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

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Abstract

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 publicationProceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020
EditorsM. Arif Wani, Feng Luo, Xiaolin Li, Dejing Dou, Francesco Bonchi
Publisher IEEE
Pages308-313
Number of pages6
ISBN (Electronic)9781728184708
DOIs
Publication statusPublished - 23 Feb 2021
Event19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020 - Virtual, Miami, United States
Duration: 14 Dec 202017 Dec 2020

Publication series

NameProceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020

Conference

Conference19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020
Country/TerritoryUnited States
CityVirtual, Miami
Period14/12/202017/12/2020

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Deep Learning
  • Metric Learning
  • Proteomics

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture

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