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While historians have analyzed and discussed the fragmentation of life in transatlantic societies by focusing on economics, law, or politics, we propose that a focus on biomedicine in the period since 1970 adds important perspectives on the shifting public role of science and of medicine. Biomedicine (the convergence of biological research and of medicine within the life sciences) moved to the center of public debates after the end of the Cold War. The papers in this special issue highlight how it became a prism through which societies, governments, and states renegotiated the roles of the medical and scientific professions, the opportunities, risks, and limits associated with scientific research and medical technologies, the responsibilities of individuals and of the professions in making life-or-death decisions, and important norms shaping the lives of individuals, families, and communities. Historians have analyzed and discussed the impact of market-oriented governing rationalities and of shifting discourses about the societal roles of individuals by focusing on politics, economics, or law. The contributors to this special issue propose to expand the focus towards biomedicine in order to include developments that otherwise would remain distinct from evolving narratives in recent contemporary history. [ABSTRACT FROM AUTHOR]

Media coverage of dementia can influence public and professional attitudes towards the syndrome, shaping societal knowledge of dementia and impacting how people with dementia are cared for. This paper reports on a study of news articles about dementia published in the British press in the years 2012–2017. The analysis combines the tools of corpus linguistics, a methodology for quantitatively surveying a vast amount of electronic linguistic data, with the qualitative perspectives of Critical Discourse Analysis, which seeks to uncover dominant discourses and ideologies. The most salient discourse that emerged from this analysis was the portrayal of dementia in biomedical terms, with a particular focus on the pathological processes of dementia, and pharmaceutical treatments and research. Keywords relating to this discourse are interrogated in detail, illuminating the linguistic strategies through which the pathology of dementia and people with dementia are depicted. This study highlights the challenges that this type of reporting presents to people living with dementia and their families, and points to the relevance of a discursive approach to understanding societal perceptions of dementia. [ABSTRACT FROM AUTHOR]

Social Constructivism about the disease concept has generally been taken to ignore the fundamental biological reality underlying diseases, as well as to fall foul of several apparently compelling objections. In this paper, I explain how the metaphysical relation of grounding can be used to tie a socially constructed account of diseases and their classification to their underlying biological and behavioural states. I then generalize the position by disambiguating several varieties of normativism, including a particularly strong 'placeholder' version of social constructivism, and showing that the grounding approach is available to each. I go on to provide what I believe to be the first attempt at a full semantics for disease-talk and disagreement, before using the placeholder to demonstrate on that basis that the most troublesome objections to normativism can be avoided even by very strong versions of the position. [ABSTRACT FROM AUTHOR]

The advantage of examining causality from the perspective of modelling is thus that it puts us naturally closer to the practice of the sciences. This means being able to set up an interdisciplinary dialogue that contrasts and compares modelling practices in different fields, say economics and biology, medicine and statistics, climate change and physics. It also means that it helps philosophers looking for questions that go beyond the narrow 'what-is-causality' or 'what-are-relata' and thus puts causality right at the centre of a complex crossroad: epistemology/methodology, metaphysics, politics/ethics. This special issue collects nine papers that touch upon various scientific fields, from system biology to medicine to quantum mechanics to economics, and different questions, from explanation and prediction to the role of both true and false assumptions in modelling. [ABSTRACT FROM AUTHOR]

Philosophers of science have recently focused on the scientific activity of modeling phenomena, and explicated several of its properties, as well as the activities embedded into it. A first approach to modeling has been elaborated in terms of representing a target system: yet other epistemic functions, such as producing data or detecting phenomena, are at least as relevant. Additional useful distinctions have emerged, such as the one between phenomenological and mechanistic models. In biological sciences, besides mathematical models, models now come in three forms: in vivo, in vitro and in silico. Each has been investigated separately, and many specific problems they raised have been laid out. Another relevant distinction is disciplinary: do models differ in significant ways according to the discipline involved-medicine or biology, evolutionary biology or earth science? Focusing on either this threefold distinction or the disciplinary boundaries reveals that they might not be sufficient from a philosophical perspective. On the contrary, focusing on the interaction between these various kinds of models, some interesting forms of explanation come to the fore, as is exemplified by the papers included in this issue. On the other hand, a focus on the use of models, rather than on their content, shows that the distinction between biological and medical models is theoretically sound. [ABSTRACT FROM AUTHOR]

The fields of medicine science and health informatics have made great progress recently and have led to in-depth analytics that is demanded by generation, collection and accumulation of massive data. Meanwhile, we are entering a new period where novel technologies are starting to analyze and explore knowledge from tremendous amount of data, bringing limitless potential for information growth. One fact that cannot be ignored is that the techniques of machine learning and deep learning applications play a more significant role in the success of bioinformatics exploration from biological data point of view, and a linkage is emphasized and established to bridge these two data analytics techniques and bioinformatics in both industry and academia. This survey concentrates on the review of recent researches using data mining and deep learning approaches for analyzing the specific domain knowledge of bioinformatics. The authors give a brief but pithy summarization of numerous data mining algorithms used for preprocessing, classification and clustering as well as various optimized neural network architectures in deep learning methods, and their advantages and disadvantages in the practical applications are also discussed and compared in terms of their industrial usage. It is believed that in this review paper, valuable insights are provided for those who are dedicated to start using data analytics methods in bioinformatics. [ABSTRACT FROM AUTHOR]

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