Special Session Proposals

As the sequencing technologies develop reducing the costs and increasing the accuracy, the research in biological sciences is transformed from hypothesis-driven to data-driven approaches. The Big Data encompasses generation of data ranging from DNA sequence data for thousands of individuals to single cell data for thousands of cell types from an individual. This has moved the bottle-neck down-stream of the data generation to use this data to gain new knowledge, finally with an aim to improve the quality of human life. The important down-stream challenges with Big Data include development of strategies for efficient storage of Big Data making them Findable, Accessible, Interoperable, and Reusable (FAIR), to make it usable for research. The next step is the development of new methods including softwares and web tools to make sense of Big Data. The final important step is demonstrate that Big Data can indeed lead to new knowledge. This session will cover the research aspects on all three aspects of Big Data described above.

Organizer:
Dr. Anagha Joshi, Group leader in the Division of Developmental Biology at the Roslin Institute, University of Edinburgh.
Website: https://www.ed.ac.uk/roslin/about/contact-us/staff/anagha-joshi

The analysis of data streams captured with biomedical sensors can be performed as an embedded procedure within the sensor or sensor network or at a later stage in a receiving system. Currently, several systems reduce the number of signals monitored via sensors (e.g. when using wearable devices) in order to save energy. In this case, the pre-processing task is embedded into the sensor or close to it. As a result, less data is transferred but pattern matching becomes more complex since cross-reference data is missing and computing power is limited. This session should present new and emerging approaches.

Organizers:
Prof. Dr. Natividad Martínez Madrid. Head of the Internet of Things laboratory and Director of the AAL-Living Lab at Reutlingen University.Reutlingen University.Department of Computer Science, Alteburgstr. 150, D-72762 Reutlingen (Germany).
Prof. Dr. Juan Antonio Ortega. Director of the Centre of Computer Scientific in Andalusia (Spain) www.cica.es and the head of the research group IDINFOR (TIC223).University of Sevilla, ETS Ingeniería Informática. Spain.
Prof. Dr. Ralf Seepold. Head of the Ubiquitous Computing Lab at HTWG Konstanz.Department of Computer Science, Brauneggerstr. 55, D-78462 Konstanz (Germany)
Websites:
http://iotlab.reutlingen-university.de
http://madeirasic.us.es/idinfor/
http://uc-lab.in.htwg-konstanz.de

Understanding of the biological systems requires progress in the measurement techniques, methods, and principles of acquisition. The development of IT and physical resolution brings novel advanced probes, devices, or interpretation as well as more questions and possibilities. Automation of the processing and analysis is increasing thanks to artificial intelligence and machine deep learning The proper bioinformatic parametrization for the analysis of complex systems continues in the way towards automatic or self setting of the acquisited biophysical attributes.

In this special section should be provided discussion on novel techniques and measurement devices, emerging challenges for complex systems, open solution and future visions. The broad examples from self parametric results will support the discussion with practical applications.

Organizer:
Dipl-Ing. Jan Urban, Ph.D.. Head of laboratory of signal and image processing. University of South Bohemia in České Budějovice. Faculty of Fisheries and Protection of Waters.South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses. Institute of Complex Systems. Czech Republic.
Websites:
www.frov.jcu.cz/en/institute-complex-systems/lab-signal-image-processing
www.researchgate.net/profile/Jan_Urban5

Genomics is concerned with the sequencing and analysis of an organism genome taking advantage of the current, cost-effective, high throughput sequencing technologies. Their continuous improvement is producing a continuous new demand of enhanced high-throughput bioinformatics tools. In this context, the generation, integration and interpretation of genetic and genomic data is driving a new era of healthcare and patient management. Medical genomics (or genomic medicine) is this emerging discipline that involves the use of genomic information about a patient as part of the clinical care with diagnostic or therapeutic purposes to improve the health outcomes. Moreover, it can be considered a subset of precision medicine that is having an impact in the fields of oncology, pharmacology, rare and undiagnosed diseases, and infectious diseases.The aim of this special session is to bring together researchers in medicine, genomics, and bioinformatics to translate medical genomics research into new diagnostic, therapeutic, and preventive medical approaches. Therefore, we invite authors to submit original research, new tools or pipelines, or their update, and review articles on relevant topics, such as (but not limited to):

  • Tools for data pre-processing (quality control and filtering)
  • Tools for sequence mapping
  • Tools for the comparison of two read libraries without an external reference.
  • Tools for genomic variants (such as variant calling or variant annotation)
  • Tools for functional annotation: identification of domains, orthologues, genetic markers, controlled vocabulary (GO, KEGG, InterPro...)
  • Tools for biological enrichment in non-model organisms
  • Tools for gene expression studies
  • Tools for Chip-Seq data
  • Tools for “big-data” analyses
  • Tools for integration in workflows
Organizer:
Prof. M. Gonzalo Claros, Department of Molecular Biology and Biochemistry, University of Málaga, Spain.
Dr. Javier Pérez Florido, Bioinformatics Research Area, Fundación Progreso y Salud, Seville, Spain.

A drug delivery system is designed to release controlled amount of drugs to a specific target area. To devise optimization strategies for targeted drug delivery the combined action of various processes need to be well understood. Mathematical modelling offers a valuable tool when evaluating potential drug carrying materials coupled with rate controlling coatings. When applied to experimental data, simulations can yield valuable insight and guide further research with the aim of identifying and evaluating key drug release mechanisms. Although diffusion is often a primary drug release process, other effects such as binding and dissolution as well as effects occurring at material interfaces are no less important in describing various rate controlling release mechanisms.

Considered systems include: intraocular- and soft contact lenses, orthopaedic implants, arterial stents and transdermal patches.

Organizers:
Ph.D cand Kristinn Gudnason.Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Iceland.
Prof. Fjola Jonsdottir. Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Iceland.
Prof. Emeritus Sven Sigurdsson. Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Iceland.
Prof. Mar Masson. Faculty of Pharmaceutical Science, University of Iceland, Iceland.

Nanostructured material science with natural origin is becoming a hot-topic in nanomedicine for addressing toxicity and high cost limitations. The absorption of pharmaceutical drugs in natural inorganic nanostructured solids is very useful for controlled delivery of bioactive compounds. Molecular modelling and analytical spectroscopic techniques are well-established research fields for characterization of these materials. This approach is becoming great interest in the studies of these nanocomposites and the interactions of organics on the surfaces of the inorganic solids in health applications. The aim of this session is to gather professionals from a wide scope of scientific disciplines to better understand molecular aspects of nanocomposite components behaviour and drug design. This interdisciplinary session can include contributions from computational chemistry (empirical potentials, quantum, coarse-grained, etc.), NMR, infrared, and Raman spectroscopies, and X-ray-diffraction/neutron/synchrotron techniques.

This special session has a multidisciplinary nature and is not easy to be included in one of the congress topics due to its transversal aim connected with several topics of the congress: Computational proteomics (protein structure modelling), Biomedicine (biomedical computing, nanomedicine), Biomedical Engineering (bio-nanotechnology), Computational systems for modelling biological processes (simulation and visualization of biological systems), health care and diseases (drug design and computational immunology).

The aim of this special session is to show the potential application of computational modelling methods in nanomedicine for experimental researchers and, at the same time, for theoreticians diagnosing possible complementary tool for experiments, generating useful discussions between experimentalists and theoreticians to promote future scientific collaborations.

Organizers:
Dr. C. Ignacio Sainz-Díaz.Instituto Andaluz de Ciencias de la Tierra, CSIC/UGR, Granada, Spain. Expert in organic chemistry and interactions of bioactive compounds on mineral surfaces by computational chemistry with experience in I+D of pharmaceutical industry.
Dr. Carola Aguzzi.Dpto. Tecnología Farmacéutica. Universidad de Granada, Granada, Spain. Expert in pharmaceutical technology andapplications of natural inorganic excipients.

Phenotype prediction problems have a very underdetermined character since the number of samples is always much lower in size than the number of genes/genetic probes/SNPs/ etc, that are monitored to explain a given phenotype. This generates decision problems that have a huge uncertainty space. This includes a great variety of problems with great impact in translational medicine, such as the analysis of mechanism of action of genes in disease progression, the investigation of new therapeutic targets, the analysis of secondary effects, treatment optimization; analysis of the effect of mutations in the transcriptome and in proteomics, etc.

The objective of the session is to present novel computational approaches to reduce the complexity of high dimensional genetic data while keeping the main information content. Application in cancer and genomics, rare and neurodegenerative diseases are welcome. Particularly the design of new methods to perform the robust analysis of pathways involved in disease development will be one the main topics addressed in this session.

Organizer:
Prof. Juan Luis Fernández-Martínez.Mathematics Department. Applied Mathematics Section. Director of the Group of Inverse Problems, Optimization and Machine Learning. University of Oviedo. Spain.
Website: https://www.researchgate.net/profile/Juan_Luis_Fernandez-Martinez

In a very short period of time, many areas of science have made a sharp transition towards data-driven methods. This new situation is clear in the life sciences and, as particular cases, in biomedicine, bioinformatics and healthcare.

You could see this as a perfect scenario for the use of data analytics, from multivariate statistics to machine learning (ML) and computational intelligence (CI), but this scenario also poses some serious challenges. One of them takes the form of (lack of) interpretability / comprehensibility / explainability of the models obtained through data analysis. This could be a bottleneck especially for complex nonlinear models, often affected by what has come to be known as the "black box syndrome".

In some areas such as medicine and healthcare, not addressing such challenge might seriously limit the chances of adoption, in real practice, of computer-based medical decision support systems (MDSS).

Interpretability and explainability have become hot research issues, and there are different reasons for that: One of them is the soaring success of deep learning artificial neural networks in recent years. These models risk not being adopted in areas where human decision is key and that decision must be explained as they are extreme "black box" cases. Another reason is the implementation of the European Union directive for General Data Protection Regulation (GDPR). Enforced in April 2018, it mandates a right to explanation of all decisions made by automated or artificially intelligent algorithmic systems. Needless to say, this directly involves data analytics and it is likely to have an impact on healthcare, medical decision making, and even in bioinformatics through the use of genomics in personalized medicine.

In this session, we call for papers that broach the topics of interpretability/ comprehensibility/ explainability of data models (with a non-reductive focus on ML and CI) in biomedicine, bioinformatics and healthcare, from different viewpoints, including:

  • Enhancement of the interpretability of existing data analysis techniques in problems related to biomedicine, bioinformatics and healthcare.
  • New methods of model interpretation/explanation in problems related to biomedicine, bioinformatics and healthcare.
  • Case studies biomedicine, bioinformatics and healthcare in which interpretability/comprehensibility/explainability is a key aspect of the investigation.
  • Methods to enhance interpretability in safety critical areas (such as, for instance, critical care).
  • Issues of ethics and social responsibility (including governance, privacy, anonymization) in biomedicine, bioinformatics and healthcare.
Organizers:
Prof. Alfredo Vellido Intelligent Data Science and Artificial Intelligence (IDEAI) Research Center. Universitat Politècnica de Catalunya, Barcelona, Spain.
Prof. Sandra Ortega-Martorell Department of Applied Mathematics, Liverpool John Moores University, Liverpool, UK.
Prof. Alessandra Tosi Mind Foundry Ltd., Oxford, UK
Prof. Iván Olier Caparroso MMU Machine Learning Research Lab, Manchester Metropolitan University, Manchester, UK

In tertiary hospitals where the nuclear medicine services have been introduced, the radioactive materials used in diagnosis and / or treatment need to be handled. The hospital design and medical planning should consider such these materials and their policy for treatment. The nuclear wastes have been divided into solid and liquid based on the used materials and for their half-life times which start from few minutes till reaching for years.

In our study, the most common radioactive liquid materials (wastes) have been treated by smart system that to detect the material and based on its HLT (activities) will be distributed in shielded storage tanks then to the sewage treatment plant (STP) of the hospital after keeping for required times. The location and capacity of these tanks together with their monitoring and control system should be considered in design stage which determines the treatment processes.

Motivation and objectives for the session: Nuclear medicine department should be considered in the design stage and its space program. Location and capacity of storage tanks and their drainage lines should be considered in hospital.


Organizer:
Dr. Khaled El-Sayed is presently an assistant professor of Biomedical Engineering with the department of Electrical and Medical Engineering at Benha University, Egypt.