Dynamic Diseases: Mathematical Informed Expert and Knowledge Computing Technology-based Computational Medicine in Complex Systems
University of Massachusetts Chan Medical School, Worcester, MA, USAInterests: Computational methods; Complex systems; Computational complexity; Fractals; Multifractal methods; Fractional methods and their applications; Wavelets and entropy along with their applications; Mathematical neuroscience and biology as well as advanced data analysis in medicine and related domains
Çankaya University, Ankara, Turkey and Institute of Space Sciences, Magurele-Bucharest, RomaniaInterests: Fractional dynamics and its applications in science and engineering; Fractional differential equations; Discrete mathematics; Mathematical physics; Mathematical biology; Computational complexity; Soliton theory; Lie symmetry; Dynamic systems on time scales and the wavelet method and its applications
School of Informatics, University of Leicester, Leicester, UKInterests: Deep learning and medical image analysis; Convolutional neural networks; Graph convolutional networks; Attention networks; Explainable AI; Advanced AI applications; Medical image analysis; Bio-Inspired computing; Pattern recognition; Transfer learning and medical sensors
Department of Neurology and Psychiatry, University of Massachusetts Medical School, Worcester, MA, USAInterests: Stroke outcomes; Its impact in improving stroke and dementia outcomes; Automatic detection of AF for a wristwatch (CoPI), and interactions between stroke and dementia with emphasis on machine learning algorithms
Department of Software, Visual Analytics for Knowledge Laboratory, Sejong University, Seoul, Republic of KoreaInterests: Medical image analysis (brain MRI; diagnostic hysteroscopy and wireless capsule endoscopy); Information security (steganography, encryption, watermarking and image hashing); Video summarization; Computer vision; Fire/smoke scene analysis; IoT (Internet of medical things; Internet of multimedia things) and video surveillance
Dear Colleagues,
Dynamic diseases are characterized by striking changes in the dynamic aspects of bodily functions, which require molecular and cellular medical aspects to understand the etiology of them and develop the right treatments in a life-saving and life quality maintaining way by addressing basic biological mechanisms and technologies timely. They also entail a computational medicine-based integrative approach directed at the analysis of underlying parameters in ambiguous conditions. Computational methodologies and mathematical modeling currently play an increasing role in the investigation of mechanisms that are central to the diagnosis, prognosis, prediction and treatment of dynamic human diseases. These progressive and transformative processes have given rise to computational medicine, which uses advanced mathematical, engineering, computational and simulation-related approaches to model the human body as a complex system that ranges from molecules, cells and tissues to the organs of the entire body. Computational medicine also allows the generation of models based on theory and knowledge that are able to capture singular and individual properties concerning health and disease with the aim of accurate decision-making.
Equifinality posits that multiple paths are directed at and linked to a common end state with the overarching aim of unifying sciences. It is critical in computational medicine and its management, which lies at the interface of computational modeling and medicine. This integration enables individualization and personalization of medical decision making to enhance health outcomes, while at the same time limiting the burden on healthcare. To attain this goal, dynamic precision medicine or personalized medicine, including the tailoring of treatment to each patient’s individual characteristics, focuses on giving the right treatment to the patient at the right time. Hence, anticipation, control and management of the complexity of unexpected events can be achieved based on detailed information obtained from conventional biomarkers, genetic cues, and phenotypic and/or psychosocial characteristics. These are critical for distinguishing amongst patients who display similar clinical features within a sub-group.
Health is considered to be a multidimensional, complex, and adaptive state arising from a myriad of non-linear interactions across micro-, macro- and nano-level variables and a biological blueprint. It extends over a broad spectrum of diseases including neurological and biological diseases, as well as cancer, epidemics, vascular disorders, medical oncology, mental disorders, immune-system disorders and virological problems, amongst many others. Accordingly, the preliminary aim of this special issue is the integration of progressive computational medicine and related disciplines with an orientation towards molecular and cellular elements so that accuracy and timely intervention within such hierarchically layered complex systems can be attained. To address the intricate problems that emerge in dynamic diseases, it is important to develop systems in conjunction with advanced mathematical algorithms that touch on chaos, fractals, multifractional, fractional calculus, machine learning and artificial intelligence amongst others. Through such cross-domain understanding, we anticipate this Special Issue will open up new frontiers in which clinical knowledge and computational methodologies act as a unifying framework to promote further research and development of theories and applications, as well as to serve human health at large.
Potential topics of the special issue include but are not limited to:
- Computational diagnostics
- Computational imaging and simulation technologies in biomedicine
- Computational molecular medicine
- Data analytics-based models
- Dynamic precision medicine
- Advanced medical image/signal processing in molecular and cellular research
- Molecular genetics and epigenetics
- Integrative machine-learning and molecular virology / immunology
- Computational modeling of molecular / cellular complex systems
- Learning-based models in medical imaging (MRI, EEG, X-rays, etc.)
- AI applications in medicine
- Neural systems mimicking neural functioning
- Neural information processing for biological and cellular systems
- Fractals and multifractal methods in dynamic diseases
- Fractional calculus in complex dynamic diseases
- Fractional computing systems
- Non-invasive assessment of disabilities with cellular and molecular orientation
- Fractional dynamic processes in dynamic diseases
- Personalization in practice: dynamic computational modeling
- Computational imaging and simulation technologies in biomedicine
Assis. Prof. Yeliz Karaca, Prof. Dumitru Baleanu, Prof. Yu-Dong Zhang, Prof. Majaz Moonis, Prof. Osvaldo Gervasi, and Assis. Prof. Khan Muhammad
Guest Editors
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