In this thesis, we discuss solutions of phenotype description based on the microscopy image analysis to deal with biological problems both in 2D and 3D space. Our description of patterns goes... Show moreIn this thesis, we discuss solutions of phenotype description based on the microscopy image analysis to deal with biological problems both in 2D and 3D space. Our description of patterns goes beyond conventional features and helps to visualize the unseen in feature dataset. These solutions share several common processes which are based on similar principles. Furthermore, we notice that advanced features and classier strategies can help us improve the performance of the solutions. The biological problems that we have studied include the endocytosis routing using high-throughput screening in 2D and time and 3D geometrical representation from biological structures. Show less
We have studied shape with a particular focus on the zebrafish model system. The shape is an essential appearance of the phenotype of a biological specimen and it can be used to read out a... Show moreWe have studied shape with a particular focus on the zebrafish model system. The shape is an essential appearance of the phenotype of a biological specimen and it can be used to read out a current state or response or to study gene expression. So accurate shape analysis requires a precise shape description. Moreover, a sufficiently large sampling size of the specimens is necessary to ensure a justified and unbiased shape analysis. The latter is very important for high-throughput in compound screening. Therefore, top performance in zebrafish analysis requires high-throughput imaging (HTI). To deal with HTI, we aim to design an elaborate and well-performing HTI architecture. For the essential operations we need computational approaches to obtain the 2D/3D shape representations that are precise and yet can be acquired fast. The quality of the obtained shape descriptions will be validated in a straightforward manner with scalar primitives, i.e., the volume and surface area of a 3D shape. These primitives serve as 3D measurements for a robust primary shape assessment in the phenotype characterisation. Using only shape description is not sufficient, e.g., for high-resolution imaging on tissue and cellular level, so texture should be considered to complement and enhance the shape analysis. Show less
This dissertation aims to identify patterns in the managerial processes during an enterprise system implementation (ESI) period. An ESI remains a notorious challenge. In most cases, it concurrently... Show moreThis dissertation aims to identify patterns in the managerial processes during an enterprise system implementation (ESI) period. An ESI remains a notorious challenge. In most cases, it concurrently involves business strategy change, organizational change, and technical innovation. This dissertation adopts structuration theory as a __sensitizing device__ for understanding change forces behind an ESI. A pattern-recognition method is used to extract and describe patterns of managerial processes out of the structuration insights. Two sub-structuration processes are selected in order to make the structuration process observable and identifiable for the pattern recognition: Communicating significance by top management and facilitating learning for ESI. Ten in-depth empirical cases are studied in a longitudinal way. The main contribution of the thesis is four-fold: (1) the thesis designs a pattern-recognition method for conducting process studies (Structuration); (2) A set of process patterns are identified. These patterns clearly show the predictive power of the process studies; (3) A method to operationalize structuration theory. The __modalities__ embedded in the structuration framework are operationalized as a specific set of techniques within certain managerial processes which are observable and actionable; (4) New insights into the performance criteria of a strategic ESI project. A success of an ESI should not only be decided by operative criteria, but also should be judged by the achieved strategic impact. Show less
High Throughput (HT) methods are high volume experimental approaches that are common in the fields of the life-sciences. The instrumentation for these methods differs per application. We will focus... Show moreHigh Throughput (HT) methods are high volume experimental approaches that are common in the fields of the life-sciences. The instrumentation for these methods differs per application. We will focus on the HT methods that are concerned with imaging. The aim of this thesis is to find robust methods for object extraction and analysis. We focus on the Computer Science aspects of such analysis, namely pattern recognition. Pattern Recognition can be seen in the context of object recognition and data mining. Both aspects will be described in this thesis. We present a framework for segmenting and recognizing the objects of interest based on Template Matching. This approach was designed for an application in the HT screening of zebrafish embryos. All proposed methods are fully automated. We further elaborate on the segmentation algorithms to apply these in software that can be used in a HT context to derive measurements. Then we apply the software on a real life problem involving zebrafish infected with Mycobacterium marinum. Show less
This study proposes a framework for mining temporal patterns from Electronic Medical Records. A new scoring scheme based on the Wilson interval is provided to obtain frequent and predictive... Show moreThis study proposes a framework for mining temporal patterns from Electronic Medical Records. A new scoring scheme based on the Wilson interval is provided to obtain frequent and predictive patterns, as well as to accelerate the mining process by reducing the number of patterns mined. This is combined with a case study using data from general practices in the Netherlands to identify children at risk of suffering from mental disorders. To develop an accurate model, feature engineering methods such as one hot encoding and frequency transformation are proposed, and the pattern selection is tailored to this type of clinical data. Six machine learning models are trained on five age groups, with XGBoost achieving the highest AUC values (0.75-0.79) with sensitivity and specificity above 0.7 and 0.6 respectively. An improvement is demonstrated by the models learning from patterns in addition to non-temporal features. Show less
