PPI) data.
Nevertheless, as the analysis of single protein structure requires
large computational efforts, also analysing protein to protein
interactions requires algorithms and software platforms for the
modeling, management and analysis of PPI data.
Moreover, thanks to such an interest in interactions, representing a
large base of protein to protein interactions may generate a very
large network, referred to as Protein Interaction Network (PIN), that
codes biochemical and spatial relations among proteins. The analysis
of such networks allows to discover new knowledge about biological
functionalities.
PINs can be represented by using (direct) graphs where nodes are
associated to proteins, and edges represent interactions among
proteins. Once that an interaction network is modeled by using a
graph, the study of biological properties can be done using
graph-based algorithms and associating biological properties of the
modeled PPI to the topological properties of the underlying graph.
In recent years many research efforts have produced different
databases for storing interactions (PPI databases), as well as many
algorithms for analyzing PINs and software tool for their
visualization. Analysis algorithms can be grouped in three main
classes: (i) algorithms that predict protein complexes, (ii)
algorithms used for the extraction of functional modules (e.g.
pathways), and (iii) algorithms for the alignment of PPI networks
(e.g. belonging to different organisms).
Nevertheless, many research problems are still open such as: the
integration of existing databases in a large map of interaction, the
introduction of semantic technologies to manage, query and analyze
data, the introduction of novel models able to represent the
spatio-temporal variation of interactome, the integration of PPI
information with biochemical pathways, gene (regulatory) pathways, and
transcriptomics.
This Workshop is designed to bring together computer scientists,
biologists and clinicians for exploring the current state-of-the-art
research taking place in all aspects of interactomics, from basic
science to clinical practice. The workshop intends to provide a forum
for the presentation oforiginal research, valuable software tools
(basic algorithms, modelling, analysis, and visualization tools,
databases), and clinical fallouts, on topics of importance to
interactomics.
TOPICS OF INTEREST
The topics of interest will include but will be not limited to:
Data management and querying in Interactomics
* Data management and analysis in Interactomics
* Data models and integration for interactomics
* Querying and retrieval of PPI data
* Semantic web technologies for Interactomics
* Parallel and Grid-based methods for interactomics
Algorithms and software tools for Interactomics
* Computational methods for Interactomics
* Novel interaction identification
* Protein interaction prediction
* Alignment of PINs
* Applications of Data Mining, Neural Networks, Soft Computing for
interactomics
* Exploration and visualization of PPI data
Interactomics and biomedical research
* Biomarker discovery (identification of molecular targets for
early detection, prognosis and treatment of diseases
* Integration and analysis of genomics, proteomic, and
interactomics data for medical applications
LINK
