Learned Embeddings from Deep Learning to Visualize and Predict Protein Sets

Learned Embeddings from Deep Learning to Visualize and Predict Protein Sets

Models from machine learning (ML) or artificial intelligence (AI) increasingly assist in guiding experimental design and decision making in molecular biology and medicine. Recently, Language Models (LMs) have been adapted from Natural Language Processing (NLP) to encode the implicit language written...

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Bibliographic Details
Main Authors: Dallago, C. (Author), Heinzinger, M. (Author), Littmann, M. (Author), Lu, A.X (Author), Min, S. (Author), Morton, J.T (Author), Olenyi, T. (Author), Rost, B. (Author), Schütze, K. (Author), Yang, K.K (Author), Yoon, S. (Author)
Format: Article
Language:English
Published: Blackwell Publishing Inc. 2021
Subjects:
amino acid sequence
article
artificial intelligence
Artificial Intelligence
cellular distribution
classifier
decision making
deep learning
Deep Learning
deep learning embeddings
embedding
experimental design
human
human experiment
machine learning
Machine Learning
molecular biology
natural language processing
Natural Language Processing
pipeline
protein
protein annotation pipeline
protein function
protein representations
protein visualization
Proteins
workflow
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