Browsing by Subject "neural networks"

Personalized medicine tailors therapies for the patient based on predicted risk factors. Some tools used for making predictions on the safety and efficacy of drugs are genetics and metabolomics. This thesis focuses on identifying biomarkers for the activity level of the drug transporter organic anion transporting polypep-tide 1B1 (OATP1B1) from data acquired from untargeted metabolite profiling. OATP1B1 transports various drugs, such as statins, from portal blood into the hepatocytes. OATP1B1 is a genetically polymorphic influx transporter, which is expressed in human hepatocytes. Statins are low-density lipoprotein cholesterol-lowering drugs, and decreased or poor OATP1B1 function has been shown to be associated with statin-induced myopathy. Based on genetic variability, individuals can be classified to those with normal, decreased or poor OATP1B1 function. These activity classes were employed to identify metabolomic biomarkers for OATP1B1. To find the most efficient way to predict the activity level and find the biomarkers that associate with the activity level, 5 different machine learning models were tested with a dataset that consisted of 356 fasting blood samples with 9152 metabolite features. The models included both a Random Forest regressor and a classifier, Gradient Boosted Decision Tree regressor and classifier, and a Deep Neural Network regressor. Hindrances specific for this type of data was the collinearity between the features and the large amount of features compared to the number of samples, which lead to issues in determining the important features of the neural network model. To adjust to this, the data was clustered according to their Spearman’s rank-order correlation ranks. Feature importances were calculated using two methods. In the case of neural network, the feature importances were calculated with permutation feature importance using mean squared error, and random forest and gradient boosted decision trees used gini impurity. The performance of each model was measured, and all classifiers had a poor ability to predict decreasead and poor function classes. All regressors performed very similarly to each other. Gradient boosted decision tree regressor performed the best by a slight margin, but random forest regressor and neural network regressor performed nearly as well. The best features from all three models were cross-referenced with the features found from y-aware PCA analysis. The y-aware PCA analysis indicated that 14 best features cover 95% of the explained variance, so 14 features were picked from each model and cross-referenced with each other. Cross-referencing highest scoring features reported by the best models found multiple features that showed up as important in many models.Taken together, machine learning methods provide powerful tools to identify potential biomarkers from untargeted metabolomics data.

Discourse dynamics is one of the important fields in digital humanities research. Over time, the perspectives and concerns of society on particular topics or events might change. Based on the changing in popularity of a certain theme different patterns are formed, increasing or decreasing the prominence of the theme in news. Tracking these changes is a challenging task. In a large text collection discourse themes are intertwined and uncategorized, which makes it hard to analyse them manually. The thesis tackles a novel task of automatic extraction of discourse trends from large text corpora. The main motivation for this work lies in the need in digital humanities to track discourse dynamics in diachronic corpora. Machine learning is a potential method to automate this task by learning patterns from the data. However, in many real use-cases ground truth is not available and annotating discourses on a corpus-level is incredibly difficult and time-consuming. This study proposes a novel procedure to generate synthetic datasets for this task, a quantitative evaluation method and a set of benchmarking models. Large-scale experiments are run using these synthetic datasets. The thesis demonstrates that a neural network model trained on such datasets can obtain meaningful results when applied to a real dataset, without any adjustments of the model.

Independent Component Analysis (ICA) aims to separate the observed signals into their underlying independent components responsible for generating the observations. Most research in ICA has focused on continuous signals, while the methodology for binary and discrete signals is less developed. Yet, binary observations are equally present in various fields and applications, such as causal discovery, signal processing, and bioinformatics. In the last decade, Boolean OR and XOR mixtures have been shown to be identifiable by ICA, but such models suffer from limited expressivity, calling for new methods to solve the problem. In this thesis, "Independent Component Analysis for Binary Data", we estimate the mixing matrix of ICA from binary observations and an additionally observed auxiliary variable by employing a linear model inspired by the Identifiable Variational Autoencoder (iVAE), which exploits the non-stationarity of the data. The model is optimized with a gradient-based algorithm that uses second-order optimization with limited memory, resulting in a training time in the order of seconds for the particular study cases. We investigate which conditions can lead to the reconstruction of the mixing matrix, concluding that the method is able to identify the mixing matrix when the number of observed variables is greater than the number of sources. In such cases, the linear binary iVAE can reconstruct the mixing matrix up to order and scale indeterminacies, which are considered in the evaluation with the Mean Cosine Similarity Score. Furthermore, the model can reconstruct the mixing matrix even under a limited sample size. Therefore, this work demonstrates the potential for applications in real-world data and also offers a possibility to study and formalize identifiability in future work. In summary, the most important contributions of this thesis are the empirical study of the conditions that enable the mixing matrix reconstruction using the binary iVAE, and the empirical results on the performance and efficiency of the model. The latter was achieved through a new combination of existing methods, including modifications and simplifications of a linear binary iVAE model and the optimization of such a model under limited computational resources.

The Finnish Environment Institute (SYKE) has at least two missions which require surveying large land areas: finding invasive alien species and monitoring the state of Finnish lakes. Various methods to accomplish these tasks exist, but they traditionally rely on manual labor by experts or citizen activism, and as such do not scale well. This thesis explores the usage of computer vision to dramatically improve the scaling of these tasks. Specifically, the aim is to fly a drone over selected areas and use a convolutional neural network architecture (U-net) to create segmentations of the images. The method performs well on select biomass estimation task classes due to large enough datasets and easy-to-distinguish core features of the classes. Furthermore, a qualitative study of datasets was performed, yielding an estimate for a lower bound of number of examples for an useful dataset. ACM Computing Classification System (CCS): CCS → Computing methodologies → Machine learning → Machine learning approaches → Neural networks