Special Session Proposals

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, Bioinformatics Research Area, Fundación Progreso y Salud, Seville, Spain.


Motivation and Objectives.

Automation and intelligent measurement devices produce multiparametric and structured datasets of huge amount. The incorporation of the multivariate data analysis, artificial intelligence, neural networks, and agent based modeling exceeds the experiences of the classical straightforward evaluation and reveal emergent attributes, dependences, or relations. For the wide spectrum of techniques, genomics, transcriptomics, metabolomics, proteomics, lipidomics, aquaphotomics, etc, it is required superposition of expert knowledge from bioinformatics, biophysics, and biocybernetics. The series of systematic experiments have to deal also with the data pipelines, databases, sharing, and proper description. The integrated concepts offer robust evaluation, verification, and comparison.

In this special section should be provided discussion on novel approaches in measurement, algorithms, methods, software, and data management focused on the omic sciences. The topic should cover practical examples, strong results, and future visions.

Organizer: Dr. Jan Urban

Dr. Jan Urban obtained his engineering degree in Cybernetics in 2005, and his PhD in Biophysics in 2010. As a postdoc he spent time in Norway, USA, Austria, and Gran Canaria. In 2012 he obtained Award of the South Bohemia University rector for the best publication. In 2018 he obtained Award of the faculty dean for the applied research. Since 2014 he his a head of Laboratory of signal and image processing, Institute of Complex Systems. His research is focused on metabolomics, resolution, and noise in biological tasks.


Motivation and Objectives.

Current process of the 4th industrial revolutions affects the also the bioinformatics data acquisition, evaluation, and availability. The novel cyber physical measuring devices are smart, autonomous, and controlled online. Clouds computing covers the data storage and processing, using artificial intelligence methods, thanks to massive computational power. The laboratory and medical praxis should be on the apex of developing, implementing, and testing the novel bioinformatics approaches, techniques, and methods, to produce excellent research results and increase the knowledge.

In this special section should be presented results, concepts, and running research with novel approaches to the bioinformatics, using internet of things (IoT) devices.

Organizer: Antonin Barta

Antonin Barta works at the Faculty of Fishery and Waters Protection. He is involved in the technological projects of the laboratory. He was participating at the COST action – European Aquaponics hub. In 2018 he obtained the deans award for the applied research in IoT biomonitoring. His research is focused on aquaponics, water quality measurements, software and hardware prototype development and metadata management. Zamek 136, Nove Hrady v Jiznich Cechach, 37333, Czech republic

Motivation and Objectives.

In recent years, the next generation sequencing enables us to interrogate the entire genomes, exomes and transcriptomes of tumor samples and obtain high resolution landscapes of the genetic changes at single-nucleotide level. More and more novel methods are proposed for efficient and effective analyses on cancer sequencing data. One of the most important questions in cancer genomics is to differentiate the patterns of the somatic mutational events. Somatic mutations, especially the somatic driver events, are considered to govern the dynamics of clone birth, evolution and proliferation. Recent studies based on cancer sequencing data, across a diversity of solid and hematological disorders, observe that tumor samples are usually both spatially and temporally heterogeneous and are frequently comprised of one or multiple founding clone(s) and a couple of sub-clones. However, there are still several open problems in cancer clonality research, which include 1) the identification of clonality-related genetic alterations, 2) discerning clonal architecture, 3) understanding their phylogenetic relationships and 4) modeling the mathematical and physical mechanisms. Strictly speaking, none of these issues is completely solved, and these issues remain in the active areas of research, where powerful and efficient bioinformatics tools are urgently demanded for better analyses on rapidly accumulating data. This Special Issue aims to publish the novel mathematical and computational approaches and data processing pipelines for cancer sequencing data, with a focus on those for tumor micor-environment and clonal architecture.


  • Cancer genomics
  • Bioinformatics
  • NGS data analysis
  • Tumor micor-environment and clonal architecture

Organizers: Jiayin Wang, Ph.D., Professor

Jiayin Wang, Ph.D., Professor, Department of Computer Science and Technology, Xi’an Jiaotong University.

Xuanping Zhang, Ph.D., Associate Professor

Xuanping Zhang, Ph.D., Associate Professor, Department of Computer Science and Technology, Xi’an Jiaotong University.

Zhongmeng Zhao, Ph.D., Professor

Zhongmeng Zhao, Ph.D., Professor, Department of Computer Science and Technology, Xi’an Jiaotong University

Dr. Jiayin Wang is a professor with Department of Computer Science and Technology of Xi’an Jiaotong University. He holds the distinguished professor of Hundred Talents Program Youth Scholar of Shaanxi Province. Dr. Wang’s research interests focus on the computational problems in clinical decision-making for precision diagnosis and treatment on cancers. He and his group have published more than 20 research articles on top journals, including CELL, Nature Medicine and Nature Communications, and have earned more than 1000 citations from research community.

Motivation and Objectives.

Telemedicine in Smart Homes and remote monitoring is implementing a core research to link up devices and technologies from medicine and informatics. A person’s vital data can be collected in a smart home environment and transferred to medical databases and the professionals. Most often different from clinical approaches, key instruments are specifically tailored devices, multidevices or even wearable devices respecting always individual preferences and non-intrusive paradigms. The proposed session will focus on leading research approaches, prototypes and implemented hardware/software co-designed systems with a clear networking applicability in smart homes with unsupervised scenarios.

Organizers: Dr. Juan Antonio Ortega

University of Seville, ETS Ingeniería Informática, Spain.

Dr. Natividad Martinez Madrid

Reutlingen University, School of Informatics, Germany. Sechenov University, Department of Information and Internet Technologies, Russia

Dr. Ralf Seepold

HTWG Konstanz, Faculty of Computer Science, Germany. 4 Sechenov University, Department of Information and Internet Technologies, Russia

Juan Antonio Ortega obtained the Ph.D. degree in Computer Science in 2000 at the University of Seville in Spain. He is the Director of the Centre of Computer Scientific in Andalusia (Spain) www.cica.es and the head of the research group IDINFOR (TIC223) - Research, Development and Innovation on Computing Science and Full Professor at the University. Our main research topics are: mobility, domotic and assisted systems, ubiquitous computing, time series and global information systems

Natividad Martinez Madrid is head of the Internet of Things laboratory and Director of the AAL-Living Lab at Reutlingen University. Furthermore, she is Professor in the Institute of Digital Medicine at the I.M. Sechenov First Moscow State Medical University (Russia). She is investigating in rule-based systems, wearable devices, big data and medical devices to support empowerment.

Ralf Seepold is Professor at HTWG Konstanz (Germany) and Director of the Ubiquitous Computing Laboratory. Ralf is also Professor at the Sechenov First Moscow State Medical University (Russia). He is leader of the international project ‘Home Health Living Lab’ for medical AAL technologies, cooperating with 30 Universities, industrial partners and care-giving associations. His main research area is Ubiquitous Computing with special focus on medical devices, intelligent environments, e-Health services and telemedicine. His group is developing algorithms and devices for personalized and mobile health diagnostic support.

Motivation and Objectives.

The analysis of DNA sequences is a crucial application area in computational biology. Finding similarity between genes and DNA subsequences provides very important knowledge of their structures and their functions. Clustering as a widely used data mining approach has been carried out to discover similarity between biological sequences . For example, by clustering genes, their functions can be predicted according to the known functions of other genes in the similar clusters. The problem of clustering sequential data can be solved by several standard pattern recognition techniques such as k-means, k-nearest neighbors and the neural networks. However, these algorithms become very complex when observations are sequences with variable lengths, like genes. New optimization algorithm have shown that they can be successfully utilized for biological sequence clustering.

Organizer: Prof. Dr. Mohammad Soruri

Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran. Ferdows Faculty of Engineering, University of Birjand, Birjand, Iran.

Motivation and Objectives.

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. As a result, drug repositioning or 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.

Keywords: Drug design, drug repositioning/repurposing, personalize medicine, data-driven healthcare, computational tools, Biomedical data analytics, Multi-target drugs.

Organizer: Prof. Dr. Hesham H. Ali

UNO Bioinformatics Core Facility College of Information Science and Technology University of Nebraska at Omaha

Brief Bio
Hesham H. Ali is a Professor of Computer Science and Lee and Wilma Seemann Distinguished Dean of the College of Information Science and Technology at the University of Nebraska at Omaha (UNO). He also serves as the director of the UNO Bioinformatics Core Facility that supports a large number of biomedical research projects in Nebraska. He has published numerous articles in various IT areas including scheduling, distributed systems, data analytics, wireless networks, and Bioinformatics. He has also published two books in scheduling and graph algorithms, and several book chapters in Bioinformatics. He has been serving as the PI or Co-PI of several projects funded by NSF, NIH and Nebraska Research Initiative in the areas of data analytics, wireless networks and Bioinformatics. He has also been leading a Research Group that focuses on developing innovative computational approaches to model complex biomedical systems and analyze big bioinformatics data. The research group is currently developing several next generation big data analytics tools for mining various types of large-scale biological and medical data. This includes the development of new graph theoretic models for assembling short reads obtained from high throughput instruments, as well as employing a novel correlation networks approach for analyzing large heterogeneous biological and health data associated with various biomedical research areas, particularly projects associated with infectious diseases, microbiome studies and aging research. He has also been leading two projects for developing secure and energy-aware wireless infrastructure to address tracking and monitoring problems in medical environments, particularly to study mobility profiling for advancing personalized healthcare.