Knowledge extraction from biomedical data using machine learning

PhD ThesisThanks to the breakthroughs in biotechnologies that have occurred during the recent years, biomedical data is accumulating at a previously unseen pace. In the field of biomedicine, decades-old statistical methods are still commonly used to analyse such data. However, the simplicity of these approaches often limits the amount of useful information that can be extracted from the data. Machine learning methods represent an important alternative due to their ability to capture complex patterns, within the data, likely missed by simpler methods. This thesis focuses on the extraction of useful knowledge from biomedical data using machine learning. Within the biomedical context, the vast majority of machine learning applications focus their e↵ort on the generation and validation of prediction models. Rarely the inferred models are used to discover meaningful biomedical knowledge. The work presented in this thesis goes beyond this scenario and devises new methodologies to mine machine learning models for the extraction of useful knowledge. The thesis targets two important and challenging biomedical analytic tasks: (1) the inference of biological networks and (2) the discovery of biomarkers. The first task aims to identify associations between di↵erent biological entities, while the second one tries to discover sets of variables that are relevant for specific biomedical conditions. Successful solutions for both problems rely on the ability to recognise complex interactions within the data, hence the use of multivariate machine learning methods. The network inference problem is addressed with FuNeL: a protocol to generate networks based on the analysis of rule-based machine learning models. The second task, the biomarker discovery, is studied with RGIFE, a heuristic that exploits the information extracted from machine learning models to guide its search for minimal subsets of variables. The extensive analysis conducted for this dissertation shows that the networks inferred with FuNeL capture relevant knowledge complementary to that extracted by standard inference methods. Furthermore, the associations defined by FuNeL are discovered - 6 - more pertinent in a disease context. The biomarkers selected by RGIFE are found to be disease-relevant and to have a high predictive power. When applied to osteoarthritis data, RGIFE confirmed the importance of previously identified biomarkers, whilst also extracting novel biomarkers with possible future clinical applications. Overall, the thesis shows new e↵ective methods to leverage the information, often remaining buried, encapsulated within machine learning models and discover useful biomedical knowledge.European Union Seventh Framework Programme (FP7/2007- 2013) that funded part of this work under the “D-BOARD” project (grant agreement number 305815)

20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017

The proceedings contain 57 papers. The special focus in this conference is on Applications of Evolutionary Computation. The topics include: Minimization of systemic risk for directed network using genetic algorithm; pricing rainfall based futures using genetic programming; dynamic portfolio optimization in ultra-high frequency environment; integration of reaction kinetics theory and gene expression programming to infer reaction mechanism; improving the reproducibility of genetic association results using genotype resampling methods; characterising the influence of rule-based knowledge representations in biological knowledge extraction from transcriptomics data; application to blood glucose forecasting; genetic programming representations for multi dimensional feature learning in biomedical classification; meta-heuristically seeded genetic algorithm for independent job scheduling in grid computing; analysis of average communicability in complex networks; configuring dynamic heterogeneous wireless communications networks using a customised genetic algorithm; multi-objective evolutionary algorithms for influence maximization in social networks; Lamarckian and lifelong memetic search in agent-based computing; two-phase strategy managing insensitivity in global optimization; avenues for the use of cellular automata in image segmentation; localization on hubs and delocalized diffusion; hybrid multi-ensemble scheduling; driving in TORCS using modular fuzzy controllers; automated game balancing in ms pacman and starcraft using evolutionary algorithms; evolving game specific UCB alternatives for general video game playing; analysis of vanilla rolling horizon evolution parameters in general video game playing and evolutionary art using the fly algorithm