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

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.

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.

Smart healthcare plays an important role towards providing robust solutions especially for COVID19 related problems. The identified solution helps in solving global and local problems worldwide. Collection and interpretation of data worldwide and systematic research helps in identifying the potential solutions as well as predict the futuristic issues. This special session emphasizes the potential problems and the related solutions .

  • Smart wearable healthcare
  • Microbiological analysis
  • Minimal invasive sensors
  • Biomedical waste management
  • Drug induced therapy
  • Early prediction and diagnosis
  • Biostatistical driven solution
  • Explainable AI and Deep learning driven solutions

Prof. Dr N,Sriraam, Professor,Dept of Medical Electronics Head R&D, MSRIT India, Ramaiah Institute of Technology, Bangalore -560 054, 080 23603122 Ext:155,

Medical sensors are micro devices containing several parts mainly including micro-tubes, micro-valves, biological/body fluids (blood, plasma, saliva, etc.), and chemical materials (reagents and other materials). Microfluidic is an interdisciplinary field that involves the science and technology of fluid flow through systems with micro scales. Computational systems and engineering simulation seem essential from start to the end of the medical sensor design and development process. The main advantages of computational systems (AI, CFD, etc.) in medical sensors design and development are as follow:

  • Improvement and optimization of design;
  • Acceleration in medical device innovation;
  • Reduction of cost and failure risk;
  • Shortens time of production and regulatory approval processes

The main objectives:

  • Determining the role of computational systems (AI, CFD, etc.) in medical sensors design and development;
  • Determining role of simulation to optimize the analysis process and design of medical micro-sensors;
  • Using computational fluid dynamics (CFD) to analyze medical micro sensors;
  • Using computational systems to combine engineering, biology, chemical and other criteria.

Prof. Patrizia Piro, Prof. Patrizia Piro is a Full Professor at the Department of Civil Engineering of University of Calabria (Italy). She is currently Vice-Rector of University of Calabria (Italy) and chair of the "Urban Hydraulic Center Studies (CSDU)" in Italy. Her research principally focuses on urban water management, with main focus on real time control of urban drainage networks and on LID (Low Impact Development) systems implementation. Her main interests are: combined sewer overflows, water pollution, flood risk mitigation, water saving, urban stormwater management, water treatment, urban drainage, low impact development, soil science, experimental analysis, and numerical modeling. She is currently: Guest Editor of Special Issue "Innovative, Smart and Sustainable Solutions for Urban Storm water Management" in Water (an Open Access Journal by MDPI); Guest Editor of Special Issue "Sustainable Urban Stormwater Management" in Sustainability (an Open Access Journal by MDPI); Guest Editor of Special Issue "Soil–Water–City Nexus in Urban Environment: Experimental Investigations and Numerical Analysis in Urban Hydrology Science" in Hydrology (an Open Access Journal by MDPI); Guest Editor of Special Issue "Climate Change Impacts on Urban Stormwater Management" in Atmosphere (an Open Access Journal by MDPI). Department of Civil Engineering, University of Calabria, 87036 Rende, Italy

Dr. Behrouz Pirouz, Behrouz Pirouz, is a researcher in the Department of Civil Engineering of the University of Calabria (Italy). His main research interests are sustainability of water and energy, sustainability of renewable energy, novel methods in using solar energy with high efficiency, CFD Modeling of medical sensors, and AI application in different areas, such as COVID-19 pandemic analysis. Currently involved in several research project activities, Editor and referee of several academic journals. Department of Civil Engineering, University of Calabria, 87036 Rende, Italy,