Feature assisted stacked attentive shortest dependency path based Bi-LSTM model for protein–protein interaction

Yadav, Shweta; Ekbal, Asif; Saha, Sriparna; Kumar, Ankit; Bhattacharyya, Pushpak

doi:10.1016/j.knosys.2018.11.020

Cited by 64 publications

(43 citation statements)

References 27 publications

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“…Other bi-directional stacked neural networks [33], [34] have shown excellent experimental results. The basic idea of Bi-S-SRU is to superimpose a forward and a backward SRU into each training sequence, and the two SRUs are connected to an output layer.…”

Section: B Principle Of Bi-s-sru Deep Learning Modelmentioning

confidence: 99%

Accurate Prediction Scheme of Water Quality in Smart Mariculture With Deep Bi-S-SRU Learning Network

Liu

et al. 2020

IEEE Access

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In the smart mariculture, the timely and accurate predictions of water quality can help farmers take countermeasures before the ecological environment deteriorates seriously. However, the openness of the mariculture environment makes the variation of water quality nonlinear, dynamic and complex. Traditional methods face challenges in prediction accuracy and generalization performance. To address these problems, an accurate water quality prediction scheme is proposed for pH, water temperature and dissolved oxygen. First, we construct a new huge raw data set collected in time series consisting of 23,204 groups of data. Then, the water quality parameters are preprocessed for data cleaning successively through threshold processing, mean proximity method, wavelet filter, and improved smoothing method. Next, the correlation between the water quality to be predicted and other dynamics parameters is revealed by the Pearson correlation coefficient method. Meanwhile, the data for training is weighted by the discovered correlation coefficients. Finally, by adding a backward SRU node to the training sequence, which can be integrated into the future context information, the deep Bi-S-SRU (Bi-directional Stacked Simple Recurrent Unit) learning network is proposed. After training, the prediction model can be obtained. The experimental results demonstrate that our proposed prediction method achieve higher prediction accuracy than the method based on RNN (Recurrent Neural Network) or LSTM (Long Short-Term Memory) with similar or less time computing complexity. In our experiments, the proposed method takes 12.5ms to predict data on average, and the prediction accuracy can reach 94.42% in the next 3∼8 days. INDEX TERMS Smart mariculture, precision agriculture, water quality prediction, SRU, deep learning.

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Section: B Principle Of Bi-s-sru Deep Learning Modelmentioning

confidence: 99%

Accurate Prediction Scheme of Water Quality in Smart Mariculture With Deep Bi-S-SRU Learning Network

Liu

et al. 2020

IEEE Access

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“…Yadav et al proposed a novel algorithm based on the deep bidirectional long shortterm memory (Bi-LSTM) that exploited word sequences and dependency path related information to predict PPI [26]. They also proposed an algorithm based on the attentive deep RNN, which combined multiple levels of representations using word sequences and dependency path related information to predict PPI [27]. Ahmed proposed a novel tree RNN with the attention mechanism to predict PPI [28].…”

Section: Background and Related Woekmentioning

confidence: 99%

Efficient ResNet Model to Predict Protein-Protein Interactions With GPU Computing

Hong

Wang

et al. 2020

IEEE Access

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Protein-protein interactions (PPI) play an important role in the cell activities of organisms. The deep research about PPI can help humans understand the mechanism of life activities and apply protein functions better. Nowadays, PPI prediction algorithms based on amino acid sequences using the recurrent neural network (RNN) can overcome the disadvantages of traditional biological experimental methods and achieve high accuracy. However, these algorithms are usually time-consuming and cannot take full advantage of graphics processing units (GPU) with efficient computation performance to accelerate PPI prediction, because the RNN model considers the time series of sequences. In this paper, we propose an efficient algorithm based on the residual network (ResNet) model to predict PPI (ResPPI). Our algorithm uses the embedding method to represent amino acid sequences, combining the advantages of powerful feature extraction capabilities of the ResNet with deep layers and GPU performance. The experimental results show that the ResPPI algorithm can ensure high accuracy and reduce training time greatly. Based on the ordinary GPU device, compared with the state-of-the-art LSTM model, the speed of the ResPPI algorithm is five times faster than that of the LSTM, whereas the ResPPI algorithm can achieve similar accuracy to the LSTM. Besides, in the case of unbalanced datasets, the ResPPI algorithm can perform better. INDEX TERMS Protein-protein interactions (PPI), residual network (ResNet), graphics processing units (GPU) computing, training speed.

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“…Zhang et al [28] showed how leveraging the complementary advantages of RNNs and CNNs in a combined hybrid model improves biomedical relation extraction. Yadav et al [29] experimented with a bidirectional LSTM network with an attention mechanism, exploiting word sequences and the shortest dependency path between the entities, whereas Zhang et al [30] introduced a residual CNN to tackle the task. Ahmed et al [31] exploited a tree LSTM network using a structured attention architecture, showing how the attention mechanism improves the performance in relation extraction.…”

Section: Related Workmentioning

confidence: 99%

High-Precision Biomedical Relation Extraction for Reducing Human Curation Efforts in Industrial Applications

et al. 2020

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The body of biomedical literature is growing at an unprecedented rate, exceeding the ability of researchers to make effective use of this knowledge-rich amount of information. This growth has created interest in biomedical relation extraction approaches to extract domain-specific knowledge for diverse applications. Despite the great progress in the techniques, the retrieved evidence still needs to undergo a time-consuming manual curation process to be truly useful. Most relation extraction systems have been conceived in the context of Shared Tasks, with the goal of maximizing the F1 score on restricted, domainspecific test sets. However, in industrial applications relations typically serve as input to a pipeline of biologically driven analyses; as a result, highly precise extractions are central for cutting down the manual curation effort, thus to translate the research evidence into practice smoothly and reliably. In this paper, we present a highly precise relation extraction system designed to reduce human curation efforts. The engine is made up of sophisticated rules that leverage linguistic aspects of the texts rather than sticking on application-specific training data. As a result, the system could be applied to diverse needs. Experiments on gold-standard corpora show that the system achieves the highest precision compared with previous rulebased, kernel-based, and neural approaches, while maintaining a F1 score comparable or superior to other methods. To show the usefulness of our approach in industrial scenarios, we finally present a case study on the mTOR pathway, showing how it could be applied on a large-scale.

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Feature assisted stacked attentive shortest dependency path based Bi-LSTM model for protein–protein interaction

Cited by 64 publications

References 27 publications

Accurate Prediction Scheme of Water Quality in Smart Mariculture With Deep Bi-S-SRU Learning Network

Accurate Prediction Scheme of Water Quality in Smart Mariculture With Deep Bi-S-SRU Learning Network

Efficient ResNet Model to Predict Protein-Protein Interactions With GPU Computing

High-Precision Biomedical Relation Extraction for Reducing Human Curation Efforts in Industrial Applications

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