The aim for this project is to research, prototype and/or evaluate approaches to increase the explanatory effectiveness of the visualisations contained in analytics dashboards or similar support data-intensive tools. Explanatory visualisations are those whose main goal is the presentation and communication of insights. By contrast, exploratory visualisations are commonly targeted at experts in data analysis in search of insights from unfamiliar datasets.
Honours and Masters project
Displaying 181 - 190 of 250 honours projects.
Quality Data Management for Ethical AI: A Checklist Based Approach
Disruptive technologies such as artificial Intelligence (AI) systems can have unintended negative social and business consequences if not implemented with care. Specifically, faulty or biased AI applications may harm individuals, risk compliance and governance breaches, and damage to the corporate brand. An example for the potential harm inflicted on people is the case of Robert Williams who was arrested because of a biased insufficiently trained facial recognition system in the US in 2020 (See the New York Times link below).
The survey and evaluation of computational tools and models for automatic question generation
In this project, we aim at surveying relevant computational tools/models used for automatic question generation, and then comparing the effectiveness of these tools/models by using existing datasets.
Identification of monoamine receptors mediating amphetamine effects on body weight
Amphetamine (AMPH) is a widely abused drug, but before it was restricted in use it was an effective weight loss agent. We have shown that a distinct group of neurons controls a large part of the body’s weight loss response to amphetamine. In 2017 a single cell RNA sequencing project was published (Campbell, Macosko et al. 2017) that described transcriptional profiles of 21,000 neurons, amongst which are the neurons we have shown mediate the weight loss caused by amphetamine.
Structure & Function of Nucleic Acid Sensors
The evolutionary back and forth between hosts and mobile genetic elements drives the innovation of remarkable molecular strategies to sense or conceal foreign genetic material. The Knott Lab uses bioinformatics, biochemistry, and structural biology to understand how CRISPR-Cas and other novel immune systems specifically sense DNA or RNA. We aim to better understand the function of nucleic acid sensors to harness their activity as tools for molecular diagnostics or as innovative biomedicines.
Identification of novel targets for treatment of PDAC using a phosphoproteomics based approach.
Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive forms of cancer, with a high mortality rate. Therapies for PDAC are limited to chemotherapy, which has been ineffective to treat advanced stages of the cancer. Therefore, it is critical to identify biomarkers and develop targeted therapies, to improve early diagnosis and patient care. We have generated mass spectrometry (proteomics and phosphoproteomics) data from 15 PDAC patient-derived xenographs (PDX), which have been grown in mice. These PDX can be separated based on their response to chemotherapy.
Investigating epigenetic regulation of immune cells responding to viral infection.
Immune protection provided by immune memory underpins successful vaccines and is mediated mainly by memory lymphocytes and long-lived antibody- secreting cells. In particular, B cell memory is key to providing a rapid and robust response upon secondary infection and continual serum antibody protection. We are working to elucidate the crucial epigenetic mechanisms that generate and maintain B cell memory, and how B cells may retain molecular and functional plasticity under chronic pathogenic pressure.
Developing a computational tool for high-throughput analyses of single-cell microscopy data in antimicrobial pharmacology
Antimicrobial resistance poses significant medical challenge worldwide. Misuse, overuse or suboptimal dosing of antibiotics are major driving factors of antimicrobial resistance. Pharmacokinetic/pharmacodynamic (PK/PD) modelling is critical for designing optimal antimicrobial therapies to maximise the efficacy and minimise the emergence of resistance. However, conventional PK/PD modelling is generally based on viable counting on agar plates after overnight culture and employs a population approach.
Comparative genomic analysis of bacteriophages against Gram-negative superbugs
Antimicrobial resistance (AMR) has posed critical challenges to global health. The World Health Organization has identified carbapenem-resistant Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacterales as the top-priority pathogens urgently to be targeted for the development of novel therapeutic options. Recently, bacteriophage therapy has attracted extensive attention owing to its potential as novel antimicrobials to combat MDR pathogens.
Spatiotemporal dynamics of spontaneous activity in neural networks
Spontaneous synchronization is a common phenomenon occurring in diverse contexts, from a group of glowing fireflies at night or chirping crickets in a field to a network of coupled neurons in the brain. The study of synchronization helps to understand how uniform behaviors emerge in populations of heterogeneous neurons. At a macroscale level, the cortex operates in two classically-defined states: “synchronized” state which is characterized by strong low-frequency fluctuations and “desynchronized” state in which low-frequency fluctuations are suppressed.