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  1. Article: In-silico

    Al Mamun Khan, Md Abdullah / Ahsan, Asif / Khan, Md Arif / Sanjana, Jannatul Maowa / Biswas, Suvro / Saleh, Md Abu / Gupta, Dipali Rani / Hoque, M Nazmul / Sakif, Tahsin Islam / Rahman, Md Masuder / Islam, Tofazzal

    Heliyon

    2023  Volume 9, Issue 4, Page(s) e15113

    Abstract: Magnaporthe ... ...

    Abstract Magnaporthe oryzae
    Language English
    Publishing date 2023-04-10
    Publishing country England
    Document type Journal Article
    ZDB-ID 2835763-2
    ISSN 2405-8440
    ISSN 2405-8440
    DOI 10.1016/j.heliyon.2023.e15113
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Screening out molecular pathways and prognostic biomarkers of ultraviolet-mediated melanoma through computational techniques.

    Hossain, Md Arju / Ahsan, Asif / Hasan, Md Imran / Sohel, Md / Khan, Md Arif / Somadder, Pratul Dipta / Monjur, Sumaiya / Miah, Md Sipon / Kibria, K M Kaderi / Ahmed, Kawsar / Rahman, Md Habibur

    The International journal of biological markers

    2024  , Page(s) 3936155241230968

    Abstract: Purpose: Ultraviolet radiation causes skin cancer, but the exact mechanism by which it occurs and the most effective methods of intervention to prevent it are yet unknown. For this purpose, our study will use bioinformatics and systems biology ... ...

    Abstract Purpose: Ultraviolet radiation causes skin cancer, but the exact mechanism by which it occurs and the most effective methods of intervention to prevent it are yet unknown. For this purpose, our study will use bioinformatics and systems biology approaches to discover potential biomarkers of skin cancer for early diagnosis and prevention of disease with applicable clinical treatments.
    Methods: This study compared gene expression and protein levels in ultraviolet-mediated cultured keratinocytes and adjacent normal skin tissue using RNA sequencing data from the National Center for Biotechnology Information-Gene Expression Omnibus (NCBI-GEO) database. Then, pathway analysis was employed with a selection of hub genes from the protein-protein interaction (PPI) network and the survival and expression profiles. Finally, potential clinical biomarkers were validated by receiver operating characteristic (ROC) curve analysis.
    Results: We identified 32 shared differentially expressed genes (DEGs) by analyzing three different subsets of the GSE85443 dataset. Skin cancer development is related to the control of several DEGs through cyclin-dependent protein serine/threonine kinase activity, cell cycle regulation, and activation of the NIMA kinase pathways. The cytoHubba plugin in Cytoscape identified 12 hub genes from PPI; among these 3 DEGs, namely,
    Conclusions: Further translational research, including clinical experiments, teratogenicity tests, and in-vitro or in-vivo studies, will be performed to evaluate the expression of these identified biomarkers regarding the prognosis of skin cancer patients.
    Language English
    Publishing date 2024-02-26
    Publishing country United States
    Document type Journal Article
    ZDB-ID 645113-5
    ISSN 1724-6008 ; 0393-6155
    ISSN (online) 1724-6008
    ISSN 0393-6155
    DOI 10.1177/03936155241230968
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Publisher Correction: Robustification of GWAS to explore effective SNPs addressing the challenges of hidden population stratification and polygenic effects.

    Akond, Zobaer / Ahsan, Md Asif / Alam, Munirul / Mollah, Md Nurul Haque

    Scientific reports

    2021  Volume 11, Issue 1, Page(s) 22306

    Language English
    Publishing date 2021-11-10
    Publishing country England
    Document type Published Erratum
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-021-00399-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections.

    Sarker, Bandhan / Rahaman, Md Matiur / Islam, Md Ariful / Alamin, Muhammad Habibulla / Husain, Md Maidul / Ferdousi, Farzana / Ahsan, Md Asif / Mollah, Md Nurul Haque

    PloS one

    2023  Volume 18, Issue 3, Page(s) e0281981

    Abstract: ... receptors (AURKA, AURKB, OAS1) by using 100 ns MD-based MM-PBSA simulations and observed their stable ...

    Abstract The pandemic of COVID-19 is a severe threat to human life and the global economy. Despite the success of vaccination efforts in reducing the spread of the virus, the situation remains largely uncontrolled due to the random mutation in the RNA sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which demands different variants of effective drugs. Disease-causing gene-mediated proteins are usually used as receptors to explore effective drug molecules. In this study, we analyzed two different RNA-Seq and one microarray gene expression profile datasets by integrating EdgeR, LIMMA, weighted gene co-expression network and robust rank aggregation approaches, which revealed SARS-CoV-2 infection causing eight hub-genes (HubGs) including HubGs; REL, AURKA, AURKB, FBXL3, OAS1, STAT4, MMP2 and IL6 as the host genomic biomarkers. Gene Ontology and pathway enrichment analyses of HubGs significantly enriched some crucial biological processes, molecular functions, cellular components and signaling pathways that are associated with the mechanisms of SARS-CoV-2 infections. Regulatory network analysis identified top-ranked 5 TFs (SRF, PBX1, MEIS1, ESR1 and MYC) and 5 miRNAs (hsa-miR-106b-5p, hsa-miR-20b-5p, hsa-miR-93-5p, hsa-miR-106a-5p and hsa-miR-20a-5p) as the key transcriptional and post-transcriptional regulators of HubGs. Then, we conducted a molecular docking analysis to determine potential drug candidates that could interact with HubGs-mediated receptors. This analysis resulted in the identification of top-ranked ten drug agents, including Nilotinib, Tegobuvir, Digoxin, Proscillaridin, Olysio, Simeprevir, Hesperidin, Oleanolic Acid, Naltrindole and Danoprevir. Finally, we investigated the binding stability of the top-ranked three drug molecules Nilotinib, Tegobuvir and Proscillaridin with the three top-ranked proposed receptors (AURKA, AURKB, OAS1) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore, the findings of this study might be useful resources for diagnosis and therapies of SARS-CoV-2 infections.
    MeSH term(s) Humans ; COVID-19/diagnosis ; COVID-19/genetics ; Transcriptome ; SARS-CoV-2/genetics ; SARS-CoV-2/metabolism ; Molecular Docking Simulation ; Aurora Kinase A/genetics ; Proscillaridin ; MicroRNAs/genetics ; Gene Regulatory Networks ; Biomarkers ; Genomics ; COVID-19 Testing
    Chemical Substances Aurora Kinase A (EC 2.7.11.1) ; Proscillaridin (KC6BL281EN) ; MicroRNAs ; Biomarkers
    Language English
    Publishing date 2023-03-13
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0281981
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Comprehensive

    Akond, Zobaer / Rahman, Hafizur / Ahsan, Md Asif / Mosharaf, Md Parvez / Alam, Munirul / Mollah, Md Nurul Haque

    BioMed research international

    2022  Volume 2022, Page(s) 4955209

    Abstract: Dicer-like (DCL), Argonaute (AGO), and RNA-dependent RNA polymerase (RDR) are known as the three major gene families that act as the critical components of RNA interference or silencing mechanisms through the noncoding small RNA molecules (miRNA and ... ...

    Abstract Dicer-like (DCL), Argonaute (AGO), and RNA-dependent RNA polymerase (RDR) are known as the three major gene families that act as the critical components of RNA interference or silencing mechanisms through the noncoding small RNA molecules (miRNA and siRNA) to regulate the expressions of protein-coding genes in eukaryotic organisms. However, most of their characteristics including structures, chromosomal location, subcellular locations, regulatory elements, and gene networking were not rigorously studied. Our analysis identified 7
    MeSH term(s) Case-Control Studies ; Gene Expression Regulation, Plant/genetics ; Genes, Plant/genetics ; Hormones ; MicroRNAs ; Phylogeny ; Plant Proteins/genetics ; Plant Proteins/metabolism ; RNA Interference ; RNA, Small Interfering ; RNA-Dependent RNA Polymerase/genetics ; Stress, Physiological ; Triticum/genetics ; Triticum/metabolism
    Chemical Substances Hormones ; MicroRNAs ; Plant Proteins ; RNA, Small Interfering ; RNA-Dependent RNA Polymerase (EC 2.7.7.48)
    Language English
    Publishing date 2022-09-19
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2698540-8
    ISSN 2314-6141 ; 2314-6133
    ISSN (online) 2314-6141
    ISSN 2314-6133
    DOI 10.1155/2022/4955209
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Virus-induced host cell metabolic alteration.

    Bappy, Syed Shahariar / Haque Asim, Md Muzammal / Ahasan, Mohammad Mainul / Ahsan, Asif / Sultana, Sorna / Khanam, Roksana / Shibly, Abu Zaffar / Kabir, Yearul

    Reviews in medical virology

    2024  Volume 34, Issue 1, Page(s) e2505

    Abstract: Viruses change the host cell metabolism to produce infectious particles and create optimal conditions for replication and reproduction. Numerous host cell pathways have been modified to ensure available biomolecules and sufficient energy. Metabolomics ... ...

    Abstract Viruses change the host cell metabolism to produce infectious particles and create optimal conditions for replication and reproduction. Numerous host cell pathways have been modified to ensure available biomolecules and sufficient energy. Metabolomics studies conducted over the past decade have revealed that eukaryotic viruses alter the metabolism of their host cells on a large scale. Modifying pathways like glycolysis, fatty acid synthesis and glutaminolysis could provide potential energy for virus multiplication. Thus, almost every virus has a unique metabolic signature and a different relationship between the viral life cycle and the individual metabolic processes. There are enormous research in virus induced metabolic reprogramming of host cells that is being conducted through numerous approaches using different vaccine candidates and antiviral drug substances. This review provides an overview of viral interference to different metabolic pathways and improved monitoring in this area will open up new ways for more effective antiviral therapies and combating virus induced oncogenesis.
    MeSH term(s) Humans ; Viruses ; Metabolic Networks and Pathways ; Glycolysis ; Virus Replication
    Language English
    Publishing date 2024-01-29
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 1086043-5
    ISSN 1099-1654 ; 1052-9276
    ISSN (online) 1099-1654
    ISSN 1052-9276
    DOI 10.1002/rmv.2505
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Publisher Correction

    Zobaer Akond / Md. Asif Ahsan / Munirul Alam / Md. Nurul Haque Mollah

    Scientific Reports, Vol 11, Iss 1, Pp 1-

    Robustification of GWAS to explore effective SNPs addressing the challenges of hidden population stratification and polygenic effects

    2021  Volume 4

    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2021-11-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Robustification of GWAS to explore effective SNPs addressing the challenges of hidden population stratification and polygenic effects.

    Akond, Zobaer / Ahsan, Md Asif / Alam, Munirul / Mollah, Md Nurul Haque

    Scientific reports

    2021  Volume 11, Issue 1, Page(s) 13060

    Abstract: Genome-wide association studies (GWAS) play a vital role in identifying important genes those is associated with the phenotypic variations of living organisms. There are several statistical methods for GWAS including the linear mixed model (LMM) which is ...

    Abstract Genome-wide association studies (GWAS) play a vital role in identifying important genes those is associated with the phenotypic variations of living organisms. There are several statistical methods for GWAS including the linear mixed model (LMM) which is popular for addressing the challenges of hidden population stratification and polygenic effects. However, most of these methods including LMM are sensitive to phenotypic outliers that may lead the misleading results. To overcome this problem, in this paper, we proposed a way to robustify the LMM approach for reducing the influence of outlying observations using the β-divergence method. The performance of the proposed method was investigated using both synthetic and real data analysis. Simulation results showed that the proposed method performs better than both linear regression model (LRM) and LMM approaches in terms of powers and false discovery rates in presence of phenotypic outliers. On the other hand, the proposed method performed almost similar to LMM approach but much better than LRM approach in absence of outliers. In the case of real data analysis, our proposed method identified 11 SNPs that are significantly associated with the rice flowering time. Among the identified candidate SNPs, some were involved in seed development and flowering time pathways, and some were connected with flower and other developmental processes. These identified candidate SNPs could assist rice breeding programs effectively. Thus, our findings highlighted the importance of robust GWAS in identifying candidate genes.
    MeSH term(s) Computer Simulation ; Flowers/genetics ; Flowers/physiology ; Gene Expression Profiling ; Gene Expression Regulation, Plant ; Gene Ontology ; Genes, Plant ; Genome-Wide Association Study ; Genotype ; Humans ; Linear Models ; Multifactorial Inheritance/genetics ; Oryza/genetics ; Oryza/physiology ; Phenotype ; Polymorphism, Single Nucleotide/genetics
    Language English
    Publishing date 2021-06-22
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-021-90774-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections.

    Bandhan Sarker / Md Matiur Rahaman / Md Ariful Islam / Muhammad Habibulla Alamin / Md Maidul Husain / Farzana Ferdousi / Md Asif Ahsan / Md Nurul Haque Mollah

    PLoS ONE, Vol 18, Iss 3, p e

    2023  Volume 0281981

    Abstract: ... receptors (AURKA, AURKB, OAS1) by using 100 ns MD-based MM-PBSA simulations and observed their stable ...

    Abstract The pandemic of COVID-19 is a severe threat to human life and the global economy. Despite the success of vaccination efforts in reducing the spread of the virus, the situation remains largely uncontrolled due to the random mutation in the RNA sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which demands different variants of effective drugs. Disease-causing gene-mediated proteins are usually used as receptors to explore effective drug molecules. In this study, we analyzed two different RNA-Seq and one microarray gene expression profile datasets by integrating EdgeR, LIMMA, weighted gene co-expression network and robust rank aggregation approaches, which revealed SARS-CoV-2 infection causing eight hub-genes (HubGs) including HubGs; REL, AURKA, AURKB, FBXL3, OAS1, STAT4, MMP2 and IL6 as the host genomic biomarkers. Gene Ontology and pathway enrichment analyses of HubGs significantly enriched some crucial biological processes, molecular functions, cellular components and signaling pathways that are associated with the mechanisms of SARS-CoV-2 infections. Regulatory network analysis identified top-ranked 5 TFs (SRF, PBX1, MEIS1, ESR1 and MYC) and 5 miRNAs (hsa-miR-106b-5p, hsa-miR-20b-5p, hsa-miR-93-5p, hsa-miR-106a-5p and hsa-miR-20a-5p) as the key transcriptional and post-transcriptional regulators of HubGs. Then, we conducted a molecular docking analysis to determine potential drug candidates that could interact with HubGs-mediated receptors. This analysis resulted in the identification of top-ranked ten drug agents, including Nilotinib, Tegobuvir, Digoxin, Proscillaridin, Olysio, Simeprevir, Hesperidin, Oleanolic Acid, Naltrindole and Danoprevir. Finally, we investigated the binding stability of the top-ranked three drug molecules Nilotinib, Tegobuvir and Proscillaridin with the three top-ranked proposed receptors (AURKA, AURKB, OAS1) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore, the findings of this study might be useful resources for ...
    Keywords Medicine ; R ; Science ; Q
    Subject code 572
    Language English
    Publishing date 2023-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Data-mining Techniques for Image-based Plant Phenotypic Traits Identification and Classification.

    Rahaman, Md Matiur / Ahsan, Md Asif / Chen, Ming

    Scientific reports

    2019  Volume 9, Issue 1, Page(s) 19526

    Abstract: Statistical data-mining (DM) and machine learning (ML) are promising tools to assist in the analysis of complex dataset. In recent decades, in the precision of agricultural development, plant phenomics study is crucial for high-throughput phenotyping of ... ...

    Abstract Statistical data-mining (DM) and machine learning (ML) are promising tools to assist in the analysis of complex dataset. In recent decades, in the precision of agricultural development, plant phenomics study is crucial for high-throughput phenotyping of local crop cultivars. Therefore, integrated or a new analytical approach is needed to deal with these phenomics data. We proposed a statistical framework for the analysis of phenomics data by integrating DM and ML methods. The most popular supervised ML methods; Linear Discriminant Analysis (LDA), Random Forest (RF), Support Vector Machine with linear (SVM-l) and radial basis (SVM-r) kernel are used for classification/prediction plant status (stress/non-stress) to validate our proposed approach. Several simulated and real plant phenotype datasets were analyzed. The results described the significant contribution of the features (selected by our proposed approach) throughout the analysis. In this study, we showed that the proposed approach removed phenotype data analysis complexity, reduced computational time of ML algorithms, and increased prediction accuracy.
    MeSH term(s) Algorithms ; Data Mining ; Discriminant Analysis ; Machine Learning ; Support Vector Machine
    Language English
    Publishing date 2019-12-20
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-019-55609-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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