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Wang T, Wang R, Wei L. AttenSyn: An Attention-Based Deep Graph Neural Network for Anticancer Synergistic Drug Combination Prediction. J Chem Inf Model 2024; 64:2854-2862. [PMID: 37565997 DOI: 10.1021/acs.jcim.3c00709] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Identifying synergistic drug combinations is fundamentally important to treat a variety of complex diseases while avoiding severe adverse drug-drug interactions. Although several computational methods have been proposed, they highly rely on handcrafted feature engineering and cannot learn better interactive information between drug pairs, easily resulting in relatively low performance. Recently, deep-learning methods, especially graph neural networks, have been widely developed in this area and demonstrated their ability to address complex biological problems. In this study, we proposed AttenSyn, an attention-based deep graph neural network for accurately predicting synergistic drug combinations. In particular, we adopted a graph neural network module to extract high-latent features based on the molecular graphs only and exploited the attention-based pooling module to learn interactive information between drug pairs to strengthen the representations of drug pairs. Comparative results on the benchmark datasets demonstrated that our AttenSyn performs better than the state-of-the-art methods in the prediction of anticancer synergistic drug combinations. Additionally, to provide good interpretability of our model, we explored and visualized some crucial substructures in drugs through attention mechanisms. Furthermore, we also verified the effectiveness of our proposed AttenSyn on two cell lines by visualizing the features of drug combinations learnt from our model, exhibiting satisfactory generalization ability.
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Affiliation(s)
- Tianshuo Wang
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
| | - Ruheng Wang
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
| | - Leyi Wei
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
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2
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Zhang Z, Wei X. Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy. Semin Cancer Biol 2023; 90:57-72. [PMID: 36796530 DOI: 10.1016/j.semcancer.2023.02.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/12/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023]
Abstract
The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.
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Affiliation(s)
- Zhe Zhang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, PR China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu 610041, PR China
| | - Xiawei Wei
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, PR China.
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3
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Partin A, Brettin TS, Zhu Y, Narykov O, Clyde A, Overbeek J, Stevens RL. Deep learning methods for drug response prediction in cancer: Predominant and emerging trends. Front Med (Lausanne) 2023; 10:1086097. [PMID: 36873878 PMCID: PMC9975164 DOI: 10.3389/fmed.2023.1086097] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 01/23/2023] [Indexed: 02/17/2023] Open
Abstract
Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, neural network architectures, learning methodologies, and evaluations schemes. However, deciphering promising predominant and emerging trends is difficult due to the variety of explored methods and lack of standardized framework for comparing drug response prediction models. To obtain a comprehensive landscape of deep learning methods, we conducted an extensive search and analysis of deep learning models that predict the response to single drug treatments. A total of 61 deep learning-based models have been curated, and summary plots were generated. Based on the analysis, observable patterns and prevalence of methods have been revealed. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.
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Affiliation(s)
- Alexander Partin
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Thomas S. Brettin
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Yitan Zhu
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Oleksandr Narykov
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Austin Clyde
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Jamie Overbeek
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Rick L. Stevens
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
- Department of Computer Science, The University of Chicago, Chicago, IL, United States
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4
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Li TH, Wang CC, Zhang L, Chen X. SNRMPACDC: computational model focused on Siamese network and random matrix projection for anticancer synergistic drug combination prediction. Brief Bioinform 2023; 24:6843566. [PMID: 36418927 DOI: 10.1093/bib/bbac503] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 09/22/2022] [Accepted: 10/24/2022] [Indexed: 11/25/2022] Open
Abstract
Synergistic drug combinations can improve the therapeutic effect and reduce the drug dosage to avoid toxicity. In previous years, an in vitro approach was utilized to screen synergistic drug combinations. However, the in vitro method is time-consuming and expensive. With the rapid growth of high-throughput data, computational methods are becoming efficient tools to predict potential synergistic drug combinations. Considering the limitations of the previous computational methods, we developed a new model named Siamese Network and Random Matrix Projection for AntiCancer Drug Combination prediction (SNRMPACDC). Firstly, the Siamese convolutional network and random matrix projection were used to process the features of the two drugs into drug combination features. Then, the features of the cancer cell line were processed through the convolutional network. Finally, the processed features were integrated and input into the multi-layer perceptron network to get the predicted score. Compared with the traditional method of splicing drug features into drug combination features, SNRMPACDC improved the interpretability of drug combination features to a certain extent. In addition, the introduction of convolutional networks can better extract the potential information in the features. SNRMPACDC achieved the root mean-squared error of 15.01 and the Pearson correlation coefficient of 0.75 in 5-fold cross-validation of regression prediction for response data. In addition, SNRMPACDC achieved the AUC of 0.91 ± 0.03 and the AUPR of 0.62 ± 0.05 in 5-fold cross-validation of classification prediction of synergistic or not. These results are almost better than all the previous models. SNRMPACDC would be an effective approach to infer potential anticancer synergistic drug combinations.
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Affiliation(s)
- Tian-Hao Li
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Xing Chen
- Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China
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5
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Lone F, Verma HK, Sharma KP. A systematic study on the challenges, characteristics and security issues in vehicular networks. INTERNATIONAL JOURNAL OF PERVASIVE COMPUTING AND COMMUNICATIONS 2023. [DOI: 10.1108/ijpcc-04-2022-0164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Purpose
The purpose of this study is to extensively explore the vehicular network paradigm, challenges faced by them and provide a reasonable solution for securing these vulnerable networks. Vehicle-to-everything (V2X) communication has brought the long-anticipated goal of safe, convenient and sustainable transportation closer to reality. The connected vehicle (CV) paradigm is critical to the intelligent transportation systems vision. It imagines a society free of a troublesome transportation system burdened by gridlock, fatal accidents and a polluted environment. The authors cannot overstate the importance of CVs in solving long-standing mobility issues and making travel safer and more convenient. It is high time to explore vehicular networks in detail to suggest solutions to the challenges encountered by these highly dynamic networks.
Design/methodology/approach
This paper compiles research on various V2X topics, from a comprehensive overview of V2X networks to their unique characteristics and challenges. In doing so, the authors identify multiple issues encountered by V2X communication networks due to their open communication nature and high mobility, especially from a security perspective. Thus, this paper proposes a trust-based model to secure vehicular networks. The proposed approach uses the communicating nodes’ behavior to establish trustworthy relationships. The proposed model only allows trusted nodes to communicate among themselves while isolating malicious nodes to achieve secure communication.
Findings
Despite the benefits offered by V2X networks, they have associated challenges. As the number of CVs on the roads increase, so does the attack surface. Connected cars provide numerous safety-critical applications that, if compromised, can result in fatal consequences. While cryptographic mechanisms effectively prevent external attacks, various studies propose trust-based models to complement cryptographic solutions for dealing with internal attacks. While numerous trust-based models have been proposed, there is room for improvement in malicious node detection and complexity. Optimizing the number of nodes considered in trust calculation can reduce the complexity of state-of-the-art solutions. The theoretical analysis of the proposed model exhibits an improvement in trust calculation, better malicious node detection and fewer computations.
Originality/value
The proposed model is the first to add another dimension to trust calculation by incorporating opinions about recommender nodes. The added dimension improves the trust calculation resulting in better performance in thwarting attacks and enhancing security while also reducing the trust calculation complexity.
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Mohan A, Kumar R, Kumar V, Yadav M. Homocysteine, Vitamin B12 and Folate Level: Possible Risk Factors in the Progression of Chronic Heart and Kidney Disorders. Curr Cardiol Rev 2023; 19:e090223213539. [PMID: 36757031 PMCID: PMC10494275 DOI: 10.2174/1573403x19666230209111854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 11/28/2022] [Accepted: 12/08/2022] [Indexed: 02/10/2023] Open
Abstract
Cobalamin is an essential molecule for humans; it is exceptionally important for various body functions, including deoxyribonucleic acid synthesis and cellular energy production. Vegans are more vulnerable to vitamin B12 deficiency than natives with moderate consumption of animal dietary supplements or people with inadequate nutritional patterns. However, the long-term effects of sub-medical deficiency have not been thoroughly studied, but they may have a negative impact on the cardiovascular system, pregnancy outcomes, and vascular, renal, cognitive, bone, and eye health. Alongside the statin remedy, that is a powerful approach for CVD prevention. Another approach is related to the B nutrition substitution remedy with folic acid, and vitamins B6 and B12 are extensively practised nowadays. There is a tremendous interest in plasma homocysteine (tHcy) as a cardiovascular hazard factor. However, current research in the field of its prevention is more inclined toward confirming the benefit of tHcy-reducing remedy with vitamin B12. Thus, while folic acid fortification is primarily aimed at reducing neural-tube defects, it may also play a significant role in the primary prevention of CVD by lowering tHcy. Folate and B-vitamins play important roles in CVD prevention and nutrition policy implementation. Patients affected with Chronic Kidney Disease (CKD) or end-stage Stage Renal Disease (ESRD) experience a tremendous cardiovascular threat that may also further lead to death. As a result, routine monitoring of vitamin B12 levels is likely to be beneficial for the early detection and treatment of metabolic vitamin B12 deficiency, as well as the prevention of heart-related diseases.
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Affiliation(s)
- Aditi Mohan
- Amity Institute of Biotechnology, Amity University, Noida Sector 125, Uttar Pradesh, India
| | - Ravi Kumar
- Department of Life Sciences, Bharathiyar University, Coimbatore, Tamil Nadu 641046, India
| | - Vivek Kumar
- Delhi Pharmaceutical Sciences and Research University, New Delhi 110017, India
| | - Mahendra Yadav
- Narayan Institute of Pharmacy, Gopal Narayan Singh University, Sasaram, Rohtas, Bihar 821305, India
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7
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El Houssaini S, El Houssaini MA, El Kafi J. Novel approach of detecting the black hole attack for vehicular ad-hoc networks based on capability indicators. INTERNATIONAL JOURNAL OF PERVASIVE COMPUTING AND COMMUNICATIONS 2022. [DOI: 10.1108/ijpcc-02-2022-0062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
In vehicular ad hoc networks (VANETs), the information transmitted is broadcast in a free access environment. Therefore, VANETs are vulnerable against attacks that can directly perturb the performance of the networks and then provoke big fall of capability. Black hole attack is an example such attack, where the attacker node pretends that having the shortest path to the destination node and then drops the packets. This paper aims to present a new method to detect the black hole attack in real-time in a VANET network.
Design/methodology/approach
This method is based on capability indicators that are widely used in industrial production processes. If the different capability indicators are greater than 1.33 and the stability ratio (Sr) is greater than 75%, the network is stable and the vehicles are communicating in an environment without the black hole attack. When the malicious nodes representing the black hole attacks are activated one by one, the fall of capability becomes more visible and the network is unstable, out of control and unmanaged, due to the presence of the attacks. The simulations were conducted using NS-3 for the network simulation and simulation of urban mobility for generating the mobility model.
Findings
The proposed mechanism does not impose significant overheads or extensive modifications in the standard Institute of Electrical and Electronics Engineers 802.11p or in the routing protocols. In addition, it can be implemented at any receiving node which allows identifying malicious nodes in real-time. The simulation results demonstrated the effectiveness of proposed scheme to detect the impact of the attack very early, especially with the use of the short-term capability indicators (Cp, Cpk and Cpm) of each performance metrics (throughput and packet loss ratio), which are more efficient at detecting quickly and very early the small deviations over a very short time. This study also calculated another indicator of network stability which is Sr, which allows to make a final decision if the network is under control and that the vehicles are communicating in an environment without the black hole attack.
Originality/value
According to the best of the authors’ knowledge, the method, using capability indicators for detecting the black hole attack in VANETs, has not been presented previously in the literature.
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Xu C, Zhang R, Duan M, Zhou Y, Bao J, Lu H, Wang J, Hu M, Hu Z, Zhou F, Zhu W. A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia. MOLECULAR THERAPY - NUCLEIC ACIDS 2022; 28:477-487. [PMID: 35505964 PMCID: PMC9046129 DOI: 10.1016/j.omtn.2022.04.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 04/01/2022] [Indexed: 01/19/2023]
Abstract
Immune thrombocytopenia (ITP) is an autoimmune disease with the typical symptom of a low platelet count in blood. ITP demonstrated age and sex biases in both occurrences and prognosis, and adult ITP was mainly induced by the living environments. The current diagnosis guideline lacks the integration of molecular heterogenicity. This study recruited the largest cohort of platelet transcriptome samples. A comprehensive procedure of feature selection, feature engineering, and stacking classification was carried out to detect the ITP biomarkers using RNA sequencing (RNA-seq) transcriptomes. The 40 detected biomarkers were loaded to train the final ITP detection model, with an overall accuracy 0.974. The biomarkers suggested that ITP onset may be associated with various transcribed components, including protein-coding genes, long intergenic non-coding RNA (lincRNA) genes, and pseudogenes with apparent transcriptions. The delivered ITP detection model may also be utilized as a complementary ITP diagnosis tool. The code and the example dataset is freely available on http://www.healthinformaticslab.org/supp/resources.php
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Affiliation(s)
- Chengfeng Xu
- Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China
| | - Ruochi Zhang
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Meiyu Duan
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Yongming Zhou
- Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China
| | - Jizhang Bao
- Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China
| | - Hao Lu
- Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China
| | - Jie Wang
- Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China
| | - Minghui Hu
- Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China
| | - Zhaoyang Hu
- Fun-Med Pharmaceutical Technology (Shanghai) Co., Ltd., RM. A310, 115 Xinjunhuan Road, Minhang District, Shanghai 201100, China
- Corresponding author Zhaoyang Hu, PhD, Fengneng Pharmaceutical Technology (Shanghai) Co., Ltd., RM. A310, 115 Xinjunhuan Road, Minhang District, Shanghai 201100, China.
| | - Fengfeng Zhou
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
- Corresponding author Fengfeng Zhou, PhD, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China.
| | - Wenwei Zhu
- Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China
- Corresponding author Wenwei Zhu, PhD, Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China.
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Guo X, Jiang Y, Zou Q. Structured Sparse Regularized TSK Fuzzy System for predicting therapeutic peptides. Brief Bioinform 2022; 23:6570018. [PMID: 35438149 DOI: 10.1093/bib/bbac135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/19/2022] [Accepted: 03/22/2022] [Indexed: 11/13/2022] Open
Abstract
Therapeutic peptides act on the skeletal system, digestive system and blood system, have antibacterial properties and help relieve inflammation. In order to reduce the resource consumption of wet experiments for the identification of therapeutic peptides, many computational-based methods have been developed to solve the identification of therapeutic peptides. Due to the insufficiency of traditional machine learning methods in dealing with feature noise. We propose a novel therapeutic peptide identification method called Structured Sparse Regularized Takagi-Sugeno-Kang Fuzzy System on Within-Class Scatter (SSR-TSK-FS-WCS). Our method achieves good performance on multiple therapeutic peptides and UCI datasets.
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Affiliation(s)
- Xiaoyi Guo
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, P.R.China
| | - Yizhang Jiang
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, P.R.China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, P.R.China
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An Efficient Algorithm for Mapping Deep Learning Applications on the NoC Architecture. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12063163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Network-on-chip (NoC) is replacing the existing on-chip communication mechanism in the latest, very-large-scale integration (VLSI) systems because of their fault tolerant design. However, in addition to the design challenges, NoC systems require a mechanism for proper application mapping in order to produce maximum benefits in terms of application-level latency, platform energy consumption, and system throughput. Similarly, the neural-network (NN)-based artificial intelligence (AI) techniques for deep learning are gaining particular interest. These applications can be executed on a cloud-based system, but some of these applications have to be executed on private cloud to integrate the data privacy. Furthermore, the public cloud systems can also be made from these NoC platforms to have better application performance. Therefore, there is a need to optimally map these applications on existing NoC-based architectures. If the application is not properly mapped, then it can create a performance hazard that may lead to delay in calculations, increase in energy consumption, and decrease in the platform lifetime. Hence, the real-time applications requiring AI services can implement these algorithms in NoC-based architectures with better real-time performance. In this article, we propose a multilevel mapping of deep learning AI applications on the NoC architectures and show its results for the energy consumption, task distribution profile, latency, and throughput. The simulation is conducted using the OCTAVE, and the simulation results show that the performance of the proposed mapping technique is better than the direct mapping techniques.
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