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 language | English |
---|---|
Title of host publication | Proceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020 |
Editors | M. Arif Wani, Feng Luo, Xiaolin Li, Dejing Dou, Francesco Bonchi |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 308-313 |
Number of pages | 6 |
ISBN (Electronic) | 9781728184708 |
DOIs | |
Publication status | Published - 23 Feb 2021 |
Event | 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020 - Virtual, Miami, United States Duration: 14 Dec 2020 → 17 Dec 2020 |
Publication series
Name | Proceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020 |
---|
Conference
Conference | 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020 |
---|---|
Country/Territory | United States |
City | Virtual, Miami |
Period | 14/12/2020 → 17/12/2020 |
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
Fingerprint
Dive into the research topics of 'Deep Metric Learning for Proteomics'. Together they form a unique fingerprint.Student theses
-
Deep learning of proteomics data
Lennox, M. (Author), Robertson, N. (Supervisor) & Devereux, B. (Supervisor), Dec 2021Student thesis: Doctoral Thesis › Doctor of Philosophy
File