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  1. Article ; Online: Multi-omics data integration and drug screening of AML cancer using Generative Adversarial Network.

    Afroz, Sabrin / Islam, Nadira / Habib, Md Ahsan / Reza, Md Selim / Ashad Alam, Md

    Methods (San Diego, Calif.)

    2024  Volume 226, Page(s) 138–150

    Abstract: In the era of precision medicine, accurate disease phenotype prediction for heterogeneous diseases, such as cancer, is emerging due to advanced technologies that link genotypes and phenotypes. However, it is difficult to integrate different types of ... ...

    Abstract In the era of precision medicine, accurate disease phenotype prediction for heterogeneous diseases, such as cancer, is emerging due to advanced technologies that link genotypes and phenotypes. However, it is difficult to integrate different types of biological data because they are so varied. In this study, we focused on predicting the traits of a blood cancer called Acute Myeloid Leukemia (AML) by combining different kinds of biological data. We used a recently developed method called Omics Generative Adversarial Network (GAN) to better classify cancer outcomes. The primary advantages of a GAN include its ability to create synthetic data that is nearly indistinguishable from real data, its high flexibility, and its wide range of applications, including multi-omics data analysis. In addition, the GAN was effective at combining two types of biological data. We created synthetic datasets for gene activity and DNA methylation. Our method was more accurate in predicting disease traits than using the original data alone. The experimental results provided evidence that the creation of synthetic data through interacting multi-omics data analysis using GANs improves the overall prediction quality. Furthermore, we identified the top-ranked significant genes through statistical methods and pinpointed potential candidate drug agents through in-silico studies. The proposed drugs, also supported by other independent studies, might play a crucial role in the treatment of AML cancer. The code is available on GitHub; https://github.com/SabrinAfroz/omicsGAN_codes?fbclid=IwAR1-/stuffmlE0hyWgSu2wlXo6dYlKUei3faLdlvpxTOOUPVlmYCloXf4Uk9ejK4I.
    Language English
    Publishing date 2024-04-24
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1066584-5
    ISSN 1095-9130 ; 1046-2023
    ISSN (online) 1095-9130
    ISSN 1046-2023
    DOI 10.1016/j.ymeth.2024.04.017
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Pakistan's COVID-19 Prevention and Control Response Using the World Health Organization's Guidelines for Epidemic Response Interventions.

    Emmanuel, Faran / Hassan, Anusheh / Ahmad, Ahsan / Reza, Tahira E

    Cureus

    2023  Volume 15, Issue 1, Page(s) e34480

    Abstract: Massive coronavirus disease 2019 (COVID-19) devastation was anticipated in Pakistan due to poor track record of responding to epidemics. However, by adopting effective and timely response measures under strong government leadership, Pakistan averted a ... ...

    Abstract Massive coronavirus disease 2019 (COVID-19) devastation was anticipated in Pakistan due to poor track record of responding to epidemics. However, by adopting effective and timely response measures under strong government leadership, Pakistan averted a significant number of infections. We present the government of Pakistan's efforts to curb the spread of COVID-19, using the World Health Organization's guidelines for epidemic response intervention. The sequence of interventions is presented under the epidemic response stages, namely anticipation, early detection, containment-control, and mitigation. Key factors of Pakistan's response included decisive political leadership and implementation of a coordinated and evidence-informed strategy. Moreover, early control measures, mobilization of front-line health workers for contact tracing, public awareness campaigns, 'smart lockdowns', and massive vaccination drives are key strategies that helped flatten the curve. These interventions and lessons learnt can help countries and regions struggling with COVID-19 to develop successful strategies to flatten the curve and enhance disease response preparedness.
    Language English
    Publishing date 2023-01-31
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 2747273-5
    ISSN 2168-8184
    ISSN 2168-8184
    DOI 10.7759/cureus.34480
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Evaluating arsenic contamination in northwestern Bangladesh: A GIS-Based assessment of groundwater vulnerability and human health impacts.

    Habib, Md Ahsan / Reza, A H M Selim / Hasan, Md Irfanul / Ahsan, Md Aminul / Moniruzzaman, Md / Hasan, Asma Binta / Shofi, Shofiul Islam / Hridoy, Kayesh Mahmud

    Heliyon

    2024  Volume 10, Issue 6, Page(s) e27917

    Abstract: One of the biggest environmental worries in the world today is the risk of arsenic (As) contamination in groundwater. The Atomic Absorption Spectrometer (AAS) was used in this work to assess the As content in groundwater samples from 38 shallow (27 m) ... ...

    Abstract One of the biggest environmental worries in the world today is the risk of arsenic (As) contamination in groundwater. The Atomic Absorption Spectrometer (AAS) was used in this work to assess the As content in groundwater samples from 38 shallow (27 m) tubewells in northwest Bangladesh to determine the existing situation, potential source(s), and likely health risk of As and other important water quality parameters. The range of arsenic concentrations (μgL
    Language English
    Publishing date 2024-03-16
    Publishing country England
    Document type Journal Article
    ZDB-ID 2835763-2
    ISSN 2405-8440
    ISSN 2405-8440
    DOI 10.1016/j.heliyon.2024.e27917
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  4. Article ; Online: Defect-Rich MoSe

    Ye, Fan / Ayub, Ahsan / Karimi, Reza / Wettig, Shawn / Sanderson, Joseph / Musselman, Kevin P

    Advanced materials (Deerfield Beach, Fla.)

    2023  Volume 35, Issue 30, Page(s) e2301129

    Abstract: ... ...

    Abstract MoSe
    Language English
    Publishing date 2023-06-07
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 1474949-X
    ISSN 1521-4095 ; 0935-9648
    ISSN (online) 1521-4095
    ISSN 0935-9648
    DOI 10.1002/adma.202301129
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Classification of imbalanced protein sequences with deep-learning approaches; application on influenza A imbalanced virus classes

    Reza Ahsan / Faezeh Ebrahimi / Mansour Ebrahimi

    Informatics in Medicine Unlocked, Vol 29, Iss , Pp 100860- (2022)

    2022  

    Abstract: Classifiers based on machine learning perform well in the classification of balanced data but struggle with imbalanced data and often merge or ignore the rarer classes, even if the rare classes are more important than other classes. A long-term learning ... ...

    Abstract Classifiers based on machine learning perform well in the classification of balanced data but struggle with imbalanced data and often merge or ignore the rarer classes, even if the rare classes are more important than other classes. A long-term learning dependency, or Long Short-Term Memory (LSTM) architecture, was developed to compare conventional models with LSTM on polynomial and time-matrix datasets to address the imbalanced classes of influenza virus A. The performances of tree induction and K-Nearest Neighborhood models were less than 90%, and they were not accurate in classifying the classes with fewer samples. The proposed LSTM model can predict all classes reached the highest possible figure of 100%. Thus, for the first time, classification of the imbalanced dataset of influenza virus A at the sequential levels is being reported, which paves the road for the analysis of the proteome-based classification of other proteins.
    Keywords LSTM ; Influenza virus A ; Classification ; Imbalanced dataset ; Computer applications to medicine. Medical informatics ; R858-859.7
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Author Correction: Synthesis and characterization of ZrFe

    Saadh, Mohamed J / Khasawneh, Hussam Elddin Nabieh / Ortiz, Geovanny Genaro Reivan / Ahsan, Muhammad / Sain, Dinesh Kumar / Yusuf, Kareem / Sillanää, Mika / Iqbal, Amjad / Akhavan-Sigari, Reza

    Scientific reports

    2024  Volume 14, Issue 1, Page(s) 7839

    Language English
    Publishing date 2024-04-03
    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/s41598-024-58184-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: SVM Communications: Venous Taskforce update and Society announcements.

    Ahsan, Syed T / Esponda, Omar / Li, Wenzhu / Amini, Reza / Shaydakov, Maxim / Wheeler, Jason / Fukaya, Eri

    Vascular medicine (London, England)

    2023  Volume 28, Issue 5, Page(s) 493–495

    MeSH term(s) Humans ; Support Vector Machine ; Veins/diagnostic imaging
    Language English
    Publishing date 2023-09-05
    Publishing country England
    Document type Journal Article
    ZDB-ID 1311628-9
    ISSN 1477-0377 ; 1358-863X
    ISSN (online) 1477-0377
    ISSN 1358-863X
    DOI 10.1177/1358863X231195630
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 4409: Show issues Location:
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  8. Article ; Online: Identification of Heat-Resistant Bacteria Based on Selection of Proper Representation of Protein Sequences Using Deep Learning Approach

    Reza Ahsan / Mansour Ebrahimi

    Majallah-i Dānishgāh-i ̒Ulūm-i Pizishkī-i Qum, Vol 14, Iss 3, Pp 54-

    2020  Volume 63

    Abstract: Background and Objectives: Identification of effective mechanisms in heat-resistance in bacteria is of great importance in some industries, such as food industry, textile manufacturing, and especially in detergent production industries. For this purpose, ...

    Abstract Background and Objectives: Identification of effective mechanisms in heat-resistance in bacteria is of great importance in some industries, such as food industry, textile manufacturing, and especially in detergent production industries. For this purpose, deep learning tools were used to identify the characteristics of heat-resistant bacteria based on protein properties. Methods: Some characteristics of heat-resistant and non-heat-resistant proteins, such as the structural properties of amino acids, the number and the frequency of each amino acid, and their physicochemical properties, were calculated. Bacterial classification was performed in three steps: first, attribute weighting methods were used to select the important variables, then those variables, were selected and finally deep learning networks were employed to extract the hierarchy of the features. Results: The results of 10 weighting methods showed that out of 73 characteristics of the number and frequency of amino acids, only 40 had weights higher than zero. Of these variables, 13 variable gained weight higher than 0.5 and only 10 variables had weight above 0.09. These 10 features were selected as important variables. The frequencies of glutamine and glutamic acid obtained the highest possible weights and were considered as two important features in the classification of heat-resistant and non-heat-resistant bacteria. The highest prediction accuracy of the deep learning networks was 92.42% for the classification of heat resistant bacteria. Conclusion: The deep neural networks can be effectively used to identify heat-resistant bacteria based on their protein properties.
    Keywords thermostable ; protein sequence ; classification ; deep learning networks ; Medicine (General) ; R5-920
    Subject code 612
    Language Persian
    Publishing date 2020-06-01T00:00:00Z
    Publisher Qom University of Medical Sciences
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Image processing techniques represent innovative tools for comparative analysis of proteins.

    Ahsan, Reza / Ebrahimi, Mansour

    Computers in biology and medicine

    2019  Volume 117, Page(s) 103584

    Abstract: Different bioinformatic and data-mining approaches have been used for the analysis of proteins. Here, we describe a novel, robust, and reliable approach for comparative analysis of a large number of proteins by combining Image Processing Techniques and ... ...

    Abstract Different bioinformatic and data-mining approaches have been used for the analysis of proteins. Here, we describe a novel, robust, and reliable approach for comparative analysis of a large number of proteins by combining Image Processing Techniques and Convolutional Deep Neural Network (IPT-CNN). As proof of principle, we used IPT-CNN to predict different subtypes of Influenza A virus (IAV). Over 8000 sequences of surface proteins haemagglutinin (HA) and neuraminidase (NA) from different IAV subtypes were used to create polynomial or binary vector datasets. The datasets were then converted into binary images. Analysis of these images enabled the classification of IAV subtypes with 100% accuracy and, compared to non-image-based approaches, within a shorter time frame. The proteome-based IPT-CNN approach described here may be used for analysis and proteome-based classification of other proteins.
    MeSH term(s) Algorithms ; Image Processing, Computer-Assisted ; Neural Networks, Computer
    Language English
    Publishing date 2019-12-20
    Publishing country United States
    Document type Journal Article
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2019.103584
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Image processing unravels the evolutionary pattern of SARS-CoV-2 against SARS and MERS through position-based pattern recognition.

    Ahsan, Reza / Tahsili, Mohammad Reza / Ebrahimi, Faezeh / Ebrahimie, Esmaeil / Ebrahimi, Mansour

    Computers in biology and medicine

    2021  Volume 134, Page(s) 104471

    Abstract: SARS-COV-2, Severe Acute Respiratory Syndrome (SARS), and the Middle East respiratory syndrome-related coronavirus (MERS) viruses are from the coronaviridae family; the former became a global pandemic (with low mortality rate) while the latter were ... ...

    Abstract SARS-COV-2, Severe Acute Respiratory Syndrome (SARS), and the Middle East respiratory syndrome-related coronavirus (MERS) viruses are from the coronaviridae family; the former became a global pandemic (with low mortality rate) while the latter were confined to a limited region (with high mortality rates). To investigate the possible structural differences at basic levels for the three viruses, genomic and proteomic sequences were downloaded and converted to polynomial datasets. Seven attribute weighting (feature selection) models were employed to find the key differences in their genome's nucleotide sequence. Most attribute weighting models selected the final nucleotide sequences (from 29,000
    Language English
    Publishing date 2021-05-08
    Publishing country United States
    Document type Journal Article
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2021.104471
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