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.
|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|
|Number of pages||6|
|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
|Name||Proceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020|
|Conference||19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020|
|Period||14/12/2020 → 17/12/2020|
Bibliographical notePublisher Copyright:
© 2020 IEEE.
Copyright 2021 Elsevier B.V., All rights reserved.
- Deep Learning
- Metric Learning
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Computer Vision and Pattern Recognition
- Hardware and Architecture