The pharmacophore kernel for virtual screening with support vector machines

We introduce a family of positive definite kernels specifically optimized for the manipulation of 3D structures of molecules with kernel methods. The kernels are based on the comparison of the three-points pharmacophores present in the 3D structures of molecul es, a set of molecular features known to be particularly relevant for virtual screening applications. We present a computationally demanding exact implementation of these kernels, as well as fast approximations related to the classical fingerprint-based approa ches. Experimental results suggest that this new approach outperforms state-of-the-art algorithms based on the 2D structure of mol ecules for the detection of inhibitors of several drug targets

A COMPREHENSIVE GEOSPATIAL KNOWLEDGE DISCOVERY FRAMEWORK FOR SPATIAL ASSOCIATION RULE MINING

Continuous advances in modern data collection techniques help spatial scientists gain access to massive and high-resolution spatial and spatio-temporal data. Thus there is an urgent need to develop effective and efficient methods seeking to find unknown and useful information embedded in big-data datasets of unprecedentedly large size (e.g., millions of observations), high dimensionality (e.g., hundreds of variables), and complexity (e.g., heterogeneous data sources, space–time dynamics, multivariate connections, explicit and implicit spatial relations and interactions). Responding to this line of development, this research focuses on the utilization of the association rule (AR) mining technique for a geospatial knowledge discovery process. Prior attempts have sidestepped the complexity of the spatial dependence structure embedded in the studied phenomenon. Thus, adopting association rule mining in spatial analysis is rather problematic. Interestingly, a very similar predicament afflicts spatial regression analysis with a spatial weight matrix that would be assigned a priori, without validation on the specific domain of application. Besides, a dependable geospatial knowledge discovery process necessitates algorithms supporting automatic and robust but accurate procedures for the evaluation of mined results. Surprisingly, this has received little attention in the context of spatial association rule mining. To remedy the existing deficiencies mentioned above, the foremost goal for this research is to construct a comprehensive geospatial knowledge discovery framework using spatial association rule mining for the detection of spatial patterns embedded in geospatial databases and to demonstrate its application within the domain of crime analysis. It is the first attempt at delivering a complete geo-spatial knowledge discovery framework using spatial association rule mining

Exploring Text Mining and Analytics for Applications in Public Security: An in-depth dive into a systematic literature review

Text mining and related analytics emerge as a technological approach to support human activities in extracting useful knowledge through texts in several formats. From a managerial point of view, it can help organizations in planning and decision-making processes, providing information that was not previously evident through textual materials produced internally or even externally. In this context, within the public/governmental scope, public security agencies are great beneficiaries of the tools associated with text mining, in several aspects, from applications in the criminal area to the collection of people's opinions and sentiments about the actions taken to promote their welfare. This article reports details of a systematic literature review focused on identifying the main areas of text mining application in public security, the most recurrent technological tools, and future research directions. The searches covered four major article bases (Scopus, Web of Science, IEEE Xplore, and ACM Digital Library), selecting 194 materials published between 2014 and the first half of 2021, among journals, conferences, and book chapters. There were several findings concerning the targets of the literature review, as presented in the results of this article

Experimentalist Equal Protection

Elsewhere Garrett and Liebman have recounted that though James Madison is considered the Father of the Constitution, his progeny disappointed him because it was defenseless against self-government\u27s mortal disease -the oppression of minorities by local majorities-because the Framers rejected the radical structural approach to equal protection that Madison proposed. Nor did the framers of the Fourteenth Amendment\u27s Equal Protection Clause and federal courts enforcing it adopt a solution Madison would have considered effectual. This Article explores recent subconstitutional innovations in governance and public administration that may finally bring the nation within reach of the constitutional polity Madison envisioned To explain how Madisonian governance mechanisms can solve the problem of equal protection, the authors turn to the thinking of another homegrown practical philosopher who was ahead of his time, John Dewey. Dewey sets out what he calls an experimentalist problem-solving method for curing the equal protection ills Madison diagnosed In two core civil rights contexts, public school reform and workplace discrimination, solutions both Madisonian and Deweyan already point the way to an experimentalist equal protection regime that remains well within our reach. Such experimentalism may not only open our rigid, tepidly enforced equal protection doctrine to an evolving, problem-solving approach, but in the process transform democratic institutions and community

Scientific inquiry and the causes of civil war : the feasibility thesis and beyond

This Master's Thesis uses Collier, Hoeffler, and Rohner's (2009) Feasibility Thesis as a field for a thorough inquiry into the question of how we can best study the causes of civil war. We advocate a pluralist research strategy, with set-theoretic (especially fuzzy-set) methods as a key component, as the most promising strategy for the study of the causes of civil war

#ChronicPain: Automated Building of a Chronic Pain Cohort from Twitter Using Machine Learning

Background: Due to the high burden of chronic pain, and the detrimental public health consequences of its treatment with opioids, there is a high-priority need to identify effective alternative therapies. Social media is a potentially valuable resource for knowledge about self-reported therapies by chronic pain sufferers. Methods: We attempted to (a) verify the presence of large-scale chronic pain-related chatter on Twitter, (b) develop natural language processing and machine learning methods for automatically detecting self-disclosures, (c) collect longitudinal data posted by them, and (d) semiautomatically analyze the types of chronic pain-related information reported by them. We collected data using chronic pain-related hashtags and keywords and manually annotated 4,998 posts to indicate if they were self-reports of chronic pain experiences. We trained and evaluated several state-of-the-art supervised text classification models and deployed the best-performing classifier. We collected all publicly available posts from detected cohort members and conducted manual and natural language processing-driven descriptive analyses. Results: Interannotator agreement for the binary annotation was 0.82 (Cohen’s kappa). The RoBERTa model performed best (F1 score: 0.84; 95% confidence interval: 0.80 to 0.89), and we used this model to classify all collected unlabeled posts. We discovered 22,795 self-reported chronic pain sufferers and collected over 3 million of their past posts. Further analyses revealed information about, but not limited to, alternative treatments, patient sentiments about treatments, side effects, and self-management strategies. Conclusion: Our social media based approach will result in an automatically growing large cohort over time, and the data can be leveraged to identify effective opioid-alternative therapies for diverse chronic pain types

Critical realism and the ‘ontological politics of drug policy’

This article explores the question of what we can consider to be real in drug policy. It examines two increasingly common aspects of drug policy analysis; radical constructionist critique and successionist data science. It shows how researchers using these assumptions have produced interesting findings, but also demonstrates their theoretical incoherence, based on their shared ‘flat ontology’. The radical constructionist claim that reality is produced within research methods – as seen in some qualitative studies - is shown to be unsustainably self-defeating. It is analytically ‘paralyzing’. This leads to two inconsistencies in radical constructionist studies; empirical ambivalence and ersatz epistemic egalitarianism. The Humean successionist approach of econometric data science is also shown to be unsustainable, and unable to provide explanations of identified patterns in data. Four consequent, limiting characteristics of this type of drug policy research are discussed: causal inference at a distance, monofinality, limited causal imagination, and overly confident causal claims. The article goes on to describe the critical realist approach towards ‘depth ontology’ and ‘generative causation’. It provides examples of how this approach is deployed in critical realist reviews and discourse analysis of drug policy. It concludes by arguing that critical realism enables more deeply explanatory, methodologically eclectic and democratically inclusive analysis of drug policy development and effects