Epistasis plays an important role in the genetic architecture of common human diseases and can be viewed from two perspectives, biological and statistical, each derived from and leading to different assumptions and research strategies. Biological epistasis is the result of physical interactions among biomolecules within gene regulatory networks and biochemical pathways in an individual such that the effect of a gene on a phenotype is dependent on one or more other genes. In contrast, statistical epistasis is defined as deviation from (...) additivity in a mathematical model summarizing the relationship between multilocus genotypes and phenotypic variation in a population. The goal of this essay is to review definitions and examples of biological and statistical epistasis and to explore the relationship between the two. Specifically, we present and discuss the following two questions in the context of human health and disease. First, when does statistical evidence of epistasis in human populations imply underlying biomolecular interactions in the etiology of disease? Second, when do biomolecular interactions produce patterns of statistical epistasis in human populations?Answers to these two reciprocal questions will provide an important framework for using genetic information to improve our ability to diagnose, prevent and treat common human diseases. We propose that systems biology will provide the necessary information for addressing these questions and that model systems such as bacteria, yeast and digital organisms will be a useful place to start. BioEssays 27:637–646, 2005. © 2005 Wiley Periodicals, Inc. (shrink)

Statistics play a critical role in biological and clinical research. To promote logically consistent representation and classification of statistical entities, we have developed the Ontology of Biological and Clinical Statistics (OBCS). OBCS extends the Ontology of Biomedical Investigations (OBI), an OBO Foundry ontology supported by some 20 communities. Currently, OBCS contains 686 terms, including 381 classes imported from OBI and 147 classes specific to OBCS. The goal of this paper is to present OBCS for community critique and to (...) describe a number of use cases designed to illustrate its potential applications. The OBCS project and source code are available at http://obcs.googlecode.com. (shrink)

Statistics play a critical role in biological and clinical research. However, most reports of scientific results in the published literature make it difficult for the reader to reproduce the statistical analyses performed in achieving those results because they provide inadequate documentation of the statistical tests and algorithms applied. The Ontology of Biological and Clinical Statistics (OBCS) is put forward here as a step towards solving this problem. Terms in OBCS, including ‘data collection’, ‘data transformation in statistics’, ‘data (...) visualization’, ‘statistical data analysis’, and ‘drawing a conclusion based on data’, cover the major types of statistical processes used in basic biological research and clinical outcome studies. OBCS is aligned with the Basic Formal Ontology (BFO) and extends the Ontology of Biomedical Investigations (OBI), an OBO (Open Biological and Biomedical Ontologies) Foundry ontology supported by over 20 research communities. We discuss two examples illustrating how the ontology is being applied. In the first (biological) use case, we describe how OBCS was applied to represent the high throughput microarray data analysis of immunological transcriptional profiles in human subjects vaccinated with an influenza vaccine. In the second (clinical outcomes) use case, we applied OBCS to represent the processing of electronic health care data to determine the associations between hospital staffing levels and patient mortality. Our case studies were designed to show how OBCS can be used for the consistent representation of statistical analysis pipelines under two different research paradigms. By representing statistics-related terms and their relations in a rigorous fashion, OBCS facilitates standard data analysis and integration, and supports reproducible biological and clinical research. (shrink)

Charney discusses the growing realization in the postgenomic era that genomic biology deviates from Mendelian assumptions at the heart of genetic heritability and association studies. Given the complexity of genomic biology, how are we to identify meaningful genetic factors that contribute to behavioral? One response is to make genetic variants the focus of biological rather than statistical analyses of behavior.

Matthen (Philos Sci 76(4):464–487, 2009) argues that explanations of evolutionary change that appeal to natural selection are statistically abstractive explanations, explanations that ignore some possible explanatory partitions that in fact impact the outcome. This recognition highlights a difficulty with making selective analyses fully rigorous. Natural selection is not about the details of what happens to any particular organism, nor, by extension, to the details of what happens in any particular population. Since selective accounts focus on tendencies, those factors that impact (...) the actual outcomes but do not impact the tendencies must be excluded. So, in order to properly exclude the factors irrelevant to selection, the relevant factors must be identified, and physical processes, environments, and populations individuated on the basis of being relevantly similar for the purposes of selective accounts. Natural selection, on this view, becomes in part a measure of the robustness of particular kinds of outcomes given variations over some kinds of inputs. (shrink)

The question is discussed how noise gained a functional meaning in the context of biology. According to the common view, noise is considered a disturbance or perturbation. I analyze how this understanding changed and what kind of developments during the last 10 years contributed to the emergence of a new understanding of noise. Results gained during a field study in a synthetic biology laboratory show that the emergence of this new research discipline—its highly interdisciplinary character, its new technologies (...) and novel modeling strategies—provided essential impulses, which led to the observed change in the concept of noise. The laboratory study is combined with a historical analysis, which explores the general question as to how concepts travel between disciplines and, specifically, how noise was transferred from engineering and physics into biology. In the past, scientists, such as Lotka and Goodwin, tried to introduce a statistical mechanics into biology and discussed the problem of “unfitting” concepts. The change in the meaning of the concept can be interpreted as a way of making it fit to the novel context in which it is applied. (shrink)

When Kangas suggested in 1986 that wildlife reserve designs could be much smaller than previously thought, community ecologists attacked his views on methodological grounds (island biogeographical theory is beset with uncertainties) and on conservation grounds (Kangas seemed to encourage deforestation and extinction). Kangas' defenders, like Simberloff, argued that in a situation of biological uncertainty (the degree/type of deforestation-induced extinction), scientists ought to follow the epistemologically conservative course and risk type-II error (the risk of not rejecting a null hypothesis that is (...) false), rather than type-I error. (This is the risk of rejecting a null hypothesis that is true). Kangas' opponents, like Noss, argued that, in a situation of scientific uncertainty, scientists ought to risk type-I, rather than type-II, error. This essay argues that there are different types of rationality appropriate to science and applied science and, therefore, in cases of applied science (like conservation biology), the more conservative course of action is for scientists to risk type-I error. The essay argues further that, on grounds of scientific rationality, Kangas, Simberloff, and others were correct in risking type-II error, but that, on grounds of decision-theoretic rationality, Noss, Waide, and others were correct in risking type-I error. (shrink)

In 1986-1987, a number of ecologists were involved in a dispute over design of wildlife reserves and species losses resulting from deforestation. The battle was played out largely in the pages of the Ecological Society of America Bulletin. The most focused aspect of the controversy began in August 1986 when P. C. Kangas gave a paper at the meetings of the Fourth International Congress of Ecology, held in Syracuse, New York.Using data on trees in Costa Rica and the “objective approach” (...) of species-area curves, Kangas argued that extinction rates due to deforestation in the tropics are actually much lower than most other researchers have alleged (Kangas 1986, p. 194). Ecologists in the audience disagreed with Kangas’ claim that extinction rates were indeed lower, complained that his questionable results would be used to justify more rapid and widespread deforestation, and claimed to have “sharply trounced his [Kangas’] arguments” (Noss 1986, pp. 278-279). (shrink)

This article explores the overlap between systems biology and predictive neuroscience, placing them in their larger context, the contemporary trend of bioinformatic convergence across the sciences. These two domains overlap with respect to their interest in data accumulation and data integration; their reliance on computational statistical correlation; and their translational goals, that is, producing practical fruits and applications from the interscientific cross-pollination that contemporary data-integrative approaches make possible. The interventions that such translational conversations generate are medical and social in (...) nature, and are aimed at both prevention and treatment. It will be argued that such approaches, socially and medically applied, contain potential for conveying both agency-enhancing and agency-diminishing social messages. The article concludes with a call to balance the overwhelmingly quantitative focus characteristic of predictive neuroscience with more qualitative empirical methodologies. This would represent a double helical approach. (shrink)

In classical physics, the world is considered as a matter-based reality, the arrangement of whose parts in time is uniquely determined by certain dynamic laws. By contrast, modern quantum physics reveals that matter is not composed of matter, but reality is merely potentiality. The world has a holistic structure, which is based on fundamental relations and not material objects, admitting more open, indeterministic developments. In this more flexible causal framework, inanimate and animate matter are not to be considered as fundamentally (...) different but as different order structures of the same immaterial entity. In a stable configuration, effectively all the uncertainties are statistically averaged out, thus the unique and deterministic behavior of ordinary inanimate matter is exhibited; whereas in the case of statically unstable but dynamically stable configurations, the “lively” features of the underlying quantum structure have a chance to surface to the mesocopic level and to be connected with what we observe as the phenomenon of life. This supposition can be more explicitly treated and clarified by identifying the electric dipole moment of biomolecules as the ordering parameter of the corresponding macroquantum system. This has important consequences for biology and medicine. In particular, it suggests the existence of a quantum-based “software,” essential perhaps for the logistics of biological processes, which are hidden behind the matter/energy-based “hardware” that alone is considered in the conventional approach. In medicine it opens an opportunity to build bridges between conventional medicine based essentially on the classical paradigm, and the various forms of complementary medicine, which have strong affinities with the new features of the modern holistic worldview. (shrink)

Though only established as a discipline since the 1970s, philosophy of biology has already triggered investigations about its own history The Oxford handbook of philosophy of biology, Oxford University Press, New York, pp 11–33, 2008). When it comes to assessing the road since travelled—the research questions that have been pursued—manuals and ontologies also offer specific viewpoints, highlighting dedicated domains of inquiry and select work. In this article, we propose to approach the history of the philosophy of biology (...) with a complementary data-driven perspective that makes use of statistical algorithms applied to the complete full-text corpus of one major journal of the field—Biology and Philosophy—from its launch in 1986 up until 2017. By running text-mining and topic-modeling algorithms, we identified 67 key research topics that span across these 32 years. We also investigated the evolution of these topics over time and their fluctuating significance in the journal articles. Our results concur with known episodes or traits of the discipline—for instance, the significance of evolution-related topics or the decrease of articles with a marked historical dimension—but also highlight a diversity of topics that is much richer than what is usually acknowledged. (shrink)

Though only established as a discipline since the 1970s, philosophy of biology has already triggered investigations about its own history The Oxford handbook of philosophy of biology, Oxford University Press, New York, pp 11–33, 2008). When it comes to assessing the road since travelled—the research questions that have been pursued—manuals and ontologies also offer specific viewpoints, highlighting dedicated domains of inquiry and select work. In this article, we propose to approach the history of the philosophy of biology (...) with a complementary data-driven perspective that makes use of statistical algorithms applied to the complete full-text corpus of one major journal of the field—Biology and Philosophy—from its launch in 1986 up until 2017. By running text-mining and topic-modeling algorithms, we identified 67 key research topics that span across these 32 years. We also investigated the evolution of these topics over time and their fluctuating significance in the journal articles. Our results concur with known episodes or traits of the discipline—for instance, the significance of evolution-related topics or the decrease of articles with a marked historical dimension—but also highlight a diversity of topics that is much richer than what is usually acknowledged. (shrink)

The most compelling extant accounts of explanation casts all explanations as causal. Yet there are sciences, theoretical population biology in particular, that explain their phenomena by appeal to statistical, non-causal properties of ensembles. I develop a generalised account of explanation. An explanation serves two functions: metaphysical and cognitive. The metaphysical function is discharged by identifying a counterfactually robust invariance relation between explanans event and explanandum. The cognitive function is discharged by providing an appropriate description of this relation. I offer (...) examples of explanations from portfolio theory and population genetics that meet this characterisation. In each case the invariance relation holds between a statistical property of an ensemble and a change in structure of the ensemble. In neither case, however, does the statistical property cause the outcome it explains. There are genuine statistical, non-causal scientific explanations. (shrink)

The phenomenon of regression toward the mean is notoriously liable to be overlooked or misunderstood; regression fallacies are easy to commit. But even when regression phenomena are duly recognized, it remains perplexing how they can feature in explanations. This article develops a philosophical account of regression explanations as “statistically autonomous” explanations that cannot be deepened by adducing details about causal histories, even if the explananda as such are embedded in the causal structure of the world. That regression explanations have statistical (...) autonomy was first suggested by Ian Hacking and has recently been defended and elaborated by André Ariew, Yasha Rohwer, and Collin Rice. However, I will argue that these analyses fail to capture what regression’s statistical autonomy consists in and how it sets regression explanations apart from other kinds of explanation. The alternative account I develop also shows what is amiss with a recent denial of regression’s statistical autonomy. Marc Lange has argued that facts that can be explained as regression phenomena can in principle also be explained by citing a conjunction of causal histories. The account of regression explanation developed here shows that his argument is based on a misunderstanding of the nature of statistical autonomy. (shrink)

In recent years, several remarkable results have shown that certain theorems of finite combinatorics are unprovable in certain logical systems. These developments have been instrumental in stimulating research in both areas, with the interface between logic and combinatorics being especially important because of its relation to crucial issues in the foundations of mathematics which were raised by the work of Kurt Godel. Because of the diversity of the lines of research that have begun to shed light on these issues, there (...) was a need for a comprehensive overview which would tie the lines together. This volume fills that need by presenting a balanced mixture of high quality expository and research articles that were presented at the August 1985 AMS-IMS-SIAM Joint Summer Research Conference, held at Humboldt State University in Arcata, California.With an introductory survey to put the works into an appropriate context, the collection consists of papers dealing with various aspects of 'unprovable theorems and fast-growing functions'. Among the topics addressed are: ordinal notations, the dynamical systems approach to Ramsey theory, Hindman's finite sums theorem and related ultrafilters, well quasiordering theory, uncountable combinatorics, nonstandard models of set theory, and a length-of-proof analysis of Godel's incompleteness theorem. Many of the articles bring the reader to the frontiers of research in this area, and most assume familiarity with combinatorics and/or mathematical logic only at the senior undergraduate or first-year graduate level. (shrink)

Biomarkers are widely used not only as prognostic or diagnostic indicators, or as surrogate markers of disease in clinical trials, but also to formulate theories of pathogenesis. We identify two problems in the use of biomarkers in mechanistic studies. The first problem arises in the case of multifactorial diseases, where different combinations of multiple causes result in patient heterogeneity. The second problem arises when a pathogenic mediator is difficult to measure. This is the case of the oxidative stress (OS) theory (...) of disease, where the causal components are reactive oxygen species (ROS) that have very short half-lives. In this case, it is usual to measure the traces left by the reaction of ROS with biological molecules, rather than the ROS themselves. Borrowing from the philosophical theories of signs, we look at the different facets of biomarkers and discuss their different value and meaning in multifactorial diseases and system medicine to inform their use in patient stratification in personalized medicine. (shrink)

The twentieth century witnessed a dramatic increase in the use of statistics by biologists, including systematists. The modern synthesis and new systematics stimulated this development, particularly after World War II. The rise of "the statistical frame of mind " resulted in a rethinking of the relationship between biological and mathematical points of view, the roles of objectivity and subjectivity in systematic research, the implications of new computing technologies, and the place of systematics among the biological disciplines.

The paper discusses the scope and influence of eugenics in defining the scientific programme of statistics and the impact of the evolution of biology on social scientists. It argues that eugenics was instrumental in providing a bridge between sciences, and therefore created both the impulse and the institutions necessary for the birth of modern statistics in its applications first to biology and then to the social sciences. Looking at the question from the point of view of (...) the history of statistics and the social sciences, and mostly concentrating on evidence from the British debates, the paper discusses how these disciplines became emancipated from eugenics precisely because of the inspiration of biology. It also relates how social scientists were fascinated and perplexed by the innovations taking place in statistical theory and practice. (shrink)

The paper discusses the scope and influence of eugenics in defining the scientific programme of statistics and the impact of the evolution of biology on social scientists. It argues that eugenics was instrumental in providing a bridge between sciences, and therefore created both the impulse and the institutions necessary for the birth of modern statistics in its applications first to biology and then to the social sciences. Looking at the question from the point of view of (...) the history of statistics and the social sciences, and mostly concentrating on evidence from the British debates, the paper discusses how these disciplines became emancipated from eugenics precisely because of the inspiration of biology. It also relates how social scientists were fascinated and perplexed by the innovations taking place in statistical theory and practice. (shrink)

Biometrics has done damage with levels of R or p or Student’s t. The damage widened with Ronald A. Fisher’s victory in the 1920s and 1930s in devising mechanical methods of “testing,” against methods of common sense and scientific impact, “oomph.” The scale along which one would measure oomph is particularly clear in biomedical sciences: life or death. Cardiovascular epidemiology, to take one example, combines with gusto the “fallacy of the transposed conditional” and what we call the “sizeless stare” of (...) statistical significance. Some medical editors have battled against the 5% philosophy, as did, for example, Kenneth Rothman, the founder of Epidemiology. And decades ago a sensible few in education, ecology, and sociology initiated a “significance test controversy.” But, grantors, journal referees, and tenure committees in the statistical sciences had faith that probability spaces can substitute for scientific judgment. A finding of p <.05 is deemed to be “better” for variable X than p <.11 for variable Y. It is not. It depends on the oomph of X and Y—the effect size, size judged in the light of how much it matters for scientific or clinical purposes. In 1995 a Cancer Trialists’ Collaborative Group, for example, came to a rare consensus on effect size: 10 different studies had agreed that a certain drug for treating prostate cancer can increase patient survival by 12%. An 11th study published in the New England Journal in 1998 dismissed the drug. The dismissal was based on a t-test, not on what William Gosset had called, against Ronald A. Fisher’s machinery, “real” error. (shrink)

Should we think of our universe as law-governed and “clockwork”-like or as disorderly and “soup”-like? Alternatively, should we consciously and intentionally synthesize these two extreme pictures? More concretely, how deterministic are the postulated causes and how rigid are the modeled properties of the best statistical methodologies used in the biological and behavioral sciences? The charge of this entry is to explore thinking about causation in the temporal evolution of biological and behavioral systems. Regression analysis and path analysis are simply explicated (...) with reference to a thought experiment of painting three universes (clockwork, soup, and conscious) useful for imagining our actual universe. Attention to historical, structural, and mechanistic explanatory perspectives broadens the palette of methodologies available for analyzing determinism in, and explanation of, biological and behavioral systems. Each justified methodology provides a partial perspective on complex reality. Is a total explanation of any system ever possible, and what would it require? (shrink)

We extend the comparatively simple processes of group symmetry-breaking in physical systems to groupoid/equivalence class phase transitions characterizing adiabatically, piecewise stationary, information transmission in prebiotic, biological, and social phenomena: High vs. Low probability paths $$\rightarrow$$ Interior and Exterior Interact $$\rightarrow$$ Multiple Interacting Tunable Workspaces Application to nonstationary processes seems possible via generalizations of the symmetry algebra, for example, to semigroupoids. The dynamic probability models explored here can be transformed into statistical tools for the analysis of real-time and other data across (...) a spectrum of important disciplines confronted by biological and other forms of cognition and their dysfunctions. (shrink)

In this essay, I consider the use of mathematical statistics in the study of biological systems in the field, using as case studies the work of Ruth Geyer Shaw and her colleagues at the University of Minnesota. To address practical issues, like how to enhance prairie restoration, and how to prepare for (and perhaps prevent) the effect of rapid climate change, she and her colleagues combine mathematical modeling and intensive data collection in the field. Using ANOVA and the more (...) versatile approach of Aster Models, they show the effects of inbreeding (which follows from the fragmentation of prairie) to lower the fitness of plant populations. They study the genetic variance of plants in stable populations, which is important when that population must deal with changing conditions. And they study the consequences of rapid climate change: even good genetic variance and initial fitness may not save a given population from extinction. This leads to a new alliance of science with local, state, federal and global politics. (shrink)

Historians are now generally agreed that the Darwinian recognition and institutionalization of the polygenist position was more than merely nominal.194 Wallace, Vogt, and Huxley had led the way, and we may add Galton (1869) to the list of those leading Darwinians who incorporated a good deal of polygenist thinking into their interpretions of human history and racial differences.195 Eventually “Mr. Darwin himself,” as Hunt had suggested he might, consolidated the Darwinian endorsement of many features of polygenism. Darwin's Descent of Man (...) was published in the same year that the Anthropological Institute was founded, and it was no coincidence that it was broadly congruent with Knoxian/ Anthropological race science. Recent scholarship has stressed the derivative character of the Descent, and Darwin's views on race were clearly influenced by the earlier interpretations of the abovecited Darwinians.196However, although the Descent was written in the light of the anthropological struggles of the 1860s, it is essential to acknowledge its origins in Darwin's notebooks of the late 1830s and early 1840s. A good deal of the congruence between Darwinian and Knoxian conceptions of race may be traced back to these early notebook constructions. As these document, Darwin, like Knox, brought to his very earliest conceptions of human evolution a “commitment to the idea of human races as discrete biological units with distinct moral and mental traits.”197 The young Darwin had been concerned with the same sorts of questions on racial biological and cultural differences that preoccupied Knox around the same time, and he was committed to as ruthless a naturalism. Apart from their individual and independent debts to Quetelet's “moral statistics,” both Darwin and Knox drew heavily on the general themes of struggle and adaptation in the contemporary “common context” of biological and social thought.198 Given their common context, the broad general similarities between the Knoxian laws of race antagonism and subordination and the Darwinian struggle for existence between races need occasion no strained historical explanation of direct influence.199Nevertheless, in more explicit ways, the Descent does show the conflation of Knoxian/Anthropological and Darwinian racial views, and Darwin located his discussion of these issues squarely within the dispute “of late years” between polygenists and monogenists.200 His mature views on race were shaped by the contemporaneous confrontations and negotiations between the Darwinians and the Anthropologicals. It is within this context that the minor historical puzzle of Darwin's failure to acknowledge Knox's “generic descent” may be explained. Apart from the difficulties of integration and interpretation of his scattered theoretical writings, Knox, through his adoption by Hunt and the Anthropologicals, became identified with anti-Darwinism and therefore with antievolutionism.201 Moreover, Knox, the disreputable and marginal “savage radical” and lately resurrected and equally unsavory “Anthropological,” was hardly an acceptable “precursor.” Yet, paradoxically, it was via the antithetical medium of the Anthropological platform that Knox's race science made an indirect and unacknowledged, but lasting, impact on the Darwinian anthropological model.In the Descent, Darwin argued that racial traits arose very early in the prehistory of man, were not biologically adaptive, and were therefore relatively fixed in character. By viewing race formation as a distant and closed episode of human history, Darwin endorsed the Knoxian categories of race as fixed and unalterable types. Although he thought it irrelevant whether human races were called species or subspecies, he conceded more to the Knoxian view than Huxley by granting that a naturalist confronted for the first time by specimens of Negro and European man “might feel himself fully justified in ranking the races of man as distinct species.”202 Consistent with the Knoxian interpretation, struggle, competition, and survival occurred between racial units rather than between individuals and, in Darwin's view, accounted for the superiority of the Anglo-Saxon and the inevitable triumph of the more intellectual and moral races over the lower and more degraded ones.Darwin was as insistent as Knox on the biological basis of intellectual and moral differences, and, through his tendency to reduce social and cultural differences to biology, he maintained the essential Knoxian/Anthropological link between race and culture.203 For above all, the Descent did much more than offer a naturalistic explanation of human evolution: it proffered social interpretation, justification, and prescription, and its timely appearance gave a powerful boost to the “moralizing naturalism” of Huxley and Galton, and to Spencer's “Social Darwinism.”204 We may draw a straight line from Knox's “moral anatomy,” through Hunt's “anthropology,” and on to “Social Darwinism” and the “social surgeons” of the eugenics movement.The Darwinians did not, of course, we their tendency to naturalize existing economic and social relations to Knox or Hunt and the Anthropologicals—they were simply reflecting the same general intellectual trend that had affected Knox and the Anthropologicals as well. And in the larger context, the forces that had created a climate receptive to Knox's racism had intensified: in the seventies, the need to justify white imperialism and class and racial inequalities was greater than ever. Scientific racism no longer appeared an aberration but the very essence of the scientific study of man, taking on a newfound respectability in the “new” evolutionary anthropology. But in more specific ways, through the struggle between the Darwinians and Anthropologicals for scientific and ideological hegemony, Knox's “moral anatomy” was institutionalized and perpetuated in late Victorian scientific racism. In the process, the delicate balance that Knox had maintained between his radicalism and his racism was outweighed by conservative institutional and social needs, and his “moral anatomy” was retooled — first by Hunt, and then by the Darwinians — to fit those needs. (shrink)

The epistemology which sees intra-specific and intra-group heterogenization, symbiotization, interactive pattern-generating and change as basic principles produces types of theories and research strategies different from the epistemology based on the notions of intra-specific and intra-group uniformity, competition and stabilization. In the uniformistic view, individual variations have been reduced mainly either to statistical deviations from the mean or to dominance relationship. On the other hand in the heterogenistic view, mutual beneficial interactions between qualitatively heterogeneous individuals within a group is regarded as (...) indispensable for the increase of the level of behavioral and biological sophistication and evolution. This article compares five epistemologies including the two mentioned above in the context of the current epistemological transition in science, particularly due to the emergence of non-equilibrium thermodynamics in physics and differentiation-amplifying reciprocal causal models in mathematics and engineering, both of which consider the universe as pattern-generating, information-creating and evolving. (shrink)

Unsupervised statistical learning is the standard setting for the development of the only advanced visual system that is both highly sophisticated and versatile, and extensively studied: that of monkeys and humans. In this extended abstract, we invoke philosophical observations, computational arguments, behavioral data and neurobiological findings to explain why computer vision researchers should care about (1) unsupervised learning, (2) statistical inference, and (3) the visual brain. We then outline a neuromorphic approach to structural primitive learning motivated by these considerations, survey (...) a range of neurobiological findings and behavioral data consistent with it, and conclude by mentioning some of the more challenging directions for future research. (shrink)

Combining information from two or possibly several blocks of data is gaining increased attention and importance in several areas of science and industry. Typical examples can be found in chemistry, spectroscopy, metabolomics, genomics, systems biology and sensory science. Many methods and procedures have been proposed and used in practice. The area goes under different names: data integration, data fusion, multiblock analyses, multiset analyses and a few more. This book is an attempt to give an up-to-date treatment of the most (...) used and important methods within an important branch of the area; namely methods based on so-called components or latent variables. These methods have already obtained an enormous attention in for instance chemometrics, bioinformatics, machine learning and sensometrics and have proved to be important both for prediction and interpretation. The book is primarily a description of methodologies, but most of the methods will be illustrated by examples from the above-mentioned areas. The book is written such that both users of the methods as well as method developers will hopefully find sections of interest. In the end of the book there is a description of a software package developed particularly for the book. This package is freely available in R and covers many of the methods discussed. To distinguish the different type of methods from each other, the book is divided into five parts. Part I is introduction and preliminary concepts. Part II is the core of the book containing the main unsupervised and supervised methods. Part III deals with more complex structures and, finally, Part IV discusses alternative unsupervised and supervised methods. The book ends with Part V discussing the available software. Our recommendation for reading the book are as follows. A minimum read of the book would involve chapters 1, 2, 3, 5 and 7. Chapters 4, 6 and 8 are more specialized and chapters 9 and 10 contain methods we think are more advanced or less obvious to use. We feel privileged to have so many friendly colleagues who were willing to spend their time on helping us to improve the book by reading separate chapters. We would like to express our thanks to: Rasmus Bro, Margriet Hendriks, Ulf Indahl, Henk Kiers, Ingrid MaÌ⁽ge, Federico Marini, AÌ⁽smund Rinnan, Rosaria Romano, Lars Erik Solberg, Marieke Timmerman, Oliver Tomic, Johan Westerhuis and Barry Wise. Of course, the correctness of the final text is fully our responsibility! (shrink)

Normality judgements are frequently used in everyday communication as well as in biological and social science. Moreover they became increasingly relevant to formal logic as part of defeasible reasoning. This paper distinguishes different kinds of normality statements. It is argued that normality laws like “Birds can normally fly” should be understood essentially in a statistical way. The argument has basically two parts: firstly, a statistical semantic core is mandatory for a descriptive reading of normality in order to explain the logical (...) features of normality laws. Secondly, a statistical justification of normality statements can be derived by game theoretic considerations if the normality law is understood as communication convention. (shrink)

Really statistical explanation is a hitherto neglected form of noncausal scientific explanation. Explanations in population biology that appeal to drift are RS explanations. An RS explanation supplies a kind of understanding that a causal explanation of the same result cannot supply. Roughly speaking, an RS explanation shows the result to be mere statistical fallout.

Several decades ago, Christopher Boorse formulated an influential statistical theory of normative biological functions but it has often been claimed that his theory suffers from insuperable problems such as an inability to handle cases of epidemic and universal diseases. This paper develops a new statistical theory of normative functions that is capable of dealing with the notorious problem of epidemic and universal diseases. The theory is also more detailed than its predecessors and offers other important advantages over them. It is (...) argued here that statistical theories of biological functions should not be so quickly dismissed. (shrink)

The core idea of statistical accounts of biological functions is that to function normally is to provide a statistically typical contribution to some goal state of the organism. In this way, statistical accounts purport to naturalize the teleological notion of function in terms of statistical facts. Boorse’s, 542–573, 1977) original biostatistical account was criticized for failing to distinguish functions from malfunctions. Recently, many have attempted to circumvent the criticism, 519–541, 2012, Journal of Medicine and Philosophy, 39, 634–647, 2014). Here, I (...) review such attempts and find them inadequate. The reason, ultimately, is that functional attribution depends on how traits would behave in relevant situations, a condition that resists statistical characterizations in terms of how they typically behave. This, I conclude, undermines the attempt to naturalize functions in statistical terms. (shrink)

This paper examines the subversive role of statistics paleontology at the end of the 19th and the beginning of the 20th centuries. In particular, I will focus on German paleontology and its relationship with statistics. I argue that in paleontology, the quantitative method was questioned and strongly limited by the first decade of the 20th century because, as its opponents noted, when the fossil record is treated statistically, it was found to generate results openly in conflict with the (...) Darwinian theory of evolution. Essentially, statistics questions the gradual mode of evolution and the role of natural selection. The main objections to statistics were addressed during the meetings at the Kaiserlich-Königliche Geologische Reichsanstalt in Vienna in the 1880s. After having introduced the statistical treatment of the fossil record, I will use the works of Charles Léo Lesquereux, Joachim Barrande, and Henry Shaler Williams to compare the objections raised in Vienna with how the statistical treatment of the data worked in practice. Furthermore, I will discuss the criticisms of Melchior Neumayr, one of the leading German opponents of statistical paleontology, to show why, and to what extent, statistics were questioned in Vienna. The final part of this paper considers what paleontologists can derive from a statistical notion of data: the necessity of opening a discussion about the completeness and nature of the paleontological data. The Vienna discussion about which method paleontologists should follow offers an interesting case study in order to understand the epistemic tensions within paleontology surrounding Darwin’s theory as well as the variety of non-Darwinian alternatives that emerged from the statistical treatment of the fossil record at the end of the 19th century. (shrink)

Biology is seen not merely as a privileged oppressor of women but as a co-victim of masculinist social assumptions. We see feminist critique as one of the normative controls that any scientist must perform whenever analyzing data, and we seek to demonstrate what has happened when this control has not been utilized. Narratives of fertilization and sex determination traditionally have been modeled on the cultural patterns of male/female interaction, leading to gender associations being placed on cells and their components. (...) We also find that when gender biases are controlled, new perceptions of these intracellular and extracellular relationships emerge. (shrink)

From the end of the First World War, a broad discussion took place within the framework of the revived German constitutional teaching on the question of the physical normality of man. The founder of the so-called statistical concept of normality, which preceded the still widespread normal (reference) interval concept, is H. Rautmann, who gave it the character of a tool for discriminating between health and disease. Among some of his successors (Bauer, Borchardt, Günther), however, it was considered more a means (...) of establishing a type, without supposing any precise relation between the frequency of a character in the population and the probability of the occurrence of disease. The concept of a statistical norm as a certain region of the variation range of a character determined by the parameters of Gaussian distribution was criticized both by the supporters of the ideal norm (Hildebrandt) and those who were in favour of a ‘personal’ norm (Grote). The underlying motifs of these three conceptions of normality influenced German constitutional doctrine until after the end of the Second World War, but without a satisfactory solution to the diagnosis of physical normality being found. Since the 1950s, world medicine has moved more and more in the direction of prevention, with the emphasis on a study of individual dispositions to disease and its precursors. In this connection a new view of health has gained importance whereby it is considered a smoothly gradated condition, not sharply distinguished from disease (‘continual’ model of health and disease as opposed to the previous ‘alternative’ model). The purpose of diagnostic characters is no longer merely to place patients in clearly defined categories as healthy or affected by one disease or another, but has taken on the function of indices of the disposition to disease among those who exhibit ‘gross normality’. Discrimination between the alternative and continuous models allows a clarification to be made of the sources of the confusion in which the pre-war concept of statistical normality had found itself. Today many exceptions are known to the rule that the functional optimum lies in the region of the population mean, both for the population as a whole and for individuals; and immense variability has been found in the manner in which individuals in the population attain health. Thus a distinction between health and disease by statistical means alone (such as establishing some sort of species design) is not possible at all. The ideal of a personal norm is pursued today through the concept of a multivariate norm backed up by modern data processing methods, though it was anticipated in principle by Kaup even in the 1920s. CiteULike Connotea Del.icio.us What's this? (shrink)