Contra-Analysis: Prioritizing Meaningful Effect Size in Scientific Research

At every phase of scientific research, scientists must decide how to allocate limited resources to pursue the research inquiries with the greatest potential. This prioritization dictates which controlled interventions are studied, awarded funding, published, reproduced with repeated experiments, investigated in related contexts, and translated for societal use. There are many factors that influence this decision-making, but interventions with larger effect size are often favored because they exert the greatest influence on the system studied. To inform these decisions, scientists must compare effect size across studies with dissimilar experiment designs to identify the interventions with the largest effect. These studies are often only loosely related in nature, using experiments with a combination of different populations, conditions, timepoints, measurement techniques, and experiment models that measure the same phenomenon with a continuous variable. We name this assessment contra-analysis and propose to use credible intervals of the relative difference in means to compare effect size across studies in a meritocracy between competing interventions. We propose a data visualization, the contra plot, that allows scientists to score and rank effect size between studies that measure the same phenomenon, aid in determining an appropriate threshold for meaningful effect, and perform hypothesis tests to determine which interventions have meaningful effect size. We illustrate the use of contra plots with real biomedical research data. Contra-analysis promotes a practical interpretation of effect size and facilitates the prioritization of scientific research.Comment: 4 figures, 8000 word

Understanding of biological teleology from a naturalistic perspective

To the extent that teleological thinking is metaphysically suspect, many theorists attempt to shift the stigma of functional explanations by reducing function ascriptions, and aim thus to de-legitimise an appeal to teleological causal relations in an analysis of function. The point is to dispel the mystery which envelops the application of function concepts by reformulating biological functional explanations so as to dispense with teleology. My project is to interrogate the success with which teleological explanations have thus been eliminated in the biological sciences, and, over the course of this thesis, I conclude that a kind of teleological causation nevertheless remains the most adequate explanatory ground of natural products. My proposal is that functional explanations are causal explanations for the presence and maintenance of self-reproducing systems. I contend that, insofar as the attribution of function presupposes the valuation of a function-bearing system as a causal necessity for its constituent parts, functional explanation references distinct and irreducible holistic properties. Using Kantian metaphysics to frame the discussion, this thesis aims first to explore critically the subject of functional characterisations of biological phenomena, and second, the metaphysical basis of modern science. Its chief contributions to the philosophical function debate reside in proposing novel arguments in justification of what I consider is an improved formulation of an attempted definition of biological function, in which teleological causal powers are explicitly recognised and accommodated in functional explanation. Moreover, this thesis attempts a naturalistic reconstruction of the metaphysical entailments of the real causality of a whol

Automatically identifying facet roles from comparative structures to support biomedical text summarization

Within the context of biomedical scholarly articles, comparison sentences represent a rhetorical structure commonly used to communicate findings. More generally, comparison sentences are rich with information about how the properties of one or more entities relate one another. So far, in the biomedical domain, the emphasis has been on recognizing comparative sentences in the text. This dissertation goes beyond sentence-level recognition and aims to automate the identification of the integral parts of a comparison sentence which are called comparative facets and include: compared entities, the basis or the endpoint of comparison as well as the result or the relationship that binds the entities and the basis. Only the sentences that contain each of the four facets are of interest in this thesis. With respect to the first compared entity, the system achieves an average F1 on a random sample of short (between 11 and 21 words long) sentences of 0.65; medium (between 22 and <= 28 words) sentences 0.70; long (between 29 and <=36 words) sentences 0.60 and very long (more than 36 words), 0.60. With respect to the basis of comparison prediction (the endpoint), the average F1 measure ranged from 0.66 on short, 0.57 on medium, 0.56 on long, and 0.50 on very long sentences. The average F1 achieved with respect to the second entity compared ranged from 0.91 on short, 0.85 on medium, 0.81 on long and 0.72 on very long sentences. In the area of semantic relation identification, the performance achieved was also sensitive to sentence length: the average F1 measure on short sentences was 0.80; it was 0.71, 0.56, and 0.51 on medium, long, and very long sentences respectively. Thus, the methods developed in this dissertation work better on sentences that are shorter (<= 28 words) and on those that do not contain multiple claims or disjunctive conjunctions. When applied to a previously unseen collection of breast cancer articles, the performance achieved with respect to the identification of compared entities and the endpoint was comparable to the results achieved on the collection that was used for building and testing the models. This result is promising with respect to the potential of this model being applied on other collections of scholarly articles in the biomedical sciences

EXPLORATION OF DOMAIN-SPECIFIC KNOWLEDGE GRAPHS FOR TESTABLE HYPOTHESIS GENERATION

In the span of a decade, we have brought about a fundamental shift in the way we structure, organize, store, and conceptualize biomedical datasets. Data which had previously been siloed has been gathered, organized, and aggregated into central repositories, interlinked with each other by categorizing these vast sums of knowledge into well defined ontologies. These interlinked databases, better known as knowledge graphs, have come to redefine our ability to explore the current state of our knowledge, answer complex questions about how objects relate to each other, and invent novel connections in vastly different research disciplines. With these knowledge graphs, new ideas can be quickly formulated, instead of relying upon the insight of a single scientist or small team of experts, these ideas can be made leveraging the vast historical catalog of research progress that has been captured in biomedical databases. Knowledge graphs can be used to propose hypotheses which narrow the nearly infinite array of possible explorations which can link any pair of ideas to only those which have some historical and practical considerations. In this way, we hope to utilize these knowledge graphs to produce hypotheses, promote those which are viable, and provide them to biomedical experts. In this work, we aim to develop methodologies to produce meaningful hypotheses using these graphs as inputs. We approach this problem by (i) utilizing intrinsic mathematical properties of the intermediate nodes along a pathways, (ii) translating existing biomedical ideas into graphical structures, and (iii) incorporating niche domain-specific biomedical datasets to explore domain problems. We have shown the ability of these methods to produce practical and useful hypotheses and pathways which can be utilized by experts for immediate exploration.Doctor of Philosoph

BIOMEDICAL ONTOLOGIES: EXAMINING ASPECTS OF INTEGRATION ACROSS BREAST CANCER KNOWLEDGE DOMAINS

The key ideas developed in this thesis lie at the intersection of epistemology, philosophy of molecular biology, medicine, and computer science. I examine how the epistemic and pragmatic needs of agents distributed across particular scientific disciplines influence the domain-specific reasoning, classification, and representation of breast cancer. The motivation to undertake an interdisciplinary approach, while addressing the problems of knowledge integration, originates in the peculiarity of the integrative endeavour of sciences that is fostered by information technologies and ontology engineering methods. I analyse what knowledge integration in this new field means and how it is possible to integrate diverse knowledge domains, such as clinical and molecular. I examine the extent and character of the integration achieved through the application of biomedical ontologies. While particular disciplines target certain aspects of breast cancer-related phenomena, biomedical ontologies target biomedical knowledge about phenomena that is often captured within diverse classificatory systems and domain-specific representations. In order to integrate dispersed pieces of knowledge, which is distributed across assorted research domains and knowledgebases, ontology engineers need to deal with the heterogeneity of terminological, conceptual, and practical aims that are not always shared among the domains. Accordingly, I analyse the specificities, similarities, and diversities across the clinical and biomedical domain conceptualisations and classifications of breast cancer. Instead of favouring a unifying approach to knowledge integration, my analysis shows that heterogeneous classifications and representations originate from different epistemic and pragmatic needs, each of which brings a fruitful insight into the problem. Thus, while embracing a pluralistic view on the ontologies that are capturing various aspects of knowledge, I argue that the resulting integration should be understood in terms of a coordinated social effort to bring knowledge together as needed and when needed, rather than in terms of a unity that represents domain-specific knowledge in a uniform manner. Furthermore, I characterise biomedical ontologies and knowledgebases as a novel socio-technological medium that allows representational interoperability across the domains. As an example, which also marks my own contribution to the collaborative efforts, I present an ontology for HER2+ breast cancer phenotypes that integrates clinical and molecular knowledge in an explicit way. Through this and a number of other examples, I specify how biomedical ontologies support a mutual enrichment of knowledge across the domains, thereby enabling the application of molecular knowledge into the clinics