Quantifying tissue volumes in pediatric brains from magnetic resonance (MR) images can provide insight into etiology and onset of neurological disease. Unbiased volumetric analysis can be applied to large population studies when automated image processing is possible. Standard segmentation strategies using adult atlases fail to account for varying tissue contrasts and types associated with the rapid growth and maturational changes seen in early neurodevelopment. The goal of this project was to develop an automated pipeline and two age-specific atlases capable of providing accurate tissue classification despite these challenges. The automated pipeline consisted of a stepwise initial atlas-to-subject registration, expectation maximization (EM) atlas based segmentation, and a post-processing level set segmentation for improved white/gray matter separation. This level set segmentation is a 3D and multiphase adaptation of a 2D method intended for use on images with the types of intensity Inhomogeneities found in MR images. The initial tissue maps required to determine spatial priors for the one-year-old atlas were created by manually cleaning the results of an adult atlas and the automated pipeline. Additional tissue maps were incrementally added until the spatial priors were sufficiently representative. The neonate atlas was similarly created, starting with the one-year-old atlas.

Evidence-based software engineering (EBSE) solutions for drug discovery that are effective, affordable, and accessible all-in-one are lacking. This thesis chronicles the progression and accomplishments of the AiDA (Artificially-intelligent Desktop Assistant) functional artificial intelligence (AI) project for the purposes of drug discovery in the challenging acute myeloid leukemia context (AML). AiDA is a highly automated combined natural language processing (NLP) and spreadsheet feature extraction solution that harbours potential to disrupt the state of current research investigation methods using big data and aggregated literature. The completed work includes a text-to-function (T2F) NLP method for automated text interpretation, a ranked-list algorithm for multi-dataset analysis, and a custom multi-purpose neural network engine presented to the user using an open-source graphics engine. Validation of the deep learning engine using MNIST and CIFAR machine learning benchmark datasets showed performance comparable to state-of-the-art libraries using similar architectures. An n-dimensional word embedding method for the handling of unstructured natural language data was devised to feed convolutional neural network (CNN) models that over 25 random permutations correctly predicted functional responses to up to 86.64% of over 300 validation transcripts. The same CNN NLP infrastructure was then used to automate biomedical context recognition in >20000 literature abstracts with up to 95.7% test accuracy over several permutations. The AiDA platform was used to compile a bidirectional ranked list of potential gene targets for pharmaceuticals by extracting features from leukemia microarray data, followed by mining of the PubMed biomedical citation database to extract recyclable pharmaceutical candidates. Downstream analysis of the candidate therapeutic targets revealed enrichments in AML- and leukemic stem cell (LSC)-related pathways. The applicability of the AiDA algorithms in whole and part to the larger biomedical research field is explored. / Thesis / Master of Science (MSc) / Lead generation is an integral requirement of any research organization in all fields and is typically a time-consuming and therefore expensive task. This is due to the requirement of human intuition to be applied iteratively over a large body of evidence. In this thesis, a new technology called the Artificially-intelligent Desktop Assistant (AiDA) is explored in order to provide a large number of leads from accumulated biomedical information. AiDA was created using a combination of classical statistics, deep learning methods, and modern graphical interface engineering. It aims to simplify the interface between the researcher and an assortment of bioinformatics tasks by organically interpreting written text messages and responding with the appropriate task. AiDA was able to identify several potential targets for new pharmaceuticals in acute myeloid leukemia (AML), a cancer of the blood, by reading whole-genome data. It then discovered appropriate therapeutics by automatically scanning through the accumulated body of biomedical research papers. Analysis of the discovered drug targets shows that together, they are involved in key biological processes that are known by the scientific community to be involved in leukemia and other cancers.