Deep Metric Learning for Proteomics

Mark Lennox*, Barry Devereux, Neil Robertson

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Deep learning has become an innovative tool for predicting the properties of a protein. However, obtaining an accurate predictive model using deep learning methods typically requires a large amount of labelled data, which is expensive and time-consuming to accumulate. Even when optimised, these algorithms are often black boxes, which make it challenging to interpret the decision-making processes that lead to the final prediction. Therefore, there is a demand for innovative modelling techniques that overcome these drawbacks within the space of bioinformatic deep learning. To address these issues, we have designed a modelling scheme that utilises techniques from com- puter vision. Specifically, we explore how triplet-networks can form a robust model architecture that is capable of learning and ranking proteins from just a few labelled examples. We evaluate our model on a variety of downstream tasks, including peak absorption wavelength, enantioselectivity, plasma membrane lo- calisation, and thermostability. The embedded representations produced by this method show considerable improvement when compared to previous baseline models. Finally, to emphasise that this is an example of white-box deep learning, we visualised the features produced by the algorithm to gain a better understand- ing as to how the network reaches its prediction for each protein property.
Original languageEnglish
Title of host publication IEEE 2020 International Conference on Machine Learning and Applications
Publisher IEEE
Publication statusAccepted - 16 Sep 2020
EventIEEE 2020 International Conference on Machine Learning and Applications -
Duration: 14 Dec 2020 → …


ConferenceIEEE 2020 International Conference on Machine Learning and Applications
Abbreviated titleICMLA 2020
Period14/12/2020 → …
Internet address

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