ArticleBioMed research international2017
Protein Function Prediction Using Deep Restricted Boltzmann Machines.
Article in BioMed research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 17 citations in OpenAlex.
- Thermal Adaptation of Cytosolic Malate Dehydrogenase Revealed by Deep Learning and Coevolutionary Analysis.Journal of chemical theory and computation · 2025Article
- Emerging Artificial Neuron Devices for Probabilistic Computing.Frontiers in neuroscience · 2021Review
- Protein function prediction with gene ontology: from traditional to deep learning models.PeerJ · 2021Article
- AI applications in functional genomics.Computational and structural biotechnology journal · 2021Review
- The Power of Universal Contextualized Protein Embeddings in Cross-species Protein Function Prediction.Evolutionary bioinformatics online · 2021Article
- Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning.Briefings in bioinformatics · 2020Article
- Artificial Neural Networks in Computer-Aided Drug Design: An Overview of Recent Advances.Advances in experimental medicine and biology · 2020Review
- DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks.Scientific reports · 2019Article
- Identification of Antioxidant Proteins With Deep Learning From Sequence Information.Frontiers in pharmacology · 2018Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurately annotating biological functions of proteins is one of the key tasks in the postgenome era. Many machine learning based methods have been applied to predict functional annotations of proteins, but this task is rarely solved by deep learning techniques. Deep learning techniques recently have been successfully applied to a wide range of problems, such as video, images, and nature language processing. Inspired by these successful applications, we investigate deep restricted Boltzmann machines (DRBM), a representative deep learning technique, to predict the missing functional annotations of partially annotated proteins. Experimental results on
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