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  1. Article: Computational-based drug repurposing methods in COVID-19.

    Masoudi-Sobhanzadeh, Yosef

    BioImpacts : BI

    2020  Volume 10, Issue 3, Page(s) 205–206

    Abstract: COVID-19, as a newly emerging disease, has disrupted human's different activities. Hence, it is essential to develop drugs or vaccines in order to control COVID-19. Since there is not a medication or vaccine for treating the disease and drug development ... ...

    Abstract COVID-19, as a newly emerging disease, has disrupted human's different activities. Hence, it is essential to develop drugs or vaccines in order to control COVID-19. Since there is not a medication or vaccine for treating the disease and drug development project is a time and cost consuming process, drug repurposing approaches may yield to proper curing plans. However, there are some limitations in this field, which make the process a challenging one. This letter aims to introduce drug repurposing methods and the existing challenges to detect candidate drugs which may be helpful in controlling COVID-19.
    Keywords covid19
    Language English
    Publishing date 2020-06-18
    Publishing country Iran
    Document type Journal Article
    ZDB-ID 2604624-6
    ISSN 2228-5660 ; 2228-5652
    ISSN (online) 2228-5660
    ISSN 2228-5652
    DOI 10.34172/bi.2020.25
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: A voting-based machine learning approach for classifying biological and clinical datasets.

    Daneshvar, Negar Hossein-Nezhad / Masoudi-Sobhanzadeh, Yosef / Omidi, Yadollah

    BMC bioinformatics

    2023  Volume 24, Issue 1, Page(s) 140

    Abstract: Background: Different machine learning techniques have been proposed to classify a wide range of biological/clinical data. Given the practicability of these approaches accordingly, various software packages have been also designed and developed. However, ...

    Abstract Background: Different machine learning techniques have been proposed to classify a wide range of biological/clinical data. Given the practicability of these approaches accordingly, various software packages have been also designed and developed. However, the existing methods suffer from several limitations such as overfitting on a specific dataset, ignoring the feature selection concept in the preprocessing step, and losing their performance on large-size datasets. To tackle the mentioned restrictions, in this study, we introduced a machine learning framework consisting of two main steps. First, our previously suggested optimization algorithm (Trader) was extended to select a near-optimal subset of features/genes. Second, a voting-based framework was proposed to classify the biological/clinical data with high accuracy. To evaluate the efficiency of the proposed method, it was applied to 13 biological/clinical datasets, and the outcomes were comprehensively compared with the prior methods.
    Results: The results demonstrated that the Trader algorithm could select a near-optimal subset of features with a significant level of p-value < 0.01 relative to the compared algorithms. Additionally, on the large-sie datasets, the proposed machine learning framework improved prior studies by ~ 10% in terms of the mean values associated with fivefold cross-validation of accuracy, precision, recall, specificity, and F-measure.
    Conclusion: Based on the obtained results, it can be concluded that a proper configuration of efficient algorithms and methods can increase the prediction power of machine learning approaches and help researchers in designing practical diagnosis health care systems and offering effective treatment plans.
    MeSH term(s) Algorithms ; Machine Learning
    Language English
    Publishing date 2023-04-11
    Publishing country England
    Document type Journal Article
    ZDB-ID 2041484-5
    ISSN 1471-2105 ; 1471-2105
    ISSN (online) 1471-2105
    ISSN 1471-2105
    DOI 10.1186/s12859-023-05274-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: A fuzzy logic-based computational method for the repurposing of drugs against COVID-19.

    Masoudi-Sobhanzadeh, Yosef / Esmaeili, Hosein / Masoudi-Nejad, Ali

    BioImpacts : BI

    2021  Volume 12, Issue 4, Page(s) 315–324

    Abstract: Introduction: ...

    Abstract Introduction:
    Language English
    Publishing date 2021-08-10
    Publishing country Iran
    Document type Journal Article
    ZDB-ID 2604624-6
    ISSN 2228-5660 ; 2228-5652
    ISSN (online) 2228-5660
    ISSN 2228-5652
    DOI 10.34172/bi.2021.40
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Computational-based drug repurposing methods in COVID-19

    Yosef Masoudi-Sobhanzadeh

    BioImpacts, Vol 10, Iss 3, Pp 205-

    2020  Volume 206

    Abstract: COVID-19, as a newly emerging disease, has disrupted human’s different activities. Hence, it is essential to develop drugs or vaccines in order to control COVID-19. Since there is not a medication or vaccine for treating the disease and drug development ... ...

    Abstract COVID-19, as a newly emerging disease, has disrupted human’s different activities. Hence, it is essential to develop drugs or vaccines in order to control COVID-19. Since there is not a medication or vaccine for treating the disease and drug development project is a time and cost consuming process, drug repurposing approaches may yield to proper curing plans. However, there are some limitations in this field, which make the process a challenging one. This letter aims to introduce drug repurposing methods and the existing challenges to detect candidate drugs which may be helpful in controlling COVID-19.
    Keywords covid-19 ; drug repositioning ; in silico drug discovery ; Medicine (General) ; R5-920 ; Biology (General) ; QH301-705.5
    Language English
    Publishing date 2020-07-01T00:00:00Z
    Publisher Tabriz University of Medical Sciences
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  5. Article: Computational-based drug repurposing methods in COVID-19

    Masoudi-Sobhanzadeh, Yosef

    BioImpacts

    Abstract: COVID-19, as a newly emerging disease, has disrupted human’s different activities. Hence, it is essential to develop drugs or vaccines in order to control COVID-19. Since there is not a medication or vaccine for treating the disease and drug development ... ...

    Abstract COVID-19, as a newly emerging disease, has disrupted human’s different activities. Hence, it is essential to develop drugs or vaccines in order to control COVID-19. Since there is not a medication or vaccine for treating the disease and drug development project is a time and cost consuming process, drug repurposing approaches may yield to proper curing plans. However, there are some limitations in this field, which make the process a challenging one. This letter aims to introduce drug repurposing methods and the existing challenges to detect candidate drugs which may be helpful in controlling COVID-19.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #688945
    Database COVID19

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  6. Article ; Online: Computational-based drug repurposing methods in COVID-19

    Masoudi-Sobhanzadeh, Yosef

    BioImpacts

    2020  Volume 10, Issue 3, Page(s) 205–206

    Abstract: COVID-19, as a newly emerging disease, has disrupted human’s different activities. Hence, it is essential to develop drugs or vaccines in order to control COVID-19. Since there is not a medication or vaccine for treating the disease and drug development ... ...

    Abstract COVID-19, as a newly emerging disease, has disrupted human’s different activities. Hence, it is essential to develop drugs or vaccines in order to control COVID-19. Since there is not a medication or vaccine for treating the disease and drug development project is a time and cost consuming process, drug repurposing approaches may yield to proper curing plans. However, there are some limitations in this field, which make the process a challenging one. This letter aims to introduce drug repurposing methods and the existing challenges to detect candidate drugs which may be helpful in controlling COVID-19.
    Keywords General Biochemistry, Genetics and Molecular Biology ; Pharmaceutical Science ; General Medicine ; covid19
    Language English
    Publisher Maad Rayan Publishing Company
    Publishing country de
    Document type Article ; Online
    ZDB-ID 2604624-6
    ISSN 2228-5660 ; 2228-5652
    ISSN (online) 2228-5660
    ISSN 2228-5652
    DOI 10.34172/bi.2020.25
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Tracing drugs from discovery to disposal.

    Omidian, Hossein / Razmara, Jafar / Parvizpour, Sepideh / Tabrizchi, Hamed / Masoudi-Sobhanzadeh, Yosef / Omidi, Yadollah

    Drug discovery today

    2023  Volume 28, Issue 5, Page(s) 103538

    Abstract: The life cycle of a drug begins with discovery and ends with its disposal. Drug discovery companies, drug manufacturers, regulatory agencies, suppliers, pharmacies, patients, healthcare providers, and many more are involved in this process. Transparency, ...

    Abstract The life cycle of a drug begins with discovery and ends with its disposal. Drug discovery companies, drug manufacturers, regulatory agencies, suppliers, pharmacies, patients, healthcare providers, and many more are involved in this process. Transparency, traceability, automation, and data security are some of the most crucial factors affecting how effectively and safely the transactions are conducted across all parties involved in the cycle. By contrast, scalability, energy consumption, regulation, standards, and complexity hamper the adoption of new technology that is expected to fulfil these requirements. Here, we highlight how blockchain technology can track, accelerate, and boost the efficiency of incredibly complicated operations, such as pharmaceutical development.
    MeSH term(s) Humans ; Blockchain ; Technology ; Automation
    Language English
    Publishing date 2023-02-23
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 1324988-5
    ISSN 1878-5832 ; 1359-6446
    ISSN (online) 1878-5832
    ISSN 1359-6446
    DOI 10.1016/j.drudis.2023.103538
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4725: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  8. Article ; Online: A fuzzy logic-based computational method for the repurposing of drugs against COVID-19

    Yosef Masoudi-Sobhanzadeh / Hosein Esmaeili / Ali Masoudi-Nejad

    BioImpacts, Vol 12, Iss 4, Pp 315-

    2022  Volume 324

    Abstract: Introduction: COVID-19 has spread out all around the world and seriously interrupted human activities. Being a newfound disease, not only many aspects of the disease are unknown, but also there is not an effective medication to cure the disease. Besides, ...

    Abstract Introduction: COVID-19 has spread out all around the world and seriously interrupted human activities. Being a newfound disease, not only many aspects of the disease are unknown, but also there is not an effective medication to cure the disease. Besides, designing a drug is a time-consuming process and needs large investment. Hence, drug repurposing techniques, employed to discover the hidden benefits of the existing drugs, maybe a useful option for treating COVID-19. Methods: The present study exploits the drug repositioning concepts and introduces some candidate drugs which may be effective in controlling COVID-19. The suggested method consists of three main steps. First, the required data such as the amino acid sequences of targets and drug-target interactions are extracted from the public databases. Second, the similarity score between the targets (protein/enzymes) and genome of SARS-COV-2 is computed using the proposed fuzzy logic-based method. Since the classical approaches yield outcomes which may not be useful for the real-world applications, the fuzzy technique can address the issue. Third, after ranking targets based on the obtained scores, the usefulness of drugs affecting them is examined for managing COVID-19. Results: The results indicate that antiviral medicines, designed for curing hepatitis C, may also cure COVID-19. According to the findings, ribavirin, simeprevir, danoprevir, and XTL-6865 may be helpful in controlling the disease. Conclusion: It can be concluded that the similarity-based drug repurposing techniques may be the most suitable option for managing emerging diseases such as COVID-19 and can be applied to a wide range of data. Also, fuzzy logic-based scoring methods can produce outcomes which are more consistent with the real-world biological applications than others.
    Keywords computational method ; covid-19 ; drug repurposing ; fuzzy logic ; hepatitis ; Medicine (General) ; R5-920 ; Biology (General) ; QH301-705.5
    Subject code 006
    Language English
    Publishing date 2022-07-01T00:00:00Z
    Publisher Tabriz University of Medical Sciences
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Discovering driver nodes in chronic kidney disease-related networks using Trader as a newly developed algorithm.

    Masoudi-Sobhanzadeh, Yosef / Gholaminejad, Alieh / Gheisari, Yousof / Roointan, Amir

    Computers in biology and medicine

    2022  Volume 148, Page(s) 105892

    Abstract: Thanks to the advances in the field of computational-based biology, a huge volume of disease-related data has been generated so far. From the existing data, the disease-related protein-protein interaction (PPI) networks seem to yield effective treatment ... ...

    Abstract Thanks to the advances in the field of computational-based biology, a huge volume of disease-related data has been generated so far. From the existing data, the disease-related protein-protein interaction (PPI) networks seem to yield effective treatment plans due to the informative/systematic representation of diseases. Yet, a large number of previous studies have failed due to the complex nature of such disease-related networks. For addressing this limitation, in the present study, we combined Trader and the DFS algorithms to identify a minimal subset of nodes (driver nodes) whose removal produces a maximum number of disjoint sub-networks. We then screened the nodes in the disease-associated PPI networks and to evaluate the efficiency of the suggested method, it was applied to six PPI networks of differentially expressed genes in chronic kidney diseases. The performance of Trader was superior to other well-known algorithms in terms of identifying driver nodes. Besides, the proportion of proteins that were targeted by at least one FDA-approved drug was significantly higher among the identified driver nodes when compared with the rest of the proteins in the networks. The proposed algorithm could be applied for predicting future therapeutic targets in complex disorder networks. In conclusion, unlike the common methods, computationally efficient algorithms can generate more practical outcomes which are compatible with real-world biological facts.
    MeSH term(s) Algorithms ; Computational Biology ; Humans ; Protein Interaction Mapping ; Protein Interaction Maps ; Proteins ; Renal Insufficiency, Chronic
    Chemical Substances Proteins
    Language English
    Publishing date 2022-07-30
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2022.105892
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 653: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  10. Article ; Online: A voting-based machine learning approach for classifying biological and clinical datasets

    Negar Hossein-Nezhad Daneshvar / Yosef Masoudi-Sobhanzadeh / Yadollah Omidi

    BMC Bioinformatics, Vol 24, Iss 1, Pp 1-

    2023  Volume 17

    Abstract: Abstract Background Different machine learning techniques have been proposed to classify a wide range of biological/clinical data. Given the practicability of these approaches accordingly, various software packages have been also designed and developed. ... ...

    Abstract Abstract Background Different machine learning techniques have been proposed to classify a wide range of biological/clinical data. Given the practicability of these approaches accordingly, various software packages have been also designed and developed. However, the existing methods suffer from several limitations such as overfitting on a specific dataset, ignoring the feature selection concept in the preprocessing step, and losing their performance on large-size datasets. To tackle the mentioned restrictions, in this study, we introduced a machine learning framework consisting of two main steps. First, our previously suggested optimization algorithm (Trader) was extended to select a near-optimal subset of features/genes. Second, a voting-based framework was proposed to classify the biological/clinical data with high accuracy. To evaluate the efficiency of the proposed method, it was applied to 13 biological/clinical datasets, and the outcomes were comprehensively compared with the prior methods. Results The results demonstrated that the Trader algorithm could select a near-optimal subset of features with a significant level of p-value < 0.01 relative to the compared algorithms. Additionally, on the large-sie datasets, the proposed machine learning framework improved prior studies by ~ 10% in terms of the mean values associated with fivefold cross-validation of accuracy, precision, recall, specificity, and F-measure. Conclusion Based on the obtained results, it can be concluded that a proper configuration of efficient algorithms and methods can increase the prediction power of machine learning approaches and help researchers in designing practical diagnosis health care systems and offering effective treatment plans.
    Keywords Clinical datasets ; Feature selection ; Gene selection ; Machine learning ; Optimization algorithm ; Voting-based approach ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
    Subject code 006
    Language English
    Publishing date 2023-04-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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