An exploration of methodologies to improve semi-supervised hierarchical clustering with knowledge-based constraints

Clustering algorithms with constraints (also known as semi-supervised clustering algorithms) have been introduced to the field of machine learning as a significant variant to the conventional unsupervised clustering learning algorithms. They have been demonstrated to achieve better performance due to integrating prior knowledge during the clustering process, that enables uncovering relevant useful information from the data being clustered. However, the research conducted within the context of developing semi-supervised hierarchical clustering techniques are still an open and active investigation area. Majority of current semi-supervised clustering algorithms are developed as partitional clustering (PC) methods and only few research efforts have been made on developing semi-supervised hierarchical clustering methods. The aim of this research is to enhance hierarchical clustering (HC) algorithms based on prior knowledge, by adopting novel methodologies. [Continues.

Molecular Bronchiolitis Obliterans Syndrome Risk Monitoring: A Systems-Based Approach

The combination of high throughput omics (i.e. genomics or proteomics) and machine learning offers new possibilities for clinical diagnostics and the detection of biomarkers. One disease for which no reliable prognostic marker has been found yet is bronchiolitis obliterans (BO), a clinical manifestation of chronic rejection after lung transplantation. BO is the major limiting factor for long-term survival after lung transplantation, and manifests as a chronic bronchiolar inammation accompanied by progressive sub-mucosal fibrosis leading to gradual obliteration of the bronchiolar lumen. The resulting reduction in forced expiratory volume per second (FEV 1 ) is defined as the bronchiolitis obliterans syndrome (BOS). As chronic lung transplant failure occurs more frequently than in other organ transplants, molecular markers for early BO and BOS detection are urgently required to adapt the patients immunosuppressive regimen when airway damage is minimal. To achieve this goal, gene expression in bronchial epithelial cells (microarray anaylsis) and on the proteome level in bronchoalveolar lavage fluid (BALF)(mass spectrometry profiling) were monitored. Analysis of the obtained data sets was performed using novel and established methods from the fields of machine learning and statistics. This thesis also introduces a novel clustering algorithm. In the analysis of gene expression microarrays one problem is the unsupervised discovery of stable and biologically relevant patient subgroups. To this end I developed a novel clustering algorithm. This algorithm focuses on the discovery of a set of patient clusters defined by the consistent up- and down-regulation of a subset of genes. Assessment of cluster stability is done using a bootstrap resampling scheme. This makes it possible to rank the genes in accordance with their clusterwise importance. The algorithm was applied to a publicly available B-cell lymphoma microarray data set and compared to other commonly used clustering algorithms

Evaluation of clustering results and novel cluster algorithms

Cluster analysis is frequently performed in many application fields to find groups in data. For example, in medicine, researchers have used gene expression data to cluster patients suffering from a particular disease (e.g., breast cancer), in order to detect new disease subtypes. Many cluster algorithms and methods for cluster validation, i.e., methods for evaluating the quality of cluster analysis results, have been proposed in the literature. However, open questions about the evaluation of both clustering results and novel cluster algorithms remain. It has rarely been discussed whether a) interesting clustering results or b) promising performance evaluations of newly presented cluster algorithms might be over-optimistic, in the sense that these good results cannot be replicated on new data or in other settings. Such questions are relevant in light of the so-called "replication crisis"; in various research disciplines such as medicine, biology, psychology, and economics, many results have turned out to be non-replicable, casting doubt on the trustworthiness and reliability of scientific findings. This crisis has led to increasing popularity of "metascience". Metascientific studies analyze problems that have contributed to the replication crisis (e.g., questionable research practices), and propose and evaluate possible solutions. So far, metascientific studies have mainly focused on issues related to significance testing. In contrast, this dissertation addresses the reliability of a) clustering results in applied research and b) results concerning newly presented cluster algorithms in the methodological literature. Different aspects of this topic are discussed in three Contributions. The first Contribution presents a framework for validating clustering results on validation data. Using validation data is vital to examine the replicability and generalizability of results. While applied researchers sometimes use validation data to check their clustering results, our article is the first to review the different approaches in the literature and to structure them in a systematic manner. We demonstrate that many classical cluster validation techniques, such as internal and external validation, can be combined with validation data. Our framework provides guidance to applied researchers who wish to evaluate their own clustering results or the results of other teams on new data. The second Contribution applies the framework from Contribution 1 to quantify over-optimistic bias in the context of a specific application field, namely unsupervised microbiome research. We analyze over-optimism effects which result from the multiplicity of analysis strategies for cluster analysis and network learning. The plethora of possible analysis strategies poses a challenge for researchers who are often uncertain about which method to use. Researchers might be tempted to try different methods on their dataset and look for the method yielding the "best" result. If only the "best" result is selectively reported, this may cause "overfitting" of the method to the dataset and the result might not be replicable on validation data. We quantify such over-optimism effects for four illustrative types of unsupervised research tasks (clustering of bacterial genera, hub detection in microbial association networks, differential network analysis, and clustering of samples). Contributions 1 and 2 consider the evaluation of clustering results and thus adopt a metascientific perspective on applied research. In contrast, the third Contribution is a metascientific study about methodological research on the development of new cluster algorithms. This Contribution analyzes the over-optimistic evaluation and reporting of novel cluster algorithms. As an illustrative example, we consider the recently proposed cluster algorithm "Rock"; initially deemed promising, it later turned out to be not generally better than its competitors. We demonstrate how Rock can nevertheless appear to outperform competitors via optimization of the evaluation design, namely the used data types, data characteristics, the algorithm’s parameters, and the choice of competing algorithms. The study is a cautionary tale that illustrates how easy it can be for researchers to claim apparent "superiority" of a new cluster algorithm. This, in turn, stresses the importance of strategies for avoiding the problems of over-optimism, such as neutral benchmark studies

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

SIS 2017. Statistics and Data Science: new challenges, new generations

The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data