This is a list of research projects for the research group.
- Deep Probabilistic This is an EPSRC grant that is a collaboration with Zoubin Ghaharamani, John Quinn and CitizenMe with additional support from Amazon and Facebook.
- CONTESSA COuNt data TimE SerieS Analysis - significance tests and sequencing data application.
- OpenDreamKit This is an EU H2020 Grant with a cross Europe Collaboration.
- PIMS: Personal Information Management Systems. This is a Innovate UK grant in collaboration with CitizenMe.
- RADIANT: Rapid Development and Distribution of Statistical Tools for High-Throughput Sequencing Data In this grant we are developing Gaussian process approaches for handling high throughput sequencing data with a focus on combining this data with other data modalities so that sequence information can be related to phenotype.
- MLPM: Machine Learning for Personalized Medicine In this grant we are concerned with the challenges of massively missing data in phenotype and relating genotype to phenotype.
- Linking recombinant gene sequence to protein product manufacturability using CHO cell genomic resources One post-doctoral research assistant. In this grant we are using Bayesian optimization techniques to predict improvements in protein production efficiency given sequence information from Chinese Hamster Ovary Cells.
- WYSIWYD: What You Say is What You Did One post-doctoral research assistant. In this grant we are developing approaches for deep Gaussian process models and efficient approaches for fitting them to large data (including streaming data).
- BIOPREDYN: New Bioinformatics Methods and Tools for Data-Driven, Predictive Dynamic Modelling in Biotechnological Applications One post-doctoral research assistant. In this grant we are focused on introducing mechanistic aspects to machine learning models in application to dynamical biological systems.
- SYNERGY: Systems approach to gene regulation biology through nuclear receptors
- ITERATIVE: An iterative pipeline of computational modelling and experimental design for uncovering gene regulatory networks in vertebrates
- Latent Force Models: Mechanistically Inspired Convolution Processes for Learning
- TIGRE: Gaussian Process Models for Systems Identification with Applications in Systems Biology (PI)
- PUMA: Improved processing of microarray data with probabilistic models (PI)
- Dimensional Reduction and Style IK (PI)
- Advanced Lifestyle Monitoring Systems (co-I)
- Learning Classifiers from Sloppily Labelled Data (PI)