Privacy-Preserving Gaussian Process Regression -- A Modular Approach to the Application of Homomorphic Encryption

Much of machine learning relies on the use of large amounts of data to train models to make predictions. When this data comes from multiple sources, for example when evaluation of data against a machine learning model is offered as a service, there can be privacy issues and legal concerns over the sharing of data. Fully homomorphic encryption (FHE) allows data to be computed on whilst encrypted, which can provide a solution to the problem of data privacy. However, FHE is both slow and restrictive, so existing algorithms must be manipulated to make them work efficiently under the FHE paradigm. Some commonly used machine learning algorithms, such as Gaussian process regression, are poorly suited to FHE and cannot be manipulated to work both efficiently and accurately. In this paper, we show that a modular approach, which applies FHE to only the sensitive steps of a workflow that need protection, allows one party to make predictions on their data using a Gaussian process regression model built from another party's data, without either party gaining access to the other's data, in a way which is both accurate and efficient. This construction is, to our knowledge, the first example of an effectively encrypted Gaussian process

Myeloid-derived Suppressor Cells in Acute Myeloid Leukaemia

PhDThe tumour microenvironment consists of an immunosuppressive niche created by the complex interactions between cancer cells and surrounding stromal cells. A critical component of this environment are myeloid-derived suppressor cells (MDSCs), a heterogeneous group of immature myeloid cells arrested at different stages of differentiation and expanded in response to a variety of tumour factors. MDSCs exert diverse effects in modulating the interactions between immune effector cells and malignant cells. An increased presence of MDSCs is associated with tumour progression, poorer outcomes, and decreased effectiveness of immunotherapeutic strategies. In this project, we sought to quantify and characterise MDSC populations in patients with Acute Myeloid Leukaemia (AML) and delineate the mechanisms underlying their expansion. We have demonstrated that immune suppressive MDSCs are expanded in the peripheral blood and bone marrow of patients with AML. Furthermore, AML cells secrete extra-cellular vesicles (EVs) that skew the tumour microenvironment from antigen-presentation to a tumour tolerogenic environment, through the expansion of MDSCs. We then demonstrated that MDSC expansion is dependent on tumour and EV expression of the oncoproteins MUC1 and c-Myc. Furthermore, we determined that MUC1 signalling promotes c-MYC expression in a microRNA (miRNA) dependent mechanism. This observation lead us to elucidate the critical role of MUC1 in suppressing microRNA-genesis in AML, via the down-regulation of the DICER protein, a key component of miRNA processing machinery. Finally, exploiting this critical pathway, we showed that MDSCs can be targeted by MUC1 inhibition or by the use of a novel hypomethylating agent SGI-110.British Society for Haematology Royal College of Physicians, UK Professor David Avigan and the department of Bone Marrow Transplantation

An optimized intermolecular force field for hydrogen bonded organic molecular crystals using atomic multipole electrostatics

This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by the International Union of Crystallography.We present a re-parameterization of the a popular intermolecular force field for describing intermolecular interactions in the organic solid state. Specifically, we optimize the performance of the exp-6 force field when used in conjunction with atomic multipole electrostatics. We also parameterize force fields that are optimized for use with multipoles derived from polarized molecular electron densities, to account for induction effects in molecular crystals. Parameterization is performed against a set of 186 experimentally determined, low temperature crystal structures and 53 measured sublimation enthalpies of hydrogen bonding organic molecules. The resulting force fields are tested on a validation set of 129 crystal structures and show improved reproduction of the structures and lattice energies of a range of organic molecular crystals compared to the original force field with atomic partial charge electrostatics. Unit cell dimensions of the validation set are typically reproduced to within 3% with the re-parameterized force fields. Lattice energies, which were all included during parameterisation, are systematically underestimated when compared to measured sublimation enthalpies, with mean absolute errors of between 7.4 and 9.0%.Engineering and Physical Sciences Research Council (Doctoral Training Account studentship