UNNT: A novel Utility for comparing Neural Net and Tree-based models.

The use of deep learning (DL) is steadily gaining traction in scientific challenges such as cancer research. Advances in enhanced data generation, machine learning algorithms, and compute infrastructure have led to an acceleration in the use of deep learning in various domains of cancer research such as drug response problems. In our study, we explored tree-based models to improve the accuracy of a single drug response model and demonstrate that tree-based models such as XGBoost (eXtreme Gradient Boosting) have advantages over deep learning models, such as a convolutional neural network (CNN), for single drug response problems.

cTULIP: application of a human-based RNA-seq primary tumor classification tool for cross-species primary tumor classification in canine.

Introduction: The domestic dog, , is quickly gaining traction as an advantageous model for use in the study of cancer, one of the leading causes of death worldwide. Naturally occurring canine cancers share clinical, histological, and molecular characteristics with the corresponding human diseases.

Methods: In this study, we take a deep-learning approach to test how similar the gene expression profile of canine glioma and bladder cancer (BLCA) tumors are to the corresponding human tumors.

Constructed wetlands and duckweed ponds as a treatment step in liquid manure handling - A life cycle assessment.

Life cycle assessment (LCA) was applied to evaluate duckweed ponds and constructed wetlands as polishing steps in pig manure liquid fraction treatment. Using nitrification-denitrification (NDN) of the liquid fraction as the starting point, the LCA compared direct land application of the NDN effluent with different combinations of duckweed ponds, constructed wetlands and discharge into natural waterbodies. Duckweed ponds and constructed wetlands are viewed as a viable tertiary treatment option and potential remedy for nutrient imbalances in areas of intense livestock farming, such as in Belgium.

TULIP: An RNA-seq-based Primary Tumor Type Prediction Tool Using Convolutional Neural Networks.

Background: With cancer as one of the leading causes of death worldwide, accurate primary tumor type prediction is critical in identifying genetic factors that can inhibit or slow tumor progression. There have been efforts to categorize primary tumor types with gene expression data using machine learning, and more recently with deep learning, in the last several years.

Methods: In this paper, we developed four 1-dimensional (1D) Convolutional Neural Network (CNN) models to classify RNA-seq count data as one of 17 highly represented primary tumor types or 32 primary tumor types regardless of imbalanced representation.

Life cycle assessment of struvite recovery and wastewater sludge end-use: A Flemish illustration.

Phosphate rock (PR) has been designated as a Critical Raw Material in the European Union (EU). This has led to increased emphasis on alternative P recovery (APR) from secondary streams like wastewater sludge (WWS). However, WWS end-use is a contentious topic, and EU member states prefer different end-use pathways (land application/incineration/valorisation in cement kilns).