Search Results (2)
Explainable machine learning techniques for applications in life sciences
As ecological, agricultural, and biological disciplines face mounting challenges like biodiversity loss, food chain disruption, and climate change, leveraging machine learning (ML) to process complex and heterogeneous data becomes increasingly vital. This dissertation explores the potential of ML in combination with explainability approaches for enhancing research in life sciences, specifically …
Diversity, ecology and sensing of the toxic diatom Pseudo-Nitzschia in the Gulf of Trieste: doctoral disertation
This work focuses on a group of diatoms, belonging to the genus Pseudo-nitzschia, which is a globally important phytoplankton genus. Some species of the genus produce the toxin domoic acid, which is responsible for shellfish intoxication known as the amnesic shellfish poisoning (ASP). These diatoms have few morphological features that …