Target Identification Among Known Drugs by Deep Learning from Heterogeneous Networks

2019 ◽  
Author(s):  
Xiangxiang Zeng ◽  
Siyi Zhu ◽  
Weiqiang Lu ◽  
Jin Huang ◽  
Zehui Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Heterogeneous Networks ◽  
Target Identification

scholarly journals Target identification among known drugs by deep learning from heterogeneous networks

2020 ◽  
Vol 11 (7) ◽  
pp. 1775-1797 ◽  
Author(s):  
Xiangxiang Zeng ◽  
Siyi Zhu ◽  
Weiqiang Lu ◽  
Zehui Liu ◽  
Jin Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Heterogeneous Networks ◽  
Target Identification ◽  
Drug Repurposing ◽  
Disease Network ◽  
The Relationship

Target identification and drug repurposing could benefit from network-based, rational deep learning prediction, and explore the relationship between drugs and targets in the heterogeneous drug–gene–disease network.

Federated Deep Learning for Collaborative Intrusion Detection in Heterogeneous Networks

2021 ◽  
Author(s):  
Segun I. Popoola ◽  
Guan Gui ◽  
Bamidele Adebisi ◽  
Mohammad Hammoudeh ◽  
Haris Gacanin
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Heterogeneous Networks ◽  
Collaborative Intrusion Detection

scholarly journals A Deep Neural Network-Based Target Recognition Algorithm for Robot Scenes

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Lijing Liu
Keyword(s):  
Deep Learning ◽  
Target Detection ◽  
Target Identification ◽  
Target Recognition ◽  
Recognition Algorithm ◽  
Accuracy Rate ◽  
Intelligent Robots ◽  
Vision Sensors ◽  
Pyramid Model ◽  
High Level

Intelligent robots are a key vehicle for artificial intelligence and are widely employed in all aspects of everyday life and work, not just in the industry. One of the talents required for intelligent robots to complete their jobs is the capacity to identify their environment, which is a crucial obstacle to be overcome. Deep learning-based target identification algorithms currently do not fully leverage the link between high-level semantic and low-level detail information in the prediction step and hence are less successful in recognizing tiny target objects. Target recognition via vision sensors has also improved in accuracy and efficiency because of the development of deep learning. However, due to the insufficient usage of semantic information and precise texture information of underlying characteristics, tiny target recognition remains a difficulty. To address the aforementioned issues, we propose a target detection method based on a jump-connected pyramid model to improve the target detection performance of robots in complex scenarios. In order to verify the effectiveness of the algorithm, we designed and implemented a software system for target detection of intelligent robots and performed software integration of the proposed algorithm model with excellent experimental results. These experiments reveal that, when compared to other algorithms, our suggested algorithm’s characteristics have higher flexibility and robustness and can deliver a higher scene classification accuracy rate.

scholarly journals Identifying piRNA targets on mRNAs in C. elegans using a deep multi-head attention network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tzu-Hsien Yang ◽  
Sheng-Cian Shiue ◽  
Kuan-Yu Chen ◽  
Yan-Yuan Tseng ◽  
Wei-Sheng Wu
Keyword(s):  
Deep Learning ◽  
Target Identification ◽  
Target Prediction ◽  
Messenger Rnas ◽  
Learning Method ◽  
C Elegans ◽  
High Prediction ◽  
Systematic Understanding ◽  
Fully Connected ◽  
Mrna Binding

Abstract Background Piwi-interacting RNAs (piRNAs) are the small non-coding RNAs (ncRNAs) that silence genomic transposable elements. And researchers found out that piRNA also regulates various endogenous transcripts. However, there is no systematic understanding of the piRNA binding patterns and how piRNA targets genes. While various prediction methods have been developed for other similar ncRNAs (e.g., miRNAs), piRNA holds distinctive characteristics and requires its own computational model for binding target prediction. Results Recently, transcriptome-wide piRNA binding events in C. elegans were probed by PRG-1 CLASH experiments. Based on the probed piRNA-messenger RNAs (mRNAs) binding pairs, in this research, we devised the first deep learning architecture based on multi-head attention to computationally identify piRNA targeting mRNA sites. In the devised deep network, the given piRNA and mRNA segment sequences are first one-hot encoded and undergo a combined operation of convolution and squeezing-extraction to unravel motif patterns. And we incorporate a novel multi-head attention sub-network to extract the hidden piRNA binding rules that can simulate the biological piRNA target recognition process. Finally, the true piRNA–mRNA binding pairs are identified by a deep fully connected sub-network. Our model obtains a supreme discriminatory power of AUC $$=$$ = 93.3% on an independent test set and successfully extracts the verified binding pattern of a synthetic piRNA. These results demonstrated that the devised model achieves high prediction performance and suggests testable potential biological piRNA binding rules. Conclusions In this research, we developed the first deep learning method to identify piRNA targeting sites on C. elegans mRNAs. And the developed deep learning method is demonstrated to be of high accuracy and can provide biological insights into piRNA–mRNA binding patterns. The piRNA binding target identification network can be downloaded from http://cosbi2.ee.ncku.edu.tw/data_download/piRNA_mRNA_binding.

scholarly journals Deep learning-based edge caching for multi-cluster heterogeneous networks

2019 ◽  
Vol 32 (19) ◽  
pp. 15317-15328 ◽  
Author(s):  
Jiachen Yang ◽  
Jipeng Zhang ◽  
Chaofan Ma ◽  
Huihui Wang ◽  
Juping Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Heterogeneous Networks ◽  
Edge Caching

scholarly journals Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1730
Author(s):  
Dan Deng ◽  
Xingwang Li ◽  
Ming Zhao ◽  
Khaled M. Rabie ◽  
Rupak Kharel
Keyword(s):  
Deep Learning ◽  
Heterogeneous Networks ◽  
Null Space ◽  
Information Leakage ◽  
Doppler Frequency ◽  
Base Station ◽  
Correlation Factor ◽  
Imperfect Csi ◽  
Deep Convolutional Neural Networks ◽  
Mimo Communications

Perfect channel state information (CSI) is required in most of the classical physical-layer security techniques, while it is difficult to obtain the ideal CSI due to the time-varying wireless fading channel. Although imperfect CSI has a great impact on the security of MIMO communications, deep learning is becoming a promising solution to handle the negative effect of imperfect CSI. In this work, we propose two types of deep learning-based secure MIMO detectors for heterogeneous networks, where the macro base station (BS) chooses the null-space eigenvectors to prevent information leakage to the femto BS. Thus, the bit error rate of the associated user is adopted as the metric to evaluate the system performance. With the help of deep convolutional neural networks (CNNs), the macro BS obtains the refined version from the imperfect CSI. Simulation results are provided to validate the proposed algorithms. The impacts of system parameters, such as the correlation factor of imperfect CSI, the normalized doppler frequency, the number of antennas is investigated in different setup scenarios. The results show that considerable performance gains can be obtained from the deep learning-based detectors compared with the classical maximum likelihood algorithm.

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