Visual Data Mining: Effective Exploration of the Biological Universe

Role of Knowledge Management in Development and Lifecycle Management of Biopharmaceuticals

Knowledge Management (KM) is a key enabler for achieving quality in a lifecycle approach for production of biopharmaceuticals. Due to the important role that it plays towards successful implementation of Quality by Design (QbD), an analysis of KM solutions is needed. This work provides a comprehensive review of the interface between KM and QbD-driven biopharmaceutical production systems as perceived by academic as well as industrial viewpoints. A comprehensive set of 356 publications addressing the applications of KM tools to QbD-related tasks were screened and a query to gather industrial inputs from 17 major biopharmaceutical organizations was performed. Three KM tool classes were identified as having high relevance for biopharmaceutical production systems and have been further explored: knowledge indicators, ontologies, and process modeling. A proposed categorization of 16 distinct KM tool classes allowed for the identification of holistic technologies supporting QbD. In addition, the classification allowed for addressing the disparity between industrial and academic expectations regarding the application of KM methodologies. This is a first of a kind attempt and thus we think that this paper would be of considerable interest to those in academia and industry that are engaged in accelerating development and commercialization of biopharmaceuticals.

Visual analytics for concept exploration in subspaces of patient groups : Making sense of complex datasets with the Doctor-in-the-loop

Medical doctors and researchers in bio-medicine are increasingly confronted with complex patient data, posing new and difficult analysis challenges. These data are often comprising high-dimensional descriptions of patient conditions and measurements on the success of certain therapies. An important analysis question in such data is to compare and correlate patient conditions and therapy results along with combinations of dimensions. As the number of dimensions is often very large, one needs to map them to a smaller number of relevant dimensions to be more amenable for expert analysis. This is because irrelevant, redundant, and conflicting dimensions can negatively affect effectiveness and efficiency of the analytic process (the so-called curse of dimensionality). However, the possible mappings from high- to low-dimensional spaces are ambiguous. For example, the similarity between patients may change by considering different combinations of relevant dimensions (subspaces). We demonstrate the potential of subspace analysis for the interpretation of high-dimensional medical data. Specifically, we present SubVIS, an interactive tool to visually explore subspace clusters from different perspectives, introduce a novel analysis workflow, and discuss future directions for high-dimensional (medical) data analysis and its visual exploration. We apply the presented workflow to a real-world dataset from the medical domain and show its usefulness with a domain expert evaluation.