Deep Learning for Inference from Biological Data

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With the rapid increase in our ability to make various measurements about biology at the genomic, epi-genetic, gene expression and population levels, there is widespread acknowledgement within the biological community that mathematical and computational modelling is a necessary tool to be able to understand how molecular interaction and genetically encoded information translates into biological function, and how to translate such understanding to help in the treatment of complex diseases. Of particular interest is the measurement of gene expression at the different levels of transcriptome and proteome, how this mapping is regulated and what kind of information encoded in the DNA sequence are determinants of it. We have been building a strong collaboration between ECS and the School of Medicine in this domain and so far have: (i) an outlier detection algorithm (published in Bioinformatics) for detection of post-translationally regulated proteins, which later formed the subject of a successful KTP bid; (ii) a publication that builds on the above (in the Journal of Immunology) looking at new data that is mapped to micro RNAs regulating protein levels; (iii) A manuscript on the subject of translation regulation (submission to Nucleic Acids Research imminent) and (iv) a grant submission this round to BBSRC (currently under review). This project will be in this rich domain, focusing on two areas we have hitherto not addressed: (i) the role of splicing and its regulation; and (ii) extracting information from the sequence level with focus on regulatory elements in the non-coding regions of the genome. We expect a combination of measured and sequence-derived features to be beneficial in making accurate inferences and will be developing advanced representations learned from deep learning techniques to achieve this.