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. Genomics is concerned with the sequencing and analysis of an organism genome taking advantage of the current, cost-effective, high throughput sequencing technologies. Continuous improvement on genomics 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 gene expression studies
• -Tools for Chip-Seq data
• -Integrative workflows and pipelines

Organizers:
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.
Dr. Francisco M. Ortuño, University of Granada,Spain.

Various applications of bioinformatics, system biology and biophysics measurement data mining require proper, accurate, and precise preprocessing or data transformation before the analysis itself. Here, the most important issues are covered by the feature selection and extraction techniques to translate the raw data into the inputs for the machine learning and multi variate statistic algorithms. Even if this is a complex task, it is reducing the problem dimensionality, removal of redundant of irrelevant data, without affecting significantly the present information. The methods and approaches are often conditioned by the physical properties of the measurement process, mathematically congruent description and parameterization, as well as biological aspects of specific tasks. With the current increase of artificial intelligence methods adoption into the bioinformatics problems solutions, it is necessary to understand the conditionality of such algorithms, to choose and use the correct approach and avoid misinterpretations, artefacts, and aliasing affects. The adoption often uses already existing knowledge from different fields, and direct application might underestimates the required conditions and corrupts the analysis results.

In this special section should be provided discussion on the multidisciplinary overlaps, development, implementation, and adoption of feature and selection methods for biological origin of the datasets in order to setup the pipeline from the measurement design through signal processing to knowledge obtaining. The topic should cover theoretical questions, practical examples, and results verifications.

Organizer:
Prof. Jan Urban, Laboratory of Signal and Image Processing, Institute of Complex Systems, South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, Faculty of Fisheries and Protection of Waters, University of South Bohemia, Czech Republic.

Dr. Urban obtained his master degree in Cybernetics and control techniques, and his PhD in Biophysics. He gained work experiences in Sweden, Norway, Austria, USA, and Spain. His work is focused on data acquisition, representation, processing, analysis, and multivariate statistics of complex datasets from biological origin.

Many advancements in medical technology are possible, including developing intelligent systems for the treatment, diagnosis, and prevention of many healthcare issues. The field of surgery is now experiencing an increase in intelligent systems. Medical rules should have been in place during the creation of these technologies to ensure market acceptance. The hospital systems can be connected to mobile devices or other specialized equipment, boosting patient monitoring.

Technology-based tools may monitor, treat, and reduce several health-related issues. The system and concepts discussed here can use sensors found in mobile devices and other sensors found in intelligent environments, and sensors utilized with other equipment. The advancements in this area right now will be incredibly beneficial for treating various ailments.

The main points of this topic are the presentation of cutting-edge, active projects, conceptual definitions of devices, systems, services, and sensor-based advanced healthcare efforts.

Authors are invited to submit complete unpublished papers, which are not under review in any other conference or journal in the following, but not limited to:

• -Assistive technology and adaptive sensing systems
• -Diagnosis and treatment with mobile sensing systems
• -Healthcare self-management systems
• -M-Heath, eHealth, and telemedicine systems
• -Body-wearer/implemented sensing devices
• -Sensing vital medical metrics
• -Sensing for persons with limited capabilities
• -Motion and path-tracking medical systems
• -Artificial intelligence with sensing data
• -Patient empowerment with technological equipment
• -Virtual and augmented reality in medical systems
• -Mobile systems usability and accessibility
• -Medical regulations in mobile systems
• -Medical regulations and privacy

Organizers:

Prof. Ivan Miguel Pires, Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal.

Prof. Norberto Jorge Gonçalves,Universidade de Trás-os-Montes e Alto Douro, Vila Real, Portugal

Prof. Paulo Jorge Coelho,Polytechnic Institute of Leiria, Leiria, Portugal

Molecular dynamics (MD) simulations have become a key method for exploring the dynamic behavior of macromolecules and studying their structure-to-function relationships. In proteomics, they are crucial for extending the understanding of several processes related to protein function, e.g., protein conformational diversity, binding pocket analysis, protein folding, ligand binding, and its influence on signaling, to name a few. Nevertheless, the investigation of the large amounts of information generated by MD simulations is a far from trivial challenge.

In this special session, we call for new research on computational techniques and machine learning (ML) algorithms that can provide efficient solutions for the diverse problems that entail the analysis of MD simulation data in their different areas of application.

Topics of interest include, but are not limited to:

• -Sampling techniques in MD simulations
• -Potential Energy Surfaces
• -Detection of Rare Events
• -Transition Pathways analytics
• -Visualization techniques for MD
• -Feature representations for molecular structures
• -Deep learning architectures for MD simulations
• -Generative Models for MD

Organizers:

Associate Prof. Caroline König, Computer Science Department at Universitat Politècnica de Catalunya, UPC BarcelonaTech, in Barcelona, Spain.

Caroline König is associate professor with the Computer Science Department at Universitat Politècnica de Catalunya, UPC BarcelonaTech, , in Barcelona, Spain, as well as postdoctoral researcher for the ‘ML-PROMOLDYN’ Spanish research project. She is involved in the development of artificial intelligence applications for the analysis of molecular dynamics data in proteomics. Her research interests are on ML approaches for automatic feature extraction from multimodal sequential data, anomaly detection and explainability of ML models.

Prof. Alfredo Vellido, Universitat Politècnica de Catalunya.

Alfredo Vellido is full professor at Universitat Politècnica de Catalunya, UPC BarcelonaTech, in Barcelona, Spain. He is member of the Spanish IABiomed and CIBER-BBN networks and Chair of the Explainable Machine Learning (EXML) Task Force for the IEEE-CIS Data Mining and Big Data Analytics Technical Committee. He has over 25 years of experience on biomedical applications of ML and bioinformatics, areas in which he has published widely.

Signal processing focuses on analysing, modifying and synthesizing signals such as sound, images and biological measurements. Signal processing techniques can be used to improve transmission, storage efficiency and subjective quality and to also emphasize or detect components of interest in a measured signal and is a very relevant topics in medicine.

Any signal transduced from a biological or medical source could be called a biosignal. The signal source could be at the molecular level, cell level, or a systemic or organ level. A wide variety of such signals are commonly encountered in the clinic, research laboratory, and sometimes even at home. Examples include the electrocardiogram (ECG), or electrical activity from the heart; speech signals; the electroencephalogram (EEG), or electrical activity from the brain; evoked potentials (EPs, i.e., auditory, visual, somatosensory, etc.), or electrical responses of the brain to specific peripheral stimulation; the electroneurogram, or field potentials from local regions in the brain; action potential signals from individual neurons or heart cells; the electromyogram (EMG), or electrical activity from the muscle; the electroretinogram from the eye; and so on.

In the other side, medical imaging is the technique and process of creating visual representations of the interior of a body for clinical analysis and medical intervention, as well as visual representation of the function of some organs or tissues (physiology). Medical imaging seeks to reveal internal structures hidden by the skin and bones, as well as to diagnose and treat disease. Medical imaging also establishes a database of normal anatomy and physiology to make it possible to identify abnormalities. Although imaging of removed organs and tissues can be performed for medical reasons, such procedures are usually considered part of pathology instead of medical imaging.

Organizer

Prof. Dr. L.Wang, Computer Science Department at Univ Calif San Diego, USA.

With continuous advancements of biomedical instruments and the associated ability to collect diverse types of valuable biological data, numerous recent research studies have been focusing on how to best extract useful information from the ‘Big biomedical Data’ currently available. While drug design has been one of the most essential areas of biomedical research, the drug design process for the most part has not fully benefited from the recent explosion growth of biological data and bioinformatics algorithms. With the incredible overhead associated with the traditional drug design process in terms of time and cost, new alternative methods, possibly based on computational approaches, are very much needed to propose innovative ways to propose effective drugs and new treatment options. Employing advanced computational tools for drug design and precision treatments has been the focus of many research studies in recent years. For example, drug repurposing has gained significant attention from biomedical researchers and pharmaceutical companies as an exciting new alternative for drug discovery that benefits from the computational approaches. This new development also promises to transform healthcare to focus more on individualized treatments, precision medicine and lower risks of harmful side effects. Other alternative drug design approaches that are based on analytical tools include the use of medicinal natural plants and herbs as well as using genetic data for developing multi-target drugs.

Any signal transduced from a biological or medical source could be called a biosignal. The signal source could be at the molecular level, cell level, or a systemic or organ level. A wide variety of such signals are commonly encountered in the clinic, research laboratory, and sometimes even at home. Examples include the electrocardiogram (ECG), or electrical activity from the heart; speech signals; the electroencephalogram (EEG), or electrical activity from the brain; evoked potentials (EPs, i.e., auditory, visual, somatosensory, etc.), or electrical responses of the brain to specific peripheral stimulation; the electroneurogram, or field potentials from local regions in the brain; action potential signals from individual neurons or heart cells; the electromyogram (EMG), or electrical activity from the muscle; the electroretinogram from the eye; and so on.

Organizer

Prof. Hesham H. Ali, UNO Bioinformatics Core Facility. College of Information Science and Technology. University of Nebraska at Omaha.