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Within recent years, there is an increasing need to train students, from biology and beyond, in quantitative methods that are relevant to cope with data-driven biology. Systems Biology is such a field that places a particular focus on the functional aspect of biology and molecular interacting processes. This paper deals with the conceptual design of a web-based course and its content for educating students in systems biology. We discuss several learning strategies and problems when dealing with structural hypertext patterns that might occur in the context of web-based learning platforms. Finally, we explain the content of the course modules and its features such as its usability and the impact on the learner.

Massively multiplayer online games (MMOGs) played on the Web provide a new form of social, computer-mediated interactions that allow the connection of millions of players worldwide. The rules governing team-based MMOGs are typically complex and nondeterministic giving rise to an intricate dynamical behavior. However, due to the novelty and complexity of MMOGs, their behavior is understudied. In this article, we investigate the MMOG World of Tanks Blitz by using a combined approach based on data science and complex adaptive systems. We analyze data on the population level to get insights into organizational principles of the game and its game mechanics. For this reason, we study the scaling behavior and the predictability of system variables. As a result, we find a power-law behavior on the population level revealing long-range interactions between system variables. Furthermore, we identify and quantify the predictability of summary statistics of the game and its decomposition into explanatory variables. This reveals a heterogeneous progression through the tiers and identifies only a single system variable as key driver for the win rate. [ABSTRACT FROM AUTHOR]

This article explores the concept of causality and its importance in various fields of science and society. Causality refers to the relationship between a cause and an effect, where the effect is a result of the cause. The article explains that causality is studied using mathematical tools such as probability, statistics, and graph theory. It also highlights the difference between correlation and causality, emphasizing that correlation does not imply causation. Understanding causality allows scientists to make predictions, develop treatments, and understand complex systems. The article concludes by discussing the Rubin causal model, which provides a method for estimating causal effects in experiments. [Extracted from the article]

The concept of a digital twin is intriguing as it presents an innovative approach to solving numerous real-world challenges. Initially emerging from the domains of manufacturing and engineering, digital twin research has transcended its origins and now finds applications across a wide range of disciplines. This multidisciplinary expansion has impressively demonstrated the potential of digital twin research. While the simulation aspect of a digital twin is often emphasized, the role of artificial intelligence (AI) and machine learning (ML) is severely understudied. For this reason, in this paper, we highlight the pivotal role of AI and ML for digital twin research. By recognizing that a digital twin is a component of a broader Digital Twin System (DTS), we can fully grasp the diverse applications of AI and ML. In this paper, we explore six AI techniques—(1) optimization (model creation), (2) optimization (model updating), (3) generative modeling, (4) data analytics, (5) predictive analytics and (6) decision making—and their potential to advance applications in health, climate science, and sustainability. [ABSTRACT FROM AUTHOR]

The concept of a digital twin (DT) has gained significant attention in academia and industry because of its perceived potential to address critical global challenges, such as climate change, healthcare, and economic crises. Originally introduced in manufacturing, many attempts have been made to present proper definitions of this concept. Unfortunately, there remains a great deal of confusion surrounding the underlying concept, with many scientists still uncertain about the distinction between a simulation, a mathematical model and a DT. The aim of this paper is to propose a formal definition of a digital twin. To achieve this goal, we utilize a data science framework that facilitates a functional representation of a DT and other components that can be combined together to form a larger entity we refer to as a digital twin system (DTS). In our framework, a DT is an open dynamical system with an updating mechanism, also referred to as complex adaptive system (CAS). Its primary function is to generate data via simulations, ideally, indistinguishable from its physical counterpart. On the other hand, a DTS provides techniques for analyzing data and decision-making based on the generated data. Interestingly, we find that a DTS shares similarities to the principles of general systems theory. This multi-faceted view of a DTS explains its versatility in adapting to a wide range of problems in various application domains such as engineering, manufacturing, urban planning, and personalized medicine. [ABSTRACT FROM AUTHOR]

In this article, we introduce a new counting polynomial, namely the orbit polynomial. It is well-known that this polynomial has a unique positive zero δ in the interval [0, 1]. The aim of this paper is to study the specific properties of this polynomial and then determine the location of this root for several classes of complex networks to compare with other graphical measures. Additionally, we compare the unique positive zero measure with several well-known centrality graph measures. [ABSTRACT FROM AUTHOR]

The idea of a digital twin has recently gained widespread attention. While, so far, it has been used predominantly for problems in engineering and manufacturing, it is believed that a digital twin also holds great promise for applications in medicine and health. However, a problem that severely hampers progress in these fields is the lack of a solid definition of the concept behind a digital twin that would be directly amenable for such big data-driven fields requiring a statistical data analysis. In this paper, we address this problem. We will see that the term 'digital twin', as used in the literature, is like a Matryoshka doll. For this reason, we unstack the concept via a data-centric machine learning perspective, allowing us to define its main components. As a consequence, we suggest to use the term Digital Twin System instead of digital twin because this highlights its complex interconnected substructure. In addition, we address ethical concerns that result from treatment suggestions for patients based on simulated data and a possible lack of explainability of the underling models. [ABSTRACT FROM AUTHOR]

Machine learning is a field composed of various pillars. Traditionally, supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL) are the dominating learning paradigms that inspired the field since the 1950s. Based on these, thousands of different methods have been developed during the last seven decades used in nearly all application domains. However, recently, other learning paradigms are gaining momentum which complement and extend the above learning paradigms significantly. These are multi‐label learning (MLL), semi‐supervised learning (SSL), one‐class classification (OCC), positive‐unlabeled learning (PUL), transfer learning (TL), multi‐task learning (MTL), and one‐shot learning (OSL). The purpose of this article is a systematic discussion of these modern learning paradigms and their connection to the traditional ones. We discuss each of the learning paradigms formally by defining key constituents and paying particular attention to the data requirements for allowing an easy connection to applications. That means, we assume a data‐driven perspective. This perspective will also allow a systematic identification of relations between the individual learning paradigms in the form of a learning‐paradigm graph (LP‐graph). Overall, the LP‐graph establishes a taxonomy among 10 different learning paradigms. This article is categorized under:Technologies > Machine LearningApplication Areas > Science and TechnologyFundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining [ABSTRACT FROM AUTHOR]