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Title
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Novel applications of machine learning in biological sciences
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Author
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Abstract
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| In recent years, the rapid increase in available data, accompanied by significant algorithmic advancements, have enhanced our ability to analyze and extract valuable insights from this data. In this thesis, we present novel applications of machine learning (ML) in biological sciences, more particular in neuroscience, ecology, and agricultural research. We pay special attention to the interpretability and explainability of our models. In Chapter 2, we address a binary classification task aimed at detecting attention in electroencephalography (EEG) data. By employing state-of-the-art ML models, we successfully differentiate between target and distractor stimuli using EEG data collected during an audiovisual attention task. Additionally, we examine subject dependence in EEG data by comparing the performance of ML models trained on individual subjects versus multiple subjects. Finally, we apply explainable AI (xAI) techniques to identify the features utilized by the models for their predictions, and find that these features align with the expectations of domain experts. Chapter 3 focuses on the relationship between soil temperature, various meteorological variables, and vegetation phenology characteristics. Using ML and xAI, we find that rising soil temperatures result in an earlier onset of the growing season for plants. Additionally, our analysis reveals that the meteorological variables have the most significant impact on the vegetation phenology characteristics, while annual variations are primarily driven by changes in the soil temperature. In Chapter 4, we address the issue of missing values in sensor data, with a focus on large-scale wireless sensor networks. We evaluate twelve missing value imputations methods, each using different imputation strategies and originating from diverse backgrounds. To enhance the evaluation process, we define a “masked missings” scenario, offering a more realistic assessment compared to the standard practice of using random missings. Our findings indicate that imputation methods explicitly accounting for spatial correlations between sensors generally perform best, and we use these insights to suggest directions for future research. Finally, in Chapter 5, we develop a new global dataset considering the historical application of fertilizers. Specifically, we use ML to predict historical fertilizer application at both the crop and country levels, based on a set of features related to crop classes, along with socioeconomic, agrological, and environmental variables. Additionally, we use xAI to identify the most relevant drivers influencing fertilizer application. |
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Language
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English
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Publication
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Antwerpen
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University of Antwerp, Faculty of Science
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2024
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DOI
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10.63028/10067/2079230151162165141
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Volume/pages
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xxvi, 162 p.
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Note
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Latré, Steven [Supervisor]
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Verdonck, Tim [Supervisor]
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Full text (open access)
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