Conference proceeding
Visualizing and Annotating Protein Sequences using A Deep Neural Network
2020 54th Asilomar Conference on Signals, Systems, and Computers, v 2020-, pp 506-510
01 Nov 2020
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
It is critical for biological studies to annotate amino acid sequences and understand how proteins function. Protein function is important to medical research in the health industry (e.g., drug discovery). With the advancement of deep learning, accurate protein annotation models have been developed for alignment free protein annotation. In this paper, we develop a deep learning model with an attention mechanism that can predict Gene Ontology labels given a protein sequence input. We believe this model can produce accurate predictions as well as maintain good interpretability. We further show how the model can be interpreted by examining and visualizing the intermediate layer output in our deep neural network.
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Details
- Title
- Visualizing and Annotating Protein Sequences using A Deep Neural Network
- Creators
- Zhengqiao Zhao - Drexel UniversityGail Rosen - Drexel University
- Publication Details
- 2020 54th Asilomar Conference on Signals, Systems, and Computers, v 2020-, pp 506-510
- Publisher
- IEEE
- Grant note
- National Science Foundation (10.13039/100000001)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000681731800099
- Scopus ID
- 2-s2.0-85107730836
- Other Identifier
- 991019168848304721
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