Preface to Computational Intelligence Applications in Medicine and Biology

This special edition of the European Science Journal is devoted to applying computational intelligence methods to solving complex problems in medicine and biology

Selected papers from the 15th and 16th international conference on Computational Intelligence Methods for Bioinformatics and Biostatistics

Funding Information: CIBB 2019 was held at the Department of Human and Social Sciences of the University of Bergamo, Italy, from the 4th to the 6th of September 2019 []. The organization of this edition of CIBB was supported by the Department of Informatics, Systems and Communication of the University of Milano-Bicocca, Italy, and by the Institute of Biomedical Technologies of the National Research Council, Italy. Besides the papers focused on computational intelligence methods applied to open problems of bioinformatics and biostatistics, the works submitted to CIBB 2019 dealt with algebraic and computational methods to study RNA behaviour, intelligence methods for molecular characterization and dynamics in translational medicine, modeling and simulation methods for computational biology and systems medicine, and machine learning in healthcare informatics and medical biology. A supplement published in BMC Medical Informatics and Decision Making journal [] collected three revised and extended papers focused on the latter topic.publishersversionpublishe

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Editors: Francisco Ortuño, Ignacio Rojas Chapter, Identification of Biologically Significant Elements Using Correlation Networks in High Performance Computing Environments, co-authored by Kathryn Dempsey Cooper, Sachin Pawaskar, and Hesham Ali, UNO faculty members. The two volume set LNCS 9043 and 9044 constitutes the refereed proceedings of the Third International Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2015, held in Granada, Spain in April 2015. The 134 papers presented were carefully reviewed and selected from 268 submissions. The scope of the conference spans the following areas: bioinformatics for healthcare and diseases, biomedical engineering, biomedical image analysis, biomedical signal analysis, computational genomics, computational proteomics, computational systems for modelling biological processes, eHealth, next generation sequencing and sequence analysis, quantitative and systems pharmacology, Hidden Markov Model (HMM) for biological sequence modeling, advances in computational intelligence for bioinformatics and biomedicine, tools for next generation sequencing data analysis, dynamics networks in system medicine, interdisciplinary puzzles of measurements in biological systems, biological networks, high performance computing in bioinformatics, computational biology and computational chemistry, advances in drug discovery and ambient intelligence for bio emotional computing.https://digitalcommons.unomaha.edu/facultybooks/1323/thumbnail.jp

Network of Time-Multiplexed Optical Parametric Oscillators as a Coherent Ising Machine

Finding the ground states of the Ising Hamiltonian [1] maps to various combinatorial optimization problems in biology, medicine, wireless communications, artificial intelligence, and social network. So far no efficient classical and quantum algorithm is known for these problems, and intensive research is focused on creating physical systems - Ising machines - capable of finding the absolute or approximate ground states of the Ising Hamiltonian [2-6]. Here we report a novel Ising machine using a network of degenerate optical parametric oscillators (OPOs). Spins are represented with above-threshold binary phases of the OPOs and the Ising couplings are realized by mutual injections [7]. The network is implemented in a single OPO ring cavity with multiple trains of femtosecond pulses and configurable mutual couplings, and operates at room temperature. We programed the smallest non-deterministic polynomial time (NP)- hard Ising problem on the machine, and in 1000 runs of the machine no computational error was detected

Seven properties of self-organization in the human brain

The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, 5) functional plasticity, 6) from-local-to-global functional organization, and 7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward

Artificial Neural Network Models for Pattern Discovery from ECG Time Series

Artificial Neural Network (ANN) models have recently become de facto models for deep learning with a wide range of applications spanning from scientific fields such as computer vision, physics, biology, medicine to social life (suggesting preferred movies, shopping lists, etc.). Due to advancements in computer technology and the increased practice of Artificial Intelligence (AI) in medicine and biological research, ANNs have been extensively applied not only to provide quick information about diseases, but also to make diagnostics accurate and cost-effective. We propose an ANN-based model to analyze a patient\u27s electrocardiogram (ECG) data and produce accurate diagnostics regarding possible heart diseases (arrhythmia, myocardial infarct, etc.). Our model is mainly characterized by its simplicity, as it does not require significant computational power to produce the results. We create and test our model using the MIT-BIH and PTB diagnostics datasets, which are real ECG time series datasets from thousands of patients

“In Silico Dreams: How Artificial Intelligence and Biotechnology will Create the Medicines of the Future”

Introduction“In Silico Dreams: How Artificial Intelligence and Biotechnology will Create the Medicines of the Future” is authored by Brian Hilbush whose education in biosciences and career in the biotechnology and pharmaceutical sectors (developing and commercialising emerging and next-generation biotechnologies) offers significant insight into the application of bioinformatics (‘gold’ biotechnology) to understand and solve biological issues using computational techniques.The book opens with a brief introduction setting the scene for what follows, including a brief overview of the book. This is followed by eight chapters that delve into aspects of this field. The first four chapters examine the science and engineering underpinning the field, while the second four chapters look towards the future. Science and Engineering Chapter 1, ‘The Information Revolution’s Impact on Biology’, offers an overview of significant milestones in technology development, highlighting the scientific discipline (for example biology, chemistry, computing and physics) underpinning the technology and how that is woven into the book’s content. This is followed by examples of in silico approaches to ‘omics’ and structural/systems biology, laying the foundations for the subsequent chapters. Chapter 2, ‘A New Era of Artificial Intelligence’, discusses artificial intelligence (AI) and deep or machine learning, highlighting different approaches to machine learning, neural network design concepts and deep learning. It provides a tentative timeline for the integration of AI into medical practice, with an insightful overview of the limitations of AI. Chapter 3, ‘The Long Road to New Medicines’, offers a historical perspective on drug development, particularly natural products and the importance of the efforts of global industry to develop robust therapeutics. It then discusses the impact of biotechnological approaches on the development of medicines in the 21st century. Chapter 4, ‘Gene Editing and the New Tools of Biotechnology’, covers the exciting field of DNA- and RNA-related biotechnologies, with an overview of tools used in precision genome engineering and of clinical trials using gene editing. The Future Chapter 5, ‘Healthcare and the Entrance of the Technology Titans’, examines some of the opportunities being invested in by Alphabet, Amazon, Apple, Microsoft and others in biotechnology-informed healthcare. It highlights in silico approaches applied in healthcare technologies (for example, data, devices, software) to address specific market opportunities and some of the drivers to adopt innovation in the field. Chapter 6, ‘AI-Based Algorithms in Biology and Medicine’, examines opportunities for AI and machine learning in medicine, highlighting the current applications of AI-based algorithms in medicine and some of the respective challenges for the implementation phase of clinical AI. Chapter 7, ‘AI in Drug Discovery and Development’, covers in silico biotechnology methods in pharmaceutical development. Examples include chemoinformatics, elucidating structure-activity relationships, high-throughput screening and virtual screening. Chapter 8, ‘Biotechnology, AI, and Medicine’s Future’, is a forward-looking discussion of how in silico biotechnology may impact the world. It highlights the discovery cycles in biology, pharmaceuticals and medicine; opportunities to build tools to decipher molecular structures and biological systems; and how this leads to the new therapeutic development landscape of engineering medicine. Conclusion The book is accessible to readers with a background in natural and computational sciences working in academia and industry, offering insights into the economic, health and societal impacts that this technology is currently having and will have in the future. While there are other books which cover aspects of the underpinning science in more detail (1–3), what I really enjoyed about this book is that it encapsulates exciting recent developments at a period when there is an explosion of activity in the field.“In Silico Dreams: How Artificial Intelligence and Biotechnology will Create the Medicines of the Future” References “Encyclopedia of Bioinformatics and Computational Biology”, eds. S. Ranganathan, M. Gribskov, K. Nakai, C. Schönbach, Elsevier Inc, Amsterdam, The Netherlands, 2019 “In Silico Chemistry and Biology: Current and Future Prospects”, eds. G. K. Gupta, M. H. Baig, Walter de Gruyter, Berlin, Germany, 2022 J. Harvey, “Computational Chemistry”, Oxford University Press, Oxford, UK, 201

Antismoking campaigns’ perception and gender differences: a comparison among EEG Indices

Human factors’ aim is to understand and evaluate the interactions between people and tasks, technologies, and environment. Among human factors, it is possible then to include the subjective reaction to external stimuli, due to individual’s characteristics and states of mind. These processes are also involved in the perception of antismoking public service announcements (PSAs), the main tool for governments to contrast the first cause of preventable deaths in the world: tobacco addiction. In the light of that, in the present article, it has been investigated through the comparison of different electroencephalographic (EEG) indices a typical item known to be able of influencing PSA perception, that is gender. In order to investigate the neurophysiological underpinnings of such different perception, we tested two PSAs: one with a female character and one with a male character. Furthermore, the experimental sample was divided into men and women, as well as smokers and nonsmokers. The employed EEG indices were the mental engagement (ME: the ratio between beta activity and the sum of alpha and theta activity); the approach/withdrawal (AW: the frontal alpha asymmetry in the alpha band); and the frontal theta activity and the spectral asymmetry index (SASI: the ratio between beta minus theta and beta plus theta). Results suggested that the ME and the AW presented an opposite trend, with smokers showing higher ME and lower AW than nonsmokers. The ME and the frontal theta also evidenced a statistically significant interaction between the kind of the PSA and the gender of the observers; specifically, women showed higher ME and frontal theta activity for the male character PSA. This study then supports the usefulness of the ME and frontal theta for purposes of PSAs targeting on the basis of gender issues and of the ME and the AW and for purposes of PSAs targeting on the basis of smoking habits

Neurophysiological Profile of Antismoking Campaigns

Over the past few decades, antismoking public service announcements (PSAs) have been used by governments to promote healthy behaviours in citizens, for instance, against drinking before the drive and against smoke. Effectiveness of such PSAs has been suggested especially for young persons. By now, PSAs efficacy is still mainly assessed through traditional methods (questionnaires and metrics) and could be performed only after the PSAs broadcasting, leading to waste of economic resources and time in the case of Ineffective PSAs. One possible countermeasure to such ineffective use of PSAs could be promoted by the evaluation of the cerebral reaction to the PSA of particular segments of population (e.g., old, young, and heavy smokers). In addition, it is crucial to gather such cerebral activity in front of PSAs that have been assessed to be effective against smoke (Effective PSAs), comparing results to the cerebral reactions to PSAs that have been certified to be not effective (Ineffective PSAs). &e eventual differences between the cerebral responses toward the two PSA groups will provide crucial information about the possible outcome of new PSAs before to its broadcasting. &is study focused on adult population, by investigating the cerebral reaction to the vision of different PSA images, which have already been shown to be Effective and Ineffective for the promotion of an antismoking behaviour. Results showed how variables as gender and smoking habits can influence the perception of PSA images, and how different communication styles of the antismoking campaigns could facilitate the comprehension of PSA’s message and then enhance the related impac