Bisociative knowledge discovery for microarray data analysis

Peer reviewe

Discovery of Novel Term Associations in a Document Collection

Non peer reviewe

Discovering context-specific relationships from biological literature by using multi-level context terms

<p>Abstract</p> <p>Background</p> <p>The Swanson's ABC model is powerful to infer hidden relationships buried in biological literature. However, the model is inadequate to infer relations with context information. In addition, the model generates a very large amount of candidates from biological text, and it is a semi-automatic, labor-intensive technique requiring human expert's manual input. To tackle these problems, we incorporate context terms to infer relations between AB interactions and BC interactions.</p> <p>Methods</p> <p>We propose 3 steps to discover meaningful hidden relationships between drugs and diseases: 1) multi-level (gene, drug, disease, symptom) entity recognition, 2) interaction extraction (drug-gene, gene-disease) from literature, 3) context vector based similarity score calculation. Subsequently, we evaluate our hypothesis with the datasets of the "Alzheimer's disease" related 77,711 PubMed abstracts. As golden standards, PharmGKB and CTD databases are used. Evaluation is conducted in 2 ways: first, comparing precision of the proposed method and the previous method and second, analysing top 10 ranked results to examine whether highly ranked interactions are truly meaningful or not.</p> <p>Results</p> <p>The results indicate that context-based relation inference achieved better precision than the previous ABC model approach. The literature analysis also shows that interactions inferred by the context-based approach are more meaningful than interactions by the previous ABC model.</p> <p>Conclusions</p> <p>We propose a novel interaction inference technique that incorporates context term vectors into the ABC model to discover meaningful hidden relationships. By utilizing multi-level context terms, our model shows better performance than the previous ABC model.</p

Creative Exploration Using Topic Based Bisociative Networks

Partial funding for Open Access provided by the UMD Libraries' Open Access Publishing Fund.Bisociative knowledge discovery is an approach that combines elements from two or more `incompatible' domains to generate creative solutions and insight. Inspired by Koestler's notion of bisociation, in this paper we propose a computational framework for the discovery of new connections between domains to promote creative discovery and inspiration in design. Specifically, we propose using topic models on a large collection of unstructured text ideas from multiple domains to discover creative sources of inspiration. We use these topics to generate a Bisociative Information Network - a graph that captures conceptual similarity between ideas - that helps designers find creative links within that network. Using a dataset of thousands of ideas from OpenIDEO, an online collaborative community, our results show usefulness of representing conceptual bridges through collections of words (topics) in finding cross-domain inspiration. We show that the discovered links between domains, whether presented on their own or via ideas they inspired, are perceived to be more novel and can also be used as creative stimuli for new idea generation

In Search of a Common Thread: Enhancing the LBD Workflow with a view to its Widespread Applicability

Literature-Based Discovery (LBD) research focuses on discovering implicit knowledge linkages in existing scientific literature to provide impetus to innovation and research productivity. Despite significant advancements in LBD research, previous studies contain several open problems and shortcomings that are hindering its progress. The overarching goal of this thesis is to address these issues, not only to enhance the discovery component of LBD, but also to shed light on new directions that can further strengthen the existing understanding of the LBD work ow. In accordance with this goal, the thesis aims to enhance the LBD work ow with a view to ensuring its widespread applicability. The goal of widespread applicability is twofold. Firstly, it relates to the adaptability of the proposed solutions to a diverse range of problem settings. These problem settings are not necessarily application areas that are closely related to the LBD context, but could include a wide range of problems beyond the typical scope of LBD, which has traditionally been applied to scientific literature. Adapting the LBD work ow to problems outside the typical scope of LBD is a worthwhile goal, since the intrinsic objective of LBD research, which is discovering novel linkages in text corpora is valid across a vast range of problem settings. Secondly, the idea of widespread applicability also denotes the capability of the proposed solutions to be executed in new environments. These `new environments' are various academic disciplines (i.e., cross-domain knowledge discovery) and publication languages (i.e., cross-lingual knowledge discovery). The application of LBD models to new environments is timely, since the massive growth of the scientific literature has engendered huge challenges to academics, irrespective of their domain. This thesis is divided into five main research objectives that address the following topics: literature synthesis, the input component, the discovery component, reusability, and portability. The objective of the literature synthesis is to address the gaps in existing LBD reviews by conducting the rst systematic literature review. The input component section aims to provide generalised insights on the suitability of various input types in the LBD work ow, focusing on their role and potential impact on the information retrieval cycle of LBD. The discovery component section aims to intermingle two research directions that have been under-investigated in the LBD literature, `modern word embedding techniques' and `temporal dimension' by proposing diachronic semantic inferences. Their potential positive in uence in knowledge discovery is veri ed through both direct and indirect uses. The reusability section aims to present a new, distinct viewpoint on these LBD models by verifying their reusability in a timely application area using a methodical reuse plan. The last section, portability, proposes an interdisciplinary LBD framework that can be applied to new environments. While highly cost-e cient and easily pluggable, this framework also gives rise to a new perspective on knowledge discovery through its generalisable capabilities. Succinctly, this thesis presents novel and distinct viewpoints to accomplish five main research objectives, enhancing the existing understanding of the LBD work ow. The thesis offers new insights which future LBD research could further explore and expand to create more eficient, widely applicable LBD models to enable broader community benefits.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202