Spatial Problems in the Nucleus
Aims and scope
Recent advancements in imaging and next generation molecular methods have opened the way to investigate the nuclear architecture in unprecedented resolution and scale. Understanding the spatial organization at the nuclear, chromosomal and sub-chromosomal levels and its role in key biological processes such as replication, gene regulation and epigenetics is an important challenge with applications to new diagnostics and therapeutics. The aim of this special session is to bring together researchers interested in the development of cutting-edge Computational Intelligence methods to address the methodological challenges posed by spatio-temproal problems in the Nucleus. Relevant topics within this context include Spatial Modelling, Chromatin Modelling, Nuclear Dynamics, 3C methods, FISH, Bias and Noise in 3C methods, Epigenetics, Spatial Markers, Data Integration methods, Data Analysis Methods.Submissions
Conference papers must be prepared following the guidelines illustrated on this website. Correct submissions require selecting this special session in the submission system. Each paper will be peer-reviewed and the resulting scores used to determine invitations for oral or poster presentations.About the organisers
Prof. Marco Botta
Prof. Marco Botta graduated, with laude, in Computer Science at Università di Torino in 1987. He received the Ph. D. in Computer Science from Università di Torino in 1993. Since Nov. 2001, he is an associate professor of Computer Science at the Faculty of Mathematical, Physical and Natural Sciences of the Università di Torino, Italy. He is affiliated to the Department of Computer Science of the same University. His research activity was mainly focused on artificial intelligence topics and, in particular, on machine learning problems. In his early research years he mainly studied and developed new methods for learning concepts from instances. Such methods were oriented both to the construction of knowledge bases for expert systems and to their refinement. Since 1996 his research activity has been devoted to the integration of symbolic and sub-symbolic learning approaches with the aim of combining the expressive power of first-order logic with the refinement mechanisms that are typical of a connectionist approach. Since 2004, his research interest shifted to study algorithms for learning from spatio/temporal sequences of data, with applications in bioinformatics, user profiling, log analysis and musical pieces analysis. He is currently applying these techniques for studying properties of the nuclear architecture.Ms. Yoli Shavit
Ms. Yoli Shavit's research interests include the application of machine learning and statistical methods to spatial and temporal problems in nuclear architecture, with the aim of developing new spatial markers for diseases and in particular for cancer. She is currently a Cambridge Trust PhD scholar at the Artificial Intelligence Group of the Computer Laboratory, University of Cambridge (UK), and a member of Churchill College. Previously, she was awarded an MSc in Bioinformatics and Systems Biology from Imperial College London (UK) and a BSc in Computer Science and in Life Science from Tel Aviv University (Israel).Large-Scale and HPC data analysis in bioinformatics: intelligent methods for computational, systems and synthetic biology.
Aims and scope
Biomedical research is currently facing the Big Data wave created by the huge amount of experiments performed every days in omics sciences. This new situation demands appropriate IT-infrastructures and scalable software to analyse data within an acceptable timespan. Massive parallel clusters, distributed technologies, on-Chip solutions such as GPGPU and Xeon Phi must be exploited with adequate algorithmic solutions to reach their full potential. The aim of this special session is to bring together researchers interested in cutting-edge methods to address the challenges posed by the huge amount of data produced in omics science. The idea is to present the latest advancements concerning High Performance Computing solutions required to treat multi-omics data and the related Big Data paradigms that are needed to manage the Large-Scale challenges of nowadays computational biology. Relevant topics within this context include all the field of Next-Generation Sequencing data analysis and interpretation, Genomics patterns identification and mining, Transcriptomics and Proteomics data integration, Systems Biology models simulation and optimization, Structural Biology and Molecular Dynamics, Synthetic Biology circuits design and simulation.Submissions
Conference papers must be prepared following the guidelines illustrated on this website. Correct submissions require selecting this special session in the submission system. Submission deadline extended to May 5, 2014. Each paper will be peer-reviewed and the resulting scores used to determine invitations for oral or poster presentations.About the organisers
Dr. Andrea Bracciali
Dr. Andrea Bracciali is currently a SICSA lecturer at the Department of Computing Science and Mathematics of the University of Stirling (from August 2010). Before that, he worked as a postdoctoral researcher at CNR, Italy (2009-2010) in the Applied Formal Methods group, and at the Computer Science Department of the University of Pisa (2003-2008), where he received his Ph.D. (2003) in Computer Science. His main research interests are centred in the formal description of interaction in computing with application for Systems Biology and Crowd Dynamics. His main research interests regard the formal description of interaction in computational systems and theories and tools for reasoning about such descriptions. This stream of research spawns form Theoretical Computer Science, Concurrency Theory and, more recently, Systems Biology.Dr. Ivan Merelli
Dr. Ivan Merelli is currently research fellow at the Institute for Biomedical Technologies (ITB) of the National Research Council (CNR) in the Bioinformatics Division. He received his Master of Science in Biomedical Engineering from the Politecnico di Milano, with a thesis about molecular surface modelling and analysis. In February 2009 he received a PhD in Computer Science from the university of Milano - Bicocca with a research project about surface matching for macromolecular docking and functional annotation. His research activities concern the development of software for sequence based genomics and for structural proteomics research, with particular interest in protein-protein interactions. He works actively on the implementation of high performance bioinformatics software using cluster infrastructures and grid distributed platforms. He has been involved in the development of databases and computational solutions for projects of Integrative System Biology, gene expression analyses and drug discovery.Computational Biostatistics For Data Integration In Systems Biomedicine
Aims and scope
Current biomedical research is facing the systems challenge involving the use of dedicated mathematical, bioinformatic and biostatistical tools. These are necessary to address the key issues of integrating data collected across multiple sources, the dynamics of disease processes, risk factors and biomarkers related to diagnosis, prognosis and response to treatments, as well as their modulation by genetic, epigenetic, lifestyle determinants, and environmental influences. The recent workshop Clinical Needs in Oncology and Cardiovascular Diseases, as drivers for a Systems Medicine approach of the EU Coordinating Action System Medicine CASyM in Genua for the Clinical Oncology round table, pointed out the following issues: The existence of paradigm shift towards integrative (cross-disciplinary Systems Medicine). Systems Medicine consortia should implement "collective" rather than individualistic ways of working. There is an unmet need for understanding the biological complexity of cancer. An interdisciplinary approach is needed for reaching successful, long-term goals. Involvement and education of the general public must be stressed. To face these issues data integration is considered the top priority. The aim of this special session is to bring together researchers interested in the development of Computational Biostatistics methods to address the applied and methodological challenges posed by the data integration problem in System Biomedicine. Relevant topics within this context include the development and application of methods for Survival Data Analysis with Omic data, Diagnosis and Prognostic tools on complex Biomarkers as well as experimental design and data analysis approaches for Pharmacogenomics and response to Polytherapies.Submissions
Conference papers must be prepared following the guidelines illustrated on this website. Correct submissions require selecting this special session in the submission system. Submission deadline extended to April 28, 2014. Each paper will be peer-reviewed and the resulting scores used to determine invitations for oral or poster presentations.About the organisers
Prof Elia Mario Biganzoli
Elia Mario Biganzoli. Professor in Medical Statistics at the Faculty of Medicine of the University of Milan and Senior Biostatistician, Unit of Medical Statistics, Biometry and Bioinformatics, Fondazione IRCCS Istituto Nazionale dei Tumori, Task Force Leader Evaluation & Benchmarking, BIOPATTERN Network of Excellence FP6 project:" Computational Intelligence for Biopattern Analysis in Support of eHealthcare". Cofounder of the IEEE Neural Network Society Special Interest Group Biopattern. He was responsible and participated to national and international projects with Associazione Italiana per la Ricerca sul Cancro, National Research Council-Polish Academy of Sciences, Italian Ministry of Health, Italian Ministry of the University and Research, European Commission. His main research fields concern statistical methods for survival analysis and biological assay development. He participated in the planning of diagnostic and prognostic studies in cancer, cardiovascular diseases, rheumatology, hematology and the analysis of their results with special interest on molecular biomarkers and bioprofiles. He developed statistical approaches for the extension of generalized linear models with artificial neural networks and splines for the flexible analysis of censored survival data. His main research focus with his group was to join biostatistics with biomedical informatics through multivariate analysis and pattern recognition approaches in oncology.
Prof. Paulo Lisboa
Professor in Industrial Mathematics in the School of Computing and Mathematical Sciences at Liverpool John Moores University and Research Professor at St Helens & Knowsley Teaching Hospitals. His research is focused on computer-based decision support in healthcare and data analytics in public health, sports science and computational marketing. In particular he has an interest in principled approaches to interpretable modelling of non-linear data and processes. He is in the Horizon2020 Advisory Group for Societal Challenge Health, Demographic Change & Wellbeing an expert evaluator for the European Community DG-INFSO, and in the EPSRC Peer Review College. He has over 250 refereed publications with awards for citations, chairs the Medical Data Analysis Task Force in the Data Mining Technical Committee of the IEEE-CIS and is Associate Editor for IET Science Measurement and Technology, Neural Computing Applications, Applied Soft Computing and Source Code for Biology and Medicine. Paulo Lisboa studied mathematical physics at Liverpool University where he took a PhD in theoretical physics in 1983. His research is focused on computer-based decision support in healthcare, extending also to the analysis of public health data for policy reporting and commissioning purposes and computational data analysis in sports science and computational marketing. In particular he has made significant developments in source identification in Magnetic Resonance Spectroscopy, flexible models for hazard estimation following surgery for breast cancer and principled approaches to retrieval-based classification using information geometry.
Prof. Jon Garibaldi
Professor of Computer Science School of Computer Science, University of Nottingham. Employment History: University of Nottingham; 1st Aug 2002 to Date Lecturer / Senior Lecturer; De Montfort University, Leicester; 01/09/99 - 31/07/02 Research Assistant / Fellow; University of Plymouth; 01/05/92 - 31/08/99 C/Unix Consultant; Self-employed; 01/10/91 - 30/04/92 Analyst/Programmer; DuPont-Howsons, Leeds; 01/10/89 - 30/09/90 Senior Programmer; Barrs Court Computer Systems, Bristol; 01/01/86 - 30/09/89 Programmer; Self-employed; 01/07/84 - 31/12/85 Qualifications 1997 Ph.D. "Intelligent Techniques for Handling Uncertainty in the Assessment of Neonatal Outcome", University of Plymouth 1991 M.Sc. "Intelligent Systems", University of Plymouth 1984 B.Sc. Hons. Physics, University of Bristol Research Overview My main research interest is in the development of artificial intelligence techniques for data modelling and analysis, biomedical decision support and in the modelling of human decision making. I also research generic machine learning topics of data clustering and classification, particularly when dealing with uncertain and complex data, and optimisation. These interests have been applied in areas such as the assessment of complex multi-modal datasets in breast cancer prognosis and diagnosis of Alzheimer's disease, the early detection of cancer through analysis of FTIR spectra, and parameter optimisation in systems biology models. Research Leadership Director of the University's Advanced Data Analysis Centre (ADAC).
Prof. Leif Peterson
Leif E. Peterson. Associate Professor of Public Health, Weill Cornell Medical College (Cornell University), and Director of the Center for Biostatistics, Houston Methodist Research Institute, Adjunct Associate Professor of Medicine, Dept. of Medicine, Baylor College of Medicine, Adjunct Associate Professor of Biostatistics, Division of Biostatistics, University of Texas, School of Public Health and Adjunct Associate Professor, Department of Neuroscience & Experimental Therapeutics, Texas A & M University. Dr. Peterson is a member of IEEE-CIS Computational Intelligence Society - Institute of Electrical and Electronics Engineers, IEEE-CIS Bioinformatics and Bioengineering Technical Committee (BBTC), and IEEE-CIS-BBTC-Task Force on Neural Networks (TFNN). Dr. Peterson has over 25 years of experience in biostatistical study design and analysis, bioinformatics, genomic classification, transcriptomics and proteomics, cancer statistics, software development for machine learning and computational intelligence. He has published more than 90 peer-reviewed papers and has published numerous US Government reports. Dr. Peterson's research focuses on data integrative systems biology approaches to develop biologically-motivated models of risk mitigation for space radiation-induced cancer and CNS effects, novel techniques for optimization based on neural adaptive learning with metaheuristics (neural networks, genetic algorithms, swarm intelligence, evolution strategies), information retrieval using duo-mining (data and text), n-grams, and non-linear and linear dimensional reduction with manifold learning, eigendecomposition, random matrix theory, and Monte Carlo simulation for uncertainty analyses.