Graph Representation Learning for Biomedical Entities

Graph representation learning has rapidly become one of the main drivers in machine learning research over recent years. The power of graph representation lies in its ability to naturally represent entities and their interactions. Incorporating in machine learning models the inductive bias contained in the graph-structured representation of the data, has led to significant advances in many areas of research. Biomedical research has particularly benefited of graph representation learning methodologies, since networks can be used to represent a multitude of biological entities and their associations. These applications and methodologies aim to find appropriate representations of biological networks for different tasks. However, it is still unclear how to effectively combine information originated from different networks. Drug development, analysis of disease progression, therapeutic response design and several more tasks require considering the complex relationships within and among several biological networks. Traditional network representations are insufficient for dealing with the heterogeneity of objects and relationships, so a richer framework is needed to handle objects and relationships of different scales. This proposal aims to develop new graph representation learning methods that can integrate and explain complex, multiplex, and multilayer networks where nodes and edges have different meanings.

Who might benefit from this research? How might they benefit from this research?

Students
(a) The major beneficiaries of the CDT will, of course, be the students that train on the program. They will be equipped with a set of skills that will be highly desirable in the organic molecule making industries. Although the proposal is directing towards a need in the pharmaceutical industry, the training and research skills are totally transferable to industries like the argochemical sector (this is an almost seamless transition as the nature of the needs are near identical to that of pharma) but also the fine chemicals industries, CRO's who serve all of these industries. With some adaptation of the skills accrued then the students will also be able to apply their knowledge to problems in the materials industries, like polymers, organic electronics and chemical biology.

(b) Synthesis will also be evolving in academia and students equipped with skills in digital molecular technologies will be at a significant advantage in being apply to implement the skills acquired while training on the CDT. These students could be the rising stars of academia in 10 years time.

(c) The non-research based training will benefit the students by providing a set of transferable skills that will see them thrive in any chosen career.

(d) The industry contacts that will be generated from the variety of interactions planned in the CDT will give students both experience and insight into the machinations of the industrial sector, helping them to gain a different training experience (form industry taught courses) and hands on experience in industrial laboratories.

(e) All student in UCAM will be able to benefit in some way form the CDT. Training courses will not be restricted to CDT students (only courses that require payment will be CDT only, and even then, we will endeavour to make additional places available for non-CDT students). The overall standard of training for all students wil be raised by a CDT, meaning that benefit will be realised across the students of the associated departments. In additional, non CDT students can also be inspired by the research of the CDT and can immerse new techniques into their own groups.

Academic researchers in related fields (PIs)
(a) new research knowledge that results from this program will benefit PIs in UCAM and across the academic community. All research will be pre-competitive, with any commercial interests managed by Cambridge Enterprise

(b) a change in mnidset of how synthetic research is carried out

(c) new collaborations will be generated withing UCAM, but also externally on a national and international level.

(d) better, more closely aligned, interactions with industry as a result of knowledge transfer

(e) access to outstanding students

Broader public
(a) in principle, more potential medicines could be made available by the research of this CDT.

Economy
(a) a new highly skilled workforce literate in disciplines essential to industry needs will be available
(b) higher productivity in industry, faster access to new medicines
(c) spin out opportunities will create jobs and will stimulate the economy
(d) automation will not remove the need for skilled people, it will allow the researchers to think of solutions to the problems we dont yet understand leading to us being able to discover solutions faster