Scientific origins are information sources that transmit encoded information signals to receivers. Originary sciences identify information preserving receivers and decode the signals to infer their origins. Paradigmatic cases of scientific origination such as the Big Bang, the origins of species, horizontal gene transfer, the origin of the Polynesian potato, and ideational origins in the history of ideas are analyzed to discover what is common to them ontologically and epistemically. Some causes are not origins. Origination supervenes on causation, but has different (...) properties. The unique properties of origins that causes do not share shield theories of origination from the kind of counterexamples and counterintuitive results that challenge comparable theories of causation. The epistemology of origination may serve as a basis for founding a novel epistemic and methodological division of the sciences into originary historical sciences and causal theoretical sciences. (shrink)

I propose an approach that expands philosophical views of scientific change, on the basis of an analysis of contemporary biomedical research and recent developments in the philosophy of scientific change. Focusing on the establishment of the exposome in epidemiology as a case study and the role of data as a context for contrasting views on change, I discuss change at conceptual, methodological, material, and social levels of biomedical epistemology. Available models of change provide key resources to discuss this type of (...) change, but I present the need for an approach that models transfer, alignment, and influence as key processes of change. I develop this as a pragmatic approach to scientific change, where processes might change substantially depending on specific circumstances, thus contributing to and complementing the debate on a crucial epistemological issue. (shrink)

In this paper, we analyse the relation between the use of environmental data in contemporary health sciences and related conceptualisations and operationalisations of the notion of environment. We consider three case studies that exemplify a different selection of environmental data and mode of data integration in data-intensive epidemiology. We argue that the diversification of data sources, their increase in scale and scope, and the application of novel analytic tools have brought about three significant conceptual shifts. First, we discuss the EXPOsOMICS (...) project, an attempt to integrate genomic and environmental data which suggests a reframing of the boundaries between external and internal environments. Second, we explore the MEDMI platform, whose efforts to combine health, environmental and climate data instantiate a reframing and expansion of environmental exposure. Third, we illustrate how extracting epidemiological insights from extensive social data collected by the CIDACS institute yields innovative attributions of causal power to environmental factors. Identifying these shifts highlights the benefits and opportunities of new environmental data, as well as the challenges that such tools bring to understanding and fostering health. It also emphasises the constraints that data selection and accessibility pose to scientific imagination, including how researchers frame key concepts in health-related research. (shrink)

Recently, it has been argued that the use of Big Data transforms the sciences, making data-driven research possible and studying causality redundant. In this paper, I focus on the claim on causal knowledge by examining the Big Data project EXPOsOMICS, whose research is funded by the European Commission and considered capable of improving our understanding of the relation between exposure and disease. While EXPOsOMICS may seem the perfect exemplification of the data-driven view, I show how causal knowledge is necessary for (...) the project, both as a source for handling complexity and as an output for meeting the project’s goals. Consequently, I argue that data-driven claims about causality are fundamentally flawed and causal knowledge should be considered a necessary aspect of Big Data science. In addition, I present the consequences of this result on other data-driven claims, concerning the role of theoretical considerations. I argue that the importance of causal knowledge and other kinds of theoretical engagement in EXPOsOMICS undermine theory-free accounts and suggest alternative ways of framing science based on Big Data. (shrink)

The distinction between distributive and non-distributive profiles figures prominently in current evaluations of the ethical and epistemological risks that are associated with automated profiling practices. The diagnosis that non-distributive profiles may coincidentally situate an individual in the wrong category is often perceived as the central shortcoming of such profiles. According to this diagnosis, most risks can be retraced to the use of non-universal generalisations and various other statistical associations. This article develops a top-down analysis of non-distributive profiles in which this (...) fallibility of non-distributive profiles is no longer central. Instead, it focuses on how profiling creates various asymmetries between an individual data-subject and a profiler. The emergence of informational, interest, and perspectival asymmetries between data-subject and profiler explains how non-distributive profiles weaken the epistemic position of a profiled individual. This alternative analysis provides a more balanced assessment of the epistemic risks associated with non-distributive profiles. (shrink)

It is widely agreed that social factors are related to health outcomes: much research served to establish correlations between classes of social factors on the one hand and classes of disease on the other hand. However, why and how social factors are an active part in the aetiology of disease development is something that is gaining attention only recently in the health sciences and in the medical humanities. In this paper, we advance the view that, just as bio-markers help trace (...) the causal continuum from exposure to disease development at the biological level, socio-markers ought to be introduced and studied in order to trace the social continuum from exposure to disease development. We explain how socio-markers differ from social indicators and how they can be used in combination with bio-markers in order to reconstruct the mixed mechanisms of health and disease, namely mechanisms in which both biological and social factors have an active causal role. (shrink)

In the last decades, Systems Biology (including cancer research) has been driven by technology, statistical modelling and bioinformatics. In this paper we try to bring biological and philosophical thinking back. We thus aim at making diferent traditions of thought compatible: (a) causality in epidemiology and in philosophical theorizing—notably, the “sufcient-component-cause framework” and the “mark transmission” approach; (b) new acquisitions about disease pathogenesis, e.g. the “branched model” in cancer, and the role of biomarkers in this process; (c) the burgeoning of omics (...) research, with a large number of “signals” and of associations that need to be interpreted. In the paper we summarize frst the current views on carcinogenesis, and then explore the relevance of current philosophical interpretations of “cancer causes”. We try to ofer a unifying framework to incorporate biomarkers and omic data into causal models, referring to a position called “evidential pluralism”. According to this view, causal reasoning is based on both “evidence of diference-making” (e.g. associations) and on “evidence of underlying biological mechanisms”. We conceptualize the way scientists detect and trace signals in terms of information transmission, which is a generalization of the mark transmission theory developed by philosopher Wesley Salmon. Our approach is capable of helping us conceptualize how heterogeneous factors such as micro and macro-biological and psycho-social—are causally linked. This is important not only to understand cancer etiology, but also to design public health policies that target the right causal factors at the macro-level. (shrink)

Since the completion of the Human Genome Project (HGP), biomedical sciences have moved away from a gene-centred view and towards a multi-factorial one in which environment, broadly speaking, plays a central role in the determination of human health and disease. Environmental exposures have been shown to be highly prevalent in disease causation. They are considered as complementary to genetic factors in the etiology of diseases, hence the introduction of the concept of the “exposome” as encompassing the totality of human environmental (...) exposures, from conception onwards (Wild in Cancer Epidemiol Biomark Prev 14:1847–1850, 2005), and the launch of the Human Exposome Project (HEP) which aims to complement the HGP. At first sight, and seen as complementary to the genome, the exposome could thus appear as contributing to the rise of novel postgenomic deterministic narratives which place the environment at their core. Is this really the case? If so, what sort of determinism is at work in exposomics research? Is it a case of environmental determinism, and if so, in what sense? Or is it a new sort of deterministic view? In this paper, we first show that causal narratives in exposomics are still very similar to gene-centred deterministic narratives. They correspond to a form of Laplacian determinism and, above all, to what Claude Bernard called the “determinism of a phenomenon”. Second, we introduce the notion of “reversed heuristic determinism” to characterize the specific deterministic narratives present in exposomics. Indeed, the accepted sorts of external environmental exposures conceived as being at the origins of diseases are determined, methodologically speaking, by their identifiable internal and biological markers. We conclude by highlighting the most relevant implications of the presence of this heuristic determinism in exposomics research. (shrink)

Large part of contemporary science is in fact technoscience, in the sense that it crucially depends on several technologies for the generation, collection, and analysis of data. This prompts a re-examination of the relations between science and technologies. In this essay, I advance the view that we’d better move beyond the ‘subordination view’ and the ‘instrumental’ view. The first aims to establish the primacy of science over technology, and the second uses technology instrumentally to support a realist position about theoretical (...) entities. I suggest that we should instead concentrate on how science and technology interact. This will reveal that technology has a poietic character, namely it actively partakes in the production of knowledge. But this poietic character can only be understood within the cognitive activity of scientific communities. Current research in molecular epidemiology, notably the projects funded within the ‘European exposome initiative’, serves as a motivation for such discussion and as an illustration of the claims made. (shrink)