Title: Graph analysis and graph drawing techniques for multi-scale biological knowledge formalization and exploration
Type: Ph.D (in progress)
Abstract: Human body representations have been used for centuries to help in understanding and documenting the shape and function of its compounding parts. Recent advances in acquisition (e.g. MRI, CT, PET) and digital modeling (e.g. numerical measurements, signals, images, 3D models) techniques have led to an increased amount of digital counterparts of human body representations on different scales (e.g. organ, cellular, molecular, tissue). In this context, a major challenge is the management, exploration and analysis of these multi-scale heterogeneous data, in order to identify relevant biological patterns and extract the hidden knowledge. Semantic web based methods have been introduced that are designed to add meaning to the raw data by using formal description of the concepts and relationships encoded within the data, leveraging the
increasing expressivity of ontology languages such as OWL.
The research objective of the thesis is to study and devise approaches of application of semantic web based methods in order to effectively manage, explore and analyse multi-scale biomedical data. The main goals are: i) Development of original solutions for the integration of existing ontologies into a unified framework supporting multi-scale descriptions of entities and concepts by using modularization and integration techniques, specific to biomedical ontologies and ii) Development of techniques supporting visualization, providing cognitively rich and semantically meaningful navigation and information retrieval in the context of multi-scale biomedical data.
Author: Asan Agibetov
Advisors: Dr. Michela Spagnuolo, Dr. Giuseppe Patanè
University: Universita' Degli Studi di Genova, Italy
Defence Date (tentative): April 2017