ArticleFrontiers in genetics2021
Identifying Membrane Protein Types Based on Lifelong Learning With Dynamically Scalable Networks.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Haplotype-based multi-locus genome-wide association study reveals genomic regions associated with reproductive stage high temperature stress tolerance in rice.Plant molecular biology · 2026Article
- Molecular Genetics and Epigenetics Regulatory Role of miRNAs on Adipogenesis and Intramuscular Fat Development in Beef Cattle: In the Context of Meat Quality.Molecular biotechnology · 2026Review
- A deployable film method to enable replicable sampling of low-abundance environmental microbiomes.Scientific reports · 2024Article
- miRNAs and Their Target Genes Play a Critical Role in Response to Heat Stress in Cynodon dactylon (L.) Pers.Molecular biotechnology · 2023Article
- Role of lncRNAs in Remodeling of the Coronary Artery Plaques in Patients with Atherosclerosis.Molecular diagnosis & therapy · 2023Article
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Authors and funding
7 authors.
Funding
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Abstract
Membrane proteins are an essential part of the body's ability to maintain normal life activities. Further research into membrane proteins, which are present in all aspects of life science research, will help to advance the development of cells and drugs. The current methods for predicting proteins are usually based on machine learning, but further improvements in prediction effectiveness and accuracy are needed. In this paper, we propose a dynamic deep network architecture based on lifelong learning in order to use computers to classify membrane proteins more effectively. The model extends the application area of lifelong learning and provides new ideas for multiple classification problems in bioinformatics. To demonstrate the performance of our model, we conducted experiments on top of two datasets and compared them with other classification methods. The results show that our model achieves high accuracy (95.3 and 93.5%) on benchmark datasets and is more effective compared to other methods.
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Registered trials
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