Opening the Black box presents: Data mining omics, clinical and social data

The "Opening the Black Box" seminar series continues this Weds 13th March at 3pm on WLB207/208.

Mario Cannataro of the Università of Catanzaro will be talking on Data mining omics, clinical and social data. See https://ida-research.net/seminars/ for more details.

Title: Data mining omics, clinical and social data

Abstract: Recently, several factors are moving biomedical research towards a (big) data-centred science:(i) the Volume of data in bioinformatics is having an explosion, especially in healthcare and medicine; (ii) new bioinformatics data is created at increasing Velocity due to advances in experimental platform and increased use of IoT (Internet of Things) health monitoring sensors; (iii) increasing Variety and (iv) Variability of data (omics, clinical, administration, sensors, and social data are inherently heterogeneous) that may lead to wrong modelling, integration and interpretation, and finally (v) increasing Value of data in bioinformatics due to costs of infrastructures to produce and analyze data, as well as, value of extracted biomedical knowledge. The emerging of this Big Data trend in Bioinformatics poses new challenges for computer science solutions, regarding the efficient storage, preprocessing, integration and analysis of omics (e.g. genomics, proteomics, and interactomics) and clinical (e.g. laboratory data, bioimages, pharmacology data, social network data, etc.) data, resulting in a main bottleneck of the analysis pipeline. To face those challenges, main trends are: (i) use of high-performance computing in all steps of analysis pipeline, including parallel processing of raw experimental data, parallel analysis of data, and efficient data visualization; (ii) deployment of data analysis pipelines and main biological databases on the Cloud; (iii) use of novel data models that combine structured (e.g. relational data) and unstructured (e.g. text, multimedia, biosignals, bioimages) data, with special focus on graph databases; (iv) development of novel data analytics methods such as Sentiment Analysis, Affective Computing and Graph Analytics, that integrate traditional statistical and data mining analysis; (v) particular attention to issues regarding privacy of patients, as well as permitted ways to use and analyze biomedical data. 

Bio: Mario Cannataro is a Full Professor of Computer Engineering and Bioinformatics at University “Magna Graecia” of Catanzaro, Italy. He is the director of the Data Analytics research centre and the chair of the Bioinformatics Laboratory at University “Magna Graecia” of Catanzaro. His current research interests include bioinformatics, medical informatics, data analytics, parallel and distributed computing. He is a Member of the editorial boards of IEEE/ACM Transaction on Computational Biology and Bioinformatics, Briefings in Bioinformatics, High-Throughput, Encyclopaedia of Bioinformatics and Computational Biology, Encyclopaedia of Systems Biology. He was guest editor of several special issues on bioinformatics and he is serving as a program committee member of several conferences. He published three books and more than 200 papers in international journals and conference proceedings. Mario Cannataro is a Senior Member of IEEE, ACM and BITS, and a member of the Board of Directors for ACM SIGBio.