A Novel Protein Mapping Method for Predicting the Protein Interactions in COVID-19 Disease by Deep Learning

Alakuş, Talha Burak; Türkoğlu, İ̇brahim

doi:10.1007/s12539-020-00405-4

Cited by 20 publications

(16 citation statements)

References 47 publications

(65 reference statements)

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“…Second, we will consider different computational models (Gaur and Chaturvedi, 2019;Liu et al, 2019;Gutiérrez-Cárdenas and Wang, 2021), for example, matrix decomposition (Chen et al, 2018a), bidirectional label propagation (Wang et al, 2019), network distance analysis (Zhang et al, 2021), internal confidence-based collaborative filtering recommendation (Wang et al, 2020b), sparse subspace learning with Laplacian regularization to search possible associations between viruses and drugs. Third, we will try to use deep learning methods to predict drugs for COVID-19 (Wang et al, 2017;Alakus and Turkoglu, 2021;Kang et al, 2021). Finally, we will also investigate the relationship between antimicrobial compounds and COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Screening Potential Drugs for COVID-19 Based on Bound Nuclear Norm Regularization

Wang

Shen

et al. 2021

Front. Genet.

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The novel coronavirus pneumonia COVID-19 infected by SARS-CoV-2 has attracted worldwide attention. It is urgent to find effective therapeutic strategies for stopping COVID-19. In this study, a Bounded Nuclear Norm Regularization (BNNR) method is developed to predict anti-SARS-CoV-2 drug candidates. First, three virus-drug association datasets are compiled. Second, a heterogeneous virus-drug network is constructed. Third, complete genomic sequences and Gaussian association profiles are integrated to compute virus similarities; chemical structures and Gaussian association profiles are integrated to calculate drug similarities. Fourth, a BNNR model based on kernel similarity (VDA-GBNNR) is proposed to predict possible anti-SARS-CoV-2 drugs. VDA-GBNNR is compared with four existing advanced methods under fivefold cross-validation. The results show that VDA-GBNNR computes better AUCs of 0.8965, 0.8562, and 0.8803 on the three datasets, respectively. There are 6 anti-SARS-CoV-2 drugs overlapping in any two datasets, that is, remdesivir, favipiravir, ribavirin, mycophenolic acid, niclosamide, and mizoribine. Molecular dockings are conducted for the 6 small molecules and the junction of SARS-CoV-2 spike protein and human angiotensin-converting enzyme 2. In particular, niclosamide and mizoribine show higher binding energy of −8.06 and −7.06 kcal/mol with the junction, respectively. G496 and K353 may be potential key residues between anti-SARS-CoV-2 drugs and the interface junction. We hope that the predicted results can contribute to the treatment of COVID-19.

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Section: Discussionmentioning

confidence: 99%

Screening Potential Drugs for COVID-19 Based on Bound Nuclear Norm Regularization

Wang

Shen

et al. 2021

Front. Genet.

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“…We look at five different approaches in this section. So, Alakus and Turkoglu [ 67 ] suggested a protein-mapping approach for predicting the interactions of COVID-19 non-structural proteins. The interactions were discovered using a bidirectional RNN model.…”

Section: Covid-19 Detection Mechanismsmentioning

confidence: 99%

“… Dataset Using TL? Method Usage Alakus and Turkoglu [ 67 ] Normalizing and classifying the mapped protein sequences using the DeepBiRNN model. −97.76% accuracy, 97.60% precision, 98.33% recall, 79.42% f1-score, and an overall AUC of 89%.…”

Section: Covid-19 Detection Mechanismsmentioning

confidence: 99%

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

Heidari¹,

Navimipour²,

Ünal³

et al. 2022

Computers in Biology and Medicine

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Since December 2019, the COVID-19 outbreak has resulted in countless deaths and has harmed all facets of human existence. COVID-19 has been designated an epidemic by the World Health Organization (WHO), which has placed a tremendous burden on nearly all countries, especially those with weak health systems. However, Deep Learning (DL) has been applied in several applications and many types of detection applications in the medical field, including thyroid diagnosis, lung nodule recognition, fetal localization, and detection of diabetic retinopathy. Furthermore, various clinical imaging sources, like Magnetic Resonance Imaging (MRI), X-ray, and Computed Tomography (CT), make DL a perfect technique to tackle the epidemic of COVID-19. Inspired by this fact, a considerable amount of research has been done. A Systematic Literature Review (SLR) has been used in this study to discover, assess, and integrate findings from relevant studies. DL techniques used in COVID-19 have also been categorized into seven main distinct categories as Long Short Term Memory Networks (LSTM), Self-Organizing Maps (SOMs), Conventional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Autoencoders, and hybrid approaches. Then, the state-of-the-art studies connected to DL techniques and applications for health problems with COVID-19 have been highlighted. Moreover, many issues and problems associated with DL implementation for COVID-19 have been addressed, which are anticipated to stimulate more investigations to control the prevalence and disaster control in the future. According to the findings, most papers are assessed using characteristics such as accuracy, delay, robustness, and scalability. Meanwhile, other features are underutilized, such as security and convergence time. Python is also the most commonly used language in papers, accounting for 75% of the time. According to the investigation, 37.83% of applications have identified chest CT/chest X-ray images for patients.

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“…Knowledge of virus evolution and transmission is essential during the pandemic to develop appropriate intervention strategies for the virus spreading control [63]. Several previous studies related to the SARS-CoV-2 genome signature evolution were: 1) identification of differences and similarities of viral variants based on genome sequence analysis [64]; 2) identification of protein interactions [65] and determination of the functions and pathways of proteins in biological processes [66]; 3) evaluation of viral mutation based on the protein sequence [67]; and 4) prediction of the SARS-CoV-2 mutation infectivity [68].…”

Section: Topic Hotspotsmentioning

confidence: 99%

Machine learning in handling disease outbreaks: a comprehensive review

Riswantini

Nugraheni

2022

Bulletin EEI

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The changes in the global environment have made impact on the evolution of infectious diseases, virus mutations, or new diseases which are challenging to be tackled with new technological advances. This work aims to identify and analyze previous studies on machine learning applications in handling disease outbreaks. Bibliometric analysis was conducted on 3,447 scientific articles selected from the Scopus database. Further, latent dirichlet analysis (LDA) method was applied to identify the topic hotspots in attempting to deepen the analysis. The LDA results identified twelve topic hotspots that can be classified into three themes: COVID-19 disease, miscellaneous diseases, and public opinion on disease outbreaks for discussion. The study reveals that the scientific structure of this domain is dominated by machine learning research on COVID-19 diseases and miscellaneous diseases caused by pathogens or some genetic factors. A huge amount of multimodal medical data was used by previous studies for prediction, forecasting, classification, or screening purposes to resolve many problems of diseases, including epidemiological surveillance, diagnosis, treatment, health monitoring, epidemic management, viral infection, and pathogenesis. Public opinions toward new diseases are also an interesting topic in addition to the public perceptions in response to the health protocol and policies.

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A Novel Protein Mapping Method for Predicting the Protein Interactions in COVID-19 Disease by Deep Learning

Cited by 20 publications

References 47 publications

Screening Potential Drugs for COVID-19 Based on Bound Nuclear Norm Regularization

Screening Potential Drugs for COVID-19 Based on Bound Nuclear Norm Regularization

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

Machine learning in handling disease outbreaks: a comprehensive review

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