Text Mining Adoption for Pharmacogenomics-based Drug Discovery in a Large Pharmaceutical Company: a Case Study

Text mining can help pharmacogenomics researchers reduce information overload hindering pharmacogenomics-based drug discovery (PGx-DD) because it can aid in the generation of rich novel information from large collections of diverse scientific literature and research data. The present study aims to understand text mining adoption and innovation for PGx-DD in the pharmaceutical industry. The study re-frames text mining as an approach to automate the generation of novel information, reviews successful exemplary text mining applications, and examines a case study of a leading pharmaceutical company within the novelty generation framework. The case study demonstrates that the Unified Theory of Acceptance and Use of Technology (UTAUT) model (Venkatesh, Morris, Davis, & Davis, 2003) does not account for conceptual barriers to adoption and innovation. By Everett Rogers' Diffusion of innovation theory (1983), the case study subject is more of an early adopter rather than an innovator. In order to fulfill the promise of PGx-DD, drug companies may need to re-conceptualize text mining by focusing on its capacity to generate novel high-quality information and subsequently return to a higher-risk path of innovation

Concept Based Knowledge Discovery from Biomedical Literature

Philosophiae Doctor - PhDThis thesis describes and introduces novel methods for knowledge discovery and presents a software system that is able to extract information from biomedical literature, review interesting connections between various biomedical concepts and in so doing, generates new hypotheses. The experimental results obtained by using methods described in this thesis, are compared to currently published results obtained by other methods and a number of case studies are described. This thesis shows how the technology, resented can be integrated with the researchers own knowledge, experimentation and observations for optimal progression of scientific research.South Afric

The Trilogy of Science: Filling the Knowledge Management Gap with Knowledge Science and Theory

The international knowledge management field has different ways of investigating, developing, believing, and studying knowledge management. Knowledge management (KM) is distinguished deductively by know-how, and its intangible nature establishes different approaches to KM concepts, practices, and developments. Exploratory research and theoretical principles have formed functional intelligences from 1896 to 2013, leading to a knowledge management knowledge science (KMKS) concept that derived a grounded theory of knowledge activity (KAT). This study addressed the impact of knowledge production problems on KM practice. The purpose of this qualitative meta-analysis study was to fit KM practice within the framework of knowledge science (KS) study. Themed questions and research variables focused on field mechanisms, operative functions, principle theory, and relationships of KMKS. The action research used by American practitioners has not established a formal structure for KS. The meta-data-analysis examined 385 transdisciplinary peer-reviewed articles using social science, service science, and systems science databases, with a selection of interdisciplinary studies that had a practice-research-theory framework. Key attributes utilizing Boolean limiters, words, phrases and publication dates, along with triangulation, language analysis and coding through analytic software identified commonalities of the data under study. Findings reflect that KM has not become a theoretically saturated field. KS as the forensic science of KM creates a paradigm shift, causes social change that averts rapid shifts in management direction and uncertainty, and connects KM philosophy and science of knowledge. These findings have social change implications by informing the work of managers and academics to generate a methodical applied science

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence

Human-Intelligence and Machine-Intelligence Decision Governance Formal Ontology

Since the beginning of the human race, decision making and rational thinking played a pivotal role for mankind to either exist and succeed or fail and become extinct. Self-awareness, cognitive thinking, creativity, and emotional magnitude allowed us to advance civilization and to take further steps toward achieving previously unreachable goals. From the invention of wheels to rockets and telegraph to satellite, all technological ventures went through many upgrades and updates. Recently, increasing computer CPU power and memory capacity contributed to smarter and faster computing appliances that, in turn, have accelerated the integration into and use of artificial intelligence (AI) in organizational processes and everyday life. Artificial intelligence can now be found in a wide range of organizational systems including healthcare and medical diagnosis, automated stock trading, robotic production, telecommunications, space explorations, and homeland security. Self-driving cars and drones are just the latest extensions of AI. This thrust of AI into organizations and daily life rests on the AI community’s unstated assumption of its ability to completely replicate human learning and intelligence in AI. Unfortunately, even today the AI community is not close to completely coding and emulating human intelligence into machines. Despite the revolution of digital and technology in the applications level, there has been little to no research in addressing the question of decision making governance in human-intelligent and machine-intelligent (HI-MI) systems. There also exists no foundational, core reference, or domain ontologies for HI-MI decision governance systems. Further, in absence of an expert reference base or body of knowledge (BoK) integrated with an ontological framework, decision makers must rely on best practices or standards that differ from organization to organization and government to government, contributing to systems failure in complex mission critical situations. It is still debatable whether and when human or machine decision capacity should govern or when a joint human-intelligence and machine-intelligence (HI-MI) decision capacity is required in any given decision situation. To address this deficiency, this research establishes a formal, top level foundational ontology of HI-MI decision governance in parallel with a grounded theory based body of knowledge which forms the theoretical foundation of a systemic HI-MI decision governance framework

The acquisition of inductive constraints

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 197-216).Human learners routinely make inductive inferences, or inferences that go beyond the data they have observed. Inferences like these must be supported by constraints, some of which are innate, although others are almost certainly learned. This thesis presents a hierarchical Bayesian framework that helps to explain the nature, use and acquisition of inductive constraints. Hierarchical Bayesian models include multiple levels of abstraction, and the representations at the upper levels place constraints on the representations at the lower levels. The probabilistic nature of these models allows them to make statistical inferences at multiple levels of abstraction. In particular, they show how knowledge can be acquired at levels quite remote from the data of experience--levels where the representations learned are naturally described as inductive constraints. Hierarchical Bayesian models can address inductive problems from many domains but this thesis focuses on models that address three aspects of high-level cognition. The first model is sensitive to patterns of feature variability, and acquires constraints similar to the shape bias in word learning. The second model acquires causal schemata--systems of abstract causal knowledge that allow learners to discover causal relationships given very sparse data. The final model discovers the structural form of a domain--for instance, it discovers whether the relationships between a set of entities are best described by a tree, a chain, a ring, or some other kind of representation. The hierarchical Bayesian approach captures several principles that go beyond traditional formulations of learning theory.(cont.) It supports learning at multiple levels of abstraction, it handles structured representations, and it helps to explain how learning can succeed given sparse and noisy data. Principles like these are needed to explain how humans acquire rich systems of knowledge, and hierarchical Bayesian models point the way towards a modern learning theory that is better able to capture the sophistication of human learning.by Charles Kemp.Ph.D

Reengineering legacy software products into software product line

Ph.DDOCTOR OF PHILOSOPH