Baker Institute and School of Computing and Information Systems
Research interests: computational biology, particularly, the integrative study of proteomics, protein structures, and their functions, virtual screening.
Research Interests: Automated and semi-automated learning
Research interests: Protein-protein interactions, Machine learning, Deep learning, Web development
Research interests: drug resistance in infectious and non-infectious diseases
Project: Using graph-based signatures to guide antibody engineering and epitope identification.
Project: Using machine learning to improve our understanding, and personalizing treatment of cancer.
Project: Exploring cardiotoxicity risk factors.
Project: Machine Learning models applied to Vaccine Design and Immunotherapies
Project: Predicting non-coding RNA disease associations
Project: Computational guided GPCR engineering and drug development.
Project: Developing structure-based deep learning methods to predict mutation effects on proteins
Project: Accurate characterisation of the functional effects of protein coding mutations
Project: Investigation of the interaction between multiple mutations on protein stability and function.
Project: investigation into constraints in penicillin-binding protein mutations in group A and C/G Streptococcus and predicting resistance associated mutations using genomic surveillance data
Project: Predicting and optimising small-molecule pharmacokinetic properties using deep learning
Project: Application of statistical and machine learning analysis of ICU data to improve patient outcomes
Research interests: Application of machine learning to clinical data. Image based machine learning.
Project: Integrating structural and epidemiological modelling to identify which Tuberculosis resistance mutations are likely to arise in a population.
Research interests: Statistical analysis of 'omic data
Project: Using population genetic diversity to characterise the mutational tolerance of a given gene, to identify pathogenic variants and structurally and functionally important regions of the protein.
Marialena did her Masters of Bioinformatics project with us in 2017-2018, looking at the structural characterisation of Mycobacterium tuberculosis streptomycin resistance variants in gidB.
Project: Detecting Abnormal Heart Sounds using Image Representations
Mi-Chi did her Masters of IT with us in 2019, combining machine learning with phenotypic screening to improve drug development.
Project: Predicting activity of small molecules against neglected tropical diseases
Project: Building small molecule toxicity predictors
Project: Mapping the role of PTM's in genetic diseases
Project: Predicting druggable targets
Project: Predicting the pathogenicity and patient outcomes of BRCA1/2 mutations.
Project: Structure guided prediction of VHL disease outcomes
Project: Using machine and deep learning to process medical imaging data. Building new tools for improved automated analysis and diagnosis from medical images.
Structural characterisation of ALS disease mutations to predict disease progression.
Project: Identification and characterisation of proteins under different selective pressures between ethnic populations.
Amanda did her undergraduate and Masters of Bioinformatics with us 2016-2018 looking at the structural characterisation of ALS disease mutations to predict disease progression. She is now pursuing her PhD at the University of Ostrava, Czech Republic.
Aaron did his undergraduate project with us in 2018 looking at mapping population variation in malaria. He is now studying medicine at Melbourne University.
Vittoria joined us in 2019 to work on the structural analysis of genetic disease causing variants. She is now completing her PhD research programme at the University of Siena.
Anna joined us in 2019 to work on an automatic modelling pipeline. She is now completing her PhD research programme at the University of Siena.
Anjalia completed her summer internship in the group in 2019. She developed a method to predict antimicrobial peptide activity. She is now looking forward to starting her PhD in the US.
Hardik worked with the group in 2019 to develop a method to map the drugability of protein surfaces.
Project: Predicting drug resistant mutations in Abl