Special Section on:

Semantic-Based Approaches for Analysis of Biological Data

The integration of biological (e.g. omics) data with biological knowledge is

a recent trend in Bioinformatics. A lot of biological information is available

and is spread on different sources and encoded in different ontologies

(e.g. Gene Ontology, as well as in many others hosted by the Open

Biomedical Ontologies Foundry).
 

Biological information is associated with biological concept in a process

known as annotation. Annotating existing protein data with biological

information may enable the use (and the development) of algorithms that

use biological ontologies as a framework to mine annotated data.
 

Recently many methodologies and algorithms that use ontologies to

extract knowledge from data, as well as to analyse ontologies themselves

have been proposed and applied to other fields. Conversely, the use of

such annotations for the analysis of protein data is a relatively novel

research area that is currently becoming more and more important

in research. As shown in literature there is a positive trend in the use

of biological information in the analysis of protein data.
 

Proposed approaches span from the definition of the similarity among

genes and proteins on the basis of the annotating terms, to the definition

of novel algorithms that use such similarities for mining protein data on a

system-level scale.
 

This special section will focus on novel approaches of such analysis such as

novel semantic similarity measures as well as novel algorithms focusing on

applications on biological problems. In particular interdisciplinary works

with clear implication and impact on wet lab biology are welcome. Finally

systematic discussion and critical comparison of main approaches may be

hosted. Remaining challenges, as well as possible future directions of

research may be highlighted.
 

Consequently topics include but are not limited to:
 

● Semantics approaches for Biomolecular network modeling

● Ontology Aware Function prediction from biomolecular networks

● Network motif analysis using semantics

● Definition of Domain Specific Ontologies

● Definition of semantic-based data mining algorithms

• Querying and retrieval of annotated data

● Integration of genome, proteome and interactome data using Semantics

● Semantic analysis of interactome and biomolecular data

● Definition of tailored Semantic Similarity Measures

● High Performance Computing for Semantic Analysis
 

Dates:

Submission: 30 -November 2014

Revision: 30 March 2015
 

Guest Editors:

Pietro Hiram Guzzi University “Magna Graecia” of Catanzaro

(hguzzi@unicz.it)

Young-Rae Cho Baylor University of Texas Young-Rae_Cho@baylor.edu

Marco Mina FBK Bruno Kessler Fundation mamina@fbk.eu
 

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