Decision making in healthcare at clinical environments is often made on the basis of multiple parameters and in the context of patient presentation, which includes the setting and the specific conditions related to the reason for admission and the procedures involved. The data used in clinical decision-making may originate from manifold sources and at multiple scales: devices in and around the patient, laboratory, blood tests, omics analyses, medical images, and ancillary information available both prior to and during the hospitalization.

Arguably, one of the most data dependent clinical environments is the intensive care unit (ICU). The ICU environment cares for acutely ill patients. Many patients within the ICU environments, and particularly surgical intensive care units (SICU), are technologically dependent on the life-sustaining devices that surround them. Some of these patients are indeed dependent for their very survival on technologies such as infusion pumps, mechanical ventilators, catheters and so on. Beyond treatment, assessment of prognosis in Critical Care and patient stratification combining different data sources is extremely important in a patient-centric environment.

With the advent and quick uptake of omics technologies in Critical Care, the use of data-based approaches for assistance in diagnosis and prognosis becomes paramount. New approaches to data analysis are thus required, and some of the most interesting ones are currently stemming from the fields of Computational Intelligence (CI) and Machine Learning (ML). This session is particularly interested in the proposal of novel CI and ML approaches and in the discussion of the challenges for the application of the existing ones to problems in Critical Care.

Topics that are of interest to this session include (but are not necessarily limited to):

• Novel applications of existing CI and ML and advanced statistical methods in Critical Care.
• Novel CI and ML techniques for Critical Care.
• CI and ML-based methods to improve model interpretability in a Critical Care context, including data/model visualization techniques.
• Novel CI and ML techniques for dealing with non-structured and heterogeneous data formats in Critical Care.

Biological samples are evolving in time, phases, periods, behaviour. To be able to understand the dynamics, we need to perform time lapse experiments. Todays technique and measurement devices allows to monitor plenty of parameters in semi=controled environment during the experiment. The increase of measured data is enormous. The interpretation requires both, qualitative and quantitative analysis. There are useful methods of biostatistic, multivariate data analysis, and artificial intelligence, namely neural networks, genetic algorithms, and agent based modeling.

In this special section we will provide discussion on broad examples from time lapse experimental design through information and data acquizition, using methods of bioinformatics, biophysics, biostatistic, and artificial inteligence. The aim of this section is to present the possible increase of the data interpretation and the related methods.

Design of Operating rooms department is one of the most complicated tasks of hospital design due to its characteristics and requirements. Patients, staff and tools should have determined passes through operating suite. Many hospitals assume that operating suite is the most important unit in hospital for its high –revenue. Arch Design of these suites is a very critical point to solve an optimized problem in spaces, work flow of clean, dirty and patient in / out in addition to staff together with their relations with adjacent departments. In this study, we will illustrate the most common designs of operating suites and select the most suitable one which satisfy the effectiveness of the operating suite, maximizing throughput, minimizing the costs of necessary, and decreasing the required spaces related to available resources / possibilities. The design should comply with country guidelines, infection control rules, occupational safety and health, and satisfy the maximum befits for patients and staff. A comparative study has been performed on fifteen hospitals and it has been recorded that single input- output technique is the best design.

Motivation and objectives for the session: Operating suite design is very critical task due to its impact. Biomedical Engineer should participate in design and review the workflow and available functions.

Visualization models have shown to be remarkably important in the interpretation of datasets across many fields of study. In the context of Bioinformatics and Computational Biology various tools have been proposed to visualize molecular data and help understand how biological systems work. Despite that, several challenges still persist when faced with complex and dynamic data and major advances are required to correctly manage the multiple dimensions of the data.

The aim of this special session is to bring researchers to present recent and ongoing research activities related with advances on visualization techniques and tools, focused on any major molecular biology problem, with the aim to allow the exchange and share of proposed ideas and experiences with novel visualization metaphors.

Topics of interest include:

• Genome and sequence data
• Omics data (transcriptomics, proteomics, metabolomics)
• Biological networks and pathways
• Time-series data
• Biomedical ontologies
• Macromolecular complexes
• Phylogenetic data
• Biomarker discovery
• Integration of image and omics data for systems biology
• Modeling and simulation of dynamical processes

The National Research Council (NRC) Committee for the Decadal Survey on Biological and Physical Sciences in Space reported that “microbial species that are uncommon, or that have significantly increased or decreased in number, can be studied in a “microbial observatory” on the International Space Station (ISS).” As part of the microbial observatory effort the NRC decadal survey committee suggested that NASA should: “(a) Capitalize on the technological maturity, low cost, and speed of genomic analyses and the rapid generation time of microbes to monitor the evolution of microbial genomic changes in response to the selective pressures present in the spaceflight environment; (b) Study changes in microbial populations from the skin and feces of the astronauts, plant and plant growth media, and environmental samples taken from surfaces and the atmosphere of the ISS; and (c) Establish an experimental program targeted at understanding the influence of the spaceflight environment on defined microbial populations.”

The proposed session will discuss about the state of the art molecular techniques, bioinformatics tools, and their benefit in answering the astronauts and others live in closed systems.

Dr. Kasthuri Venkateswaran (Venkat) is the Senior Research Scientist at NASA – Jet Propulsion Laboratory and supports Biotechnology and Planetary Protection Group. His 39+ years of research encompass marine, food, and environmental microbiology. He is also leading ISS “Microbial Observatory” projects to measure microorganisms associated with U.S. nodes, as well as Kibo Japanese Experiment Modules. He has applied his research in molecular microbial analysis to better understand the ecological aspects of microbes, while conducting field studies in several extreme environments such as deep sea (2,500 m), spacecraft mission (Mars Odyssey, Genesis, Mars Exploration Rovers, Mars Express), assembly facility clean rooms (various NASA and European Space Agency [ESA] facilities), and the space environment in Earth orbit (ISS; ESA Columbus facility). He directs several research and development tasks for the JPL – Mars Program Office, which enables the cleaning, sterilization, and validation of spacecraft components. He directs several NASA competitive awards on the microbial monitoring of spacecraft and associated environments for the Exploration System Mission Directorate, closed habitats like ISS or its Earth analogues for the Human Exploration and Operation Mission Directorate. Also, he provides expertise for non-NASA programs such as commercial agencies (Boeing – airline cabin air measurement), medical industries (tissue and organ transplants processing) in measuring microbial pathogens that are problematic and health related. The bioinformatics databases generated by Venkateswaran’s team are extremely useful in the development of biosensors. Further, these models or information in database are extrapolated to what is known about the spacecraft surfaces and enclosed habitats in an attempt to determine forward contamination as well as develop countermeasures (advance cleaning and sterilization technologies) to control the problematic microbial species. Specifically, his research into the study of clean room environment using state-of-the art molecular analysis coupled with nucleic acid and protein-based microarray, will allow accurate interpretation of data and implementation of planetary protection policies of present missions, helping to set standards for future life-detection missions. The NASA-JPL Science and Technology website will give some of his professional and contact information (http://scienceandtechnology.jpl.nasa.gov/people/k_venkateswaran/). He is the recipient of the first “One NASA Peer Award,” in 2005 for JPL and also fetched the JPL Center–One NASA Peer Award in the same year. He holds several patents (~10 biotechnology related), NASA Space Act Award, 2003; ~25 Novel Technology Report Awards since joined JPL in 1997.

Background: Advances in sequencing techniques have accelerated the data generation at diverse regulatory levels in an unprecedented way. The challenge now is to integrate these data to understand regulation at a systems level. As the sequencing technologies evolve, new tools and resources follow revealing new aspects of complex biological systems.

Aims and scope: This special session will bring together experts from computational biology and machine learning to present recent advances in the development of new tools and resources using next generation sequencing data including novel emerging fields such as single-cell transcriptomics. The session will feature an invited speaker and three/four short talks. To promote emerging leaders of the field, we will select invited speakers who have gained their independence in recent years.

Dr. Anagha Joshi is a group leader in the Division of Developmental Biology at the Roslin Institute, University of Edinburgh. The research in her group includes the development of innovative mathematical and computational approaches for integrating large-scale data, building predictive models and learning new biology, using blood as a model system. Her expertise in next generation sequencing data analysis (RNA and ChIP sequencing) has led to many collaborative projects including FANTOM5 consortium resulting into over 40 peer reviewed publications. She and her team have developed algorithms for building transcriptional regulatory networks using gene expression and high-throughput interaction data in diverse systems, from yeast, to plants, to human cancers.

There is a significant demand for tools and services supporting rehabilitation, wellbeing and healthy life styles while reducing the level of intrusiveness as well as increasing real-time available and reliable results. For example self-monitoring applications need to be improved to move beyond tracking exercise habits and capture a more comprehensive digital footprint of human behavior. This session focusses on primary parameter capturing devices and networks demonstrating advances in sensor development including a customized algorithmic shell research to support diagnostic decisions. Target domains are for example continuous differentiating between mental and physical stress, blood pressure monitoring, sleep quality monitoring, HRV etc.

Genomics is concerned with the sequencing and analysis of an organism’s genome. It is involved in the understanding of how every single gene can affect the entire genome. This goal is mainly afforded using the current, cost-effective, high throughput sequencing technologies. These technologies produce a huge amount of data that usually require high performance computing solutions and opens new ways for the study of genomics, but also transcriptomics, gene expression, and systems biology, among others. The continuous improvements and broader applications on sequencing technologies is producing a continuous new demand of improved high-throughput bioinformatics tools.

Therefore, we invite authors to submit original research, new tools or pipelines, or their update, and review articles on topics helping in the study of genomics in the wider sense, such as (but not limited to):

• Tools for data pre-processing (quality control and filtering)
• Tools for sequence mapping
• Tools for de novo assembly
• Tools for quality check of sequence assembling
• 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 handling and editing complex workflows and pipelines
• Databases for bioinformatics

Long noncoding RNAs (lncRNAs) make up large amounts of the RNA and total genomic repertoire. Studies on the functional characterization of lncRNAs have resulted in data on interactions with their RNA peers, DNA or proteins . Although there has been an increase in evidence for the link between lncRNAs and diverse human diseases; there is a dearth of lncRNA-protein association studies. Additionally, existing methods do not provide theories about the possible molecular causes of such associations linking to diseases. How such regulatory interactions between classes of lncRNAs and proteins would have a significant influence on the organism and disease remains a challenge. There have been good number of bioinformatics approaches that have come up in the recent past exploring these challenges. The idea behind this session is to bring together the wide gamut of researchers who have worked on these methods across different organisms.

The following are the sub-topics of the proposed session which we would like to call for papers.

• LncRNAs in genomes: Annotation and curation
• LncRNA-protein interactions leading to important diseases: Systems Biology approaches
• Identifying lncRNAs with respect to their mechanism and dysregulation in diseases
• LncRNA databases and webservers
• Machine learning approaches and prediction servers

Gamification is a hot topic in many areas as it aims at motivating people to do things driven by different innate needs like the wish to accomplish tasks, to compete against others or to gain something. Those and other motivators are efficiently applied in computer games and could be extraordinarily useful to achieve that patients perform their daily exercises regularly and having fun.

The idea of exergames (Exercise games) is not new, literature reveals that much effort has been done already and with great success. Nevertheless, most applications have been developed for special problems or diseases (i.e. Stroke, Parkinson, Cerebral Palsy etc.) and are not generally applicable. In general, people suffering from severe disabilities and chronical diseases are rarely addressed as a target group. Also, the focus is generally set on the medical achievements, which is correct, but the next step would be to enhance the fun factor because no tool is of much use if the patient is not using it due to boredom or demotivation.

The objective of this special session is to gather new ideas about the combination of need and fun, i.e. find ways to create exercise platforms that fit everybody’s needs, provide access to the therapist for monitoring and configuration, while the patients benefit physically and mentally when having a good time.

Target groups would be people of:

• all ages, while focussing on younger people, who can be involved more easily but are less addressed in the literature.
• all diseases, while focussing on chronical illness and severe disabilities (e.g. muscle dystrophies and atrophies)

The contributions should show advances in at least one of the following areas:

• Adaptability to users with all kinds of problems (e.g. possibility to configure the limbs used to play or playing with facial movements, wheelchair and standing modes, coping with muscle weaknesses etc.)
• Implementation of gaming techniques and special motivators
• Physical or mental exercises, aimed at rehabilitation or daily practice
• Understanding the users, awareness of their level of motivation, fatigue or progress and react accordingly

Motivation and objectives for the session: Diabetes is the 8th most common cause of death, while its treatming relies on technology to process continuously measured glucose levels.

More details will be presented

In many biological applications, multiple data types may be produced to determine genetics, epigenetics and microbiome affecting gene regulation and metabolism. Although, producing multiple data type should create more complete description of the processes under study due to multiple factors such as study design (synchronisation of data production, number of samples, varying conditions) the analysis may leave more unfulfilled promises than synergy expected from the wealth of data.

In this session new would like to address some of the following challenges:

• How to conduct meaningful meta-analysis on historical data?
• How to use biological knowledge (represented in reproducible and interoperable manner) in analysis of large and sparse data sets more effectively?
• How to fill the gap between hypothesis driven mechanistic studies e.g. applying modelling to very well studied biochemical processes and data driven hypothesis free approaches? How omics data can help?
• Beyond meta-transcriptomics and metagenomics: integration and interpretation of microbiome and host data.

We would like to bring together communities concerned with these topics to present state-of-the-art and current cutting edge developments, preferably work under construction or published within last year.

• Objective 1: Presentation and discussion of newest methods
• Objective 2: Round-table discussions on the topics highlighted above and other related suggested by session participants

Additional topic that is not fitting the proposed session but I would love to see addressed is: How to improve open access to the data that is not NGS (e.g. metabolomics, proteomics, plant phenotyping)? For this an active participation of journal editors would be necessary to discuss opportunities to change journal publication policies.

Current scientific methods produce various omics data sets covering many cellular functions. However, these data sets are commonly processed separately due to limited ways how to connect different omics data together for a meaningful analysis. Moreover, it is currently a problem to integrate such data into mathematical model. We are entering the new era of biological research where the main problem is not to obtain the data but to process and analyze them. In this regard, a strong mathematical approach can be very effective (see J. Gunawardena's essay Models in biology: ‘accurate descriptions of our pathetic thinking’, BMC Biology 2014, 12:29).

In this Special Session we focus on big data (omics and biological pathways) related with oncological research. General biological processes that are relevant to cancer can also be studied. Mathematical tools basically mean statistical learning (data mining, inference, prediction), modeling, and simulation. We want to make special emphasis on causality. Closely tied to mathematical tools, efficient computational tools can be considered.