Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles

Le, Nguyen Quoc Khanh; Lin, Chih-Min; Yapp, Edward Kien Yee; Yeh, Hui‐Yuan

doi:10.1016/j.cmpb.2019.05.016

Cited by 69 publications

(52 citation statements)

References 38 publications

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“…Hence, it is surprising that many DL applications in GP have not paid enough attention to this problem (Ma et al, 2017;Montesinos-López et al, 2018b;Montesinos-López et al, 2019b). Several approaches have been proposed for hyperparameter tuning (e.g., Bellot et al, 2018;Cho and Hegde, 2019;Le et al, 2019;Rajaraman et al, 2019;Yoo, 2019). Here, DL architectures were optimized using Talos (Autonomia Talos, 2019), which works combining all parameters in a grid.…”

Section: Hyperparameter Optimizationmentioning

confidence: 99%

Exploring Deep Learning for Complex Trait Genomic Prediction in Polyploid Outcrossing Species

et al. 2020

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Section: Hyperparameter Optimizationmentioning

confidence: 99%

Exploring Deep Learning for Complex Trait Genomic Prediction in Polyploid Outcrossing Species

et al. 2020

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“…In order to improve the diagnosis results, more data sets are required for training the model [2,6]. Deep learning (DL) is one of the ML approaches which has been successfully implemented in a diverse range of biomedical fields with large datasets, as presented in references [7,8]. This approach has become a promising field and has been proliferating in recent years [9,10].…”

Section: Introductionmentioning

confidence: 99%

An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsupervised Feature Extraction Technique

et al. 2019

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An automated classification system based on a Deep Learning (DL) technique for Cardiac Disease (CD) monitoring and detection is proposed in this paper. The proposed DL architecture is divided into Deep Auto-Encoders (DAEs) as an unsupervised form of feature learning and Deep Neural Networks (DNNs) as a classifier. The objective of this study is to improve on the previous machine learning technique that consists of several data processing steps such as feature extraction and feature selection or feature reduction. It is also noticed that the previously used machine learning technique required human interference and expertise in determining robust features, yet was time-consuming in the labeling and data processing steps. In contrast, DL enables an embedded feature extraction and feature selection in DAEs pre-training and DNNs fine-tuning process directly from raw data. Hence, DAEs is able to extract high-level of features not only from the training data but also from unseen data. The proposed model uses 10 classes of imbalanced data from ECG signals. Since it is related to the cardiac region, abnormality is usually considered for an early diagnosis of CD. In order to validate the result, the proposed model is compared with the shallow models and DL approaches. Results found that the proposed method achieved a promising performance with 99.73% accuracy, 91.20% sensitivity, 93.60% precision, 99.80% specificity, and a 91.80% F1-Score. Moreover, both the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR) curve from the confusion matrix showed that the developed model is a good classifier. The developed model based on unsupervised feature extraction and deep neural network is ready to be used on a large population before its installation for clinical usage.

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“…All layers are combined to form the architecture of our CNN system using a specific order. Similar to many published works in this field (Le et al, 2018(Le et al, , 2019aNguyen et al, 2019), different layers used in CNN for the current study include:…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Identifying SNAREs by Incorporating Deep Learning Architecture and Amino Acid Embedding Representation

Lin

2019

Front. Physiol.

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SNAREs (soluble N-ethylmaleimide-sensitive factor activating protein receptors) are a group of proteins that are crucial for membrane fusion and exocytosis of neurotransmitters from the cell. They play an important role in a broad range of cell processes, including cell growth, cytokinesis, and synaptic transmission, to promote cell membrane integration in eukaryotes. Many studies determined that SNARE proteins have been associated with a lot of human diseases, especially in cancer. Therefore, identifying their functions is a challenging problem for scientists to better understand the cancer disease as well as design the drug targets for treatment. We described each protein sequence based on the amino acid embeddings using fastText, which is a natural language processing model performing well in its field. Because each protein sequence is similar to a sentence with different words, applying language model into protein sequence is challenging and promising. After generating, the amino acid embedding features were fed into a deep learning algorithm for prediction. Our model which combines fastText model and deep convolutional neural networks could identify SNARE proteins with an independent test accuracy of 92.8%, sensitivity of 88.5%, specificity of 97%, and Matthews correlation coefficient (MCC) of 0.86. Our performance results were superior to the state-of-the-art predictor (SNARE-CNN). We suggest this study as a reliable method for biologists for SNARE identification and it serves a basis for applying fastText word embedding model into bioinformatics, especially in protein sequencing prediction.

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Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles

Cited by 69 publications

References 38 publications

Exploring Deep Learning for Complex Trait Genomic Prediction in Polyploid Outcrossing Species

Exploring Deep Learning for Complex Trait Genomic Prediction in Polyploid Outcrossing Species

An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsupervised Feature Extraction Technique

Identifying SNAREs by Incorporating Deep Learning Architecture and Amino Acid Embedding Representation

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