Trinh-Trung-Duong Nguyen
Yuan Ze University
6 Papers
9 Citations
Trinh-Trung-Duong Nguyen is an academic researcher from Yuan Ze University. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 3, co-authored 6 publications.
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Papers
A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information
TL;DR: In this article, the authors presented a novel technique by incorporating BERT-based multilingual model in bioinformatics to represent the information of DNA sequences, and treated DNA sequences as natural sentences and then used BERT models to transform them into fixed-length numerical matrices.
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FAD-BERT: Improved prediction of FAD binding sites using pre-training of deep bidirectional transformers.
TL;DR: In this article, a new approach based on pre-training of Bidirectional Encoder Representations from Transformers (BERT), Position-specific Scoring Matrix profiles (PSSM), Amino Acid Index database (AAIndex) was proposed to predict FAD-binding sites from the transport proteins which are found in nature recently.
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GT-Finder: Classify the family of glucose transporters with pre-trained BERT language models.
TL;DR: A bidirectional transformer-based protein model (TransportersBERT) is developed for comparison with existing pre-trained BERT models and it is observed that BERT-Base and Bert-Large models improved the performance by more than 4% in terms of average sensitivity and Matthews correlation coefficient, indicating the efficiency of this approach.
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Addressing data imbalance problems in ligand-binding site prediction using a variational autoencoder and a convolutional neural network.
TL;DR: In this paper, a deep neural network-based variational autoencoder (VAE) was proposed to learn important attributes of the minority classes concerning nonlinear combinations, and the trained VAE was used to generate new minority class samples that were later added to the original data to create a balanced dataset.
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Incorporating a transfer learning technique with amino acid embeddings to efficiently predict N-linked glycosylation sites in ion channels
TL;DR: In this paper, the authors used transfer learning to predict N-linked glycosylation sites in ion channel proteins, which achieved an accuracy, specificity, sensitivity, and Matthews correlation coefficient of 93.4, 92.8, 98.6, and 0.717.
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