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  1. Book ; Online: Recent Trends in Computational Research on Diseases

    Altaf-Ul-Amin, Md / Kanaya, Shigehiko / Ono, Naoaki / Huang, Ming

    2022  

    Keywords Technology: general issues ; History of engineering & technology ; water temperature ; bathing ; ECG ; heart rate variability ; quantitative analysis ; t-test ; hypertrophic cardiomyopathy ; data mining ; automated curation ; molecular mechanisms ; atrial fibrillation ; sudden cardiac death ; heart failure ; left ventricular outflow tract obstruction ; cardiac fibrosis ; myocardial ischemia ; compound-protein interaction ; Jamu ; machine learning ; drug discovery ; herbal medicine ; data augmentation ; deep learning ; ECG quality assessment ; drug-target interactions ; protein-protein interactions ; chronic diseases ; drug repurposing ; maximum flow ; adenosine methylation ; m6A ; RNA modification ; neuronal development ; genetic variation ; copy number variants ; disease-related traits ; sequential order ; association test ; blood pressure ; cuffless measurement ; longitudinal experiment ; plethysmograph ; nonlinear regression ; n/a
    Language 0|e
    Size 1 electronic resource (130 pages)
    Publisher MDPI - Multidisciplinary Digital Publishing Institute
    Publishing place Basel
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT021609325
    ISBN 9783036532318 ; 3036532315
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Article ; Online: Antibacterial Activity Prediction of Plant Secondary Metabolites Based on a Combined Approach of Graph Clustering and Deep Neural Network.

    Karim, Mohammad Bozlul / Kanaya, Shigehiko / Altaf-Ul-Amin, Md

    Molecular informatics

    2022  Volume 41, Issue 7, Page(s) e2100247

    Abstract: The plants produce numerous types of secondary metabolites which have pharmacological importance in drug development for different diseases. Computational methods widely use the fingerprints of the metabolites to understand different properties and ... ...

    Abstract The plants produce numerous types of secondary metabolites which have pharmacological importance in drug development for different diseases. Computational methods widely use the fingerprints of the metabolites to understand different properties and similarities among metabolites and for the prediction of chemical reactions etc. In this work, we developed three different deep neural network models (DNN) to predict the antibacterial property of plant metabolites. We developed the first DNN model using the fingerprint set of metabolites as features. In the second DNN model, we searched the similarities among fingerprints using correlation and used one representative feature from each group of highly correlated fingerprints. In the third model, the fingerprints of metabolites were used to find structurally similar chemical compound clusters. Form each cluster a representative metabolite is selected and made part of the training dataset. The second model reduced the number of features where the third model achieved better classification results for test data. In both cases, we applied the simple graph clustering method to cluster the corresponding network. The correlation-based DNN model reduced some features while retaining an almost similar performance compared to the first DNN model. The third model improves classification results for test data by capturing wider variance within training data using graph clustering method. This third model is somewhat novel approach and can be applied to build DNN models for other purposes.
    MeSH term(s) Anti-Bacterial Agents/pharmacology ; Cluster Analysis ; Neural Networks, Computer
    Chemical Substances Anti-Bacterial Agents
    Language English
    Publishing date 2022-01-28
    Publishing country Germany
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2537668-8
    ISSN 1868-1751 ; 1868-1743
    ISSN (online) 1868-1751
    ISSN 1868-1743
    DOI 10.1002/minf.202100247
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Recent Trends in Computational Biomedical Research.

    Altaf-Ul-Amin, Md / Kanaya, Shigehiko / Ono, Naoaki / Huang, Ming

    Life (Basel, Switzerland)

    2021  Volume 12, Issue 1

    Abstract: Recent advances in information technology have brought forth a paradigm shift in science, especially in the biology and medical fields [ ... ]. ...

    Abstract Recent advances in information technology have brought forth a paradigm shift in science, especially in the biology and medical fields [...].
    Language English
    Publishing date 2021-12-24
    Publishing country Switzerland
    Document type Editorial
    ZDB-ID 2662250-6
    ISSN 2075-1729
    ISSN 2075-1729
    DOI 10.3390/life12010027
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Deep Learning Approach for Predicting the Therapeutic Usages of Unani Formulas towards Finding Essential Compounds.

    Wijaya, Sony Hartono / Nasution, Ahmad Kamal / Batubara, Irmanida / Gao, Pei / Huang, Ming / Ono, Naoaki / Kanaya, Shigehiko / Altaf-Ul-Amin, Md

    Life (Basel, Switzerland)

    2023  Volume 13, Issue 2

    Abstract: The use of herbal medicines in recent decades has increased because their side effects are considered lower than conventional medicine. Unani herbal medicines are often used in Southern Asia. These herbal medicines are usually composed of several types ... ...

    Abstract The use of herbal medicines in recent decades has increased because their side effects are considered lower than conventional medicine. Unani herbal medicines are often used in Southern Asia. These herbal medicines are usually composed of several types of medicinal plants to treat various diseases. Research on herbal medicine usually focuses on insight into the composition of plants used as ingredients. However, in the present study, we extended to the level of metabolites that exist in the medicinal plants. This study aimed to develop a predictive model of the Unani therapeutic usage based on its constituent metabolites using deep learning and data-intensive science approaches. Furthermore, the best prediction model was then utilized to extract important metabolites for each therapeutic usage of Unani. In this study, it was observed that the deep neural network approach provided a much better prediction model than other algorithms including random forest and support vector machine. Moreover, according to the best prediction model using the deep neural network, we identified 118 important metabolites for nine therapeutic usages of Unani.
    Language English
    Publishing date 2023-02-03
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2662250-6
    ISSN 2075-1729
    ISSN 2075-1729
    DOI 10.3390/life13020439
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Automated Sleep Staging via Parallel Frequency-Cut Attention.

    Chen, Zheng / Yang, Ziwei / Zhu, Lingwei / Chen, Wei / Tamura, Toshiyo / Ono, Naoaki / Altaf-Ul-Amin, Md / Kanaya, Shigehiko / Huang, Ming

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

    2023  Volume 31, Page(s) 1974–1985

    Abstract: Stage-based sleep screening is a widely-used tool in both healthcare and neuroscientific research, as it allows for the accurate assessment of sleep patterns and stages. In this paper, we propose a novel framework that is based on authoritative guidance ... ...

    Abstract Stage-based sleep screening is a widely-used tool in both healthcare and neuroscientific research, as it allows for the accurate assessment of sleep patterns and stages. In this paper, we propose a novel framework that is based on authoritative guidance in sleep medicine and is designed to automatically capture the time-frequency characteristics of sleep electroencephalogram (EEG) signals in order to make staging decisions. Our framework consists of two main phases: a feature extraction process that partitions the input EEG spectrograms into a sequence of time-frequency patches, and a staging phase that searches for correlations between the extracted features and the defining characteristics of sleep stages. To model the staging phase, we utilize a Transformer model with an attention-based module, which allows for the extraction of global contextual relevance among time-frequency patches and the use of this relevance for staging decisions. The proposed method is validated on the large-scale Sleep Heart Health Study dataset and achieves new state-of-the-art results for the wake, N2, and N3 stages, with respective F1 scores of 0.93, 0.88, and 0.87 using only EEG signals. Our method also demonstrates high inter-rater reliability, with a kappa score of 0.80. Moreover, we provide visualizations of the correspondence between sleep staging decisions and features extracted by our method, which enhances the interpretability of the proposal. Overall, our work represents a significant contribution to the field of automated sleep staging and has important implications for both healthcare and neuroscience research.
    MeSH term(s) Humans ; Reproducibility of Results ; Sleep Stages ; Sleep ; Polysomnography/methods ; Electroencephalography/methods
    Language English
    Publishing date 2023-04-12
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1166307-8
    ISSN 1558-0210 ; 1063-6528 ; 1534-4320
    ISSN (online) 1558-0210
    ISSN 1063-6528 ; 1534-4320
    DOI 10.1109/TNSRE.2023.3243589
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4726: 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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  6. Article ; Online: Learning vector quantized representation for cancer subtypes identification.

    Chen, Zheng / Yang, Ziwei / Zhu, Lingwei / Gao, Peng / Matsubara, Takashi / Kanaya, Shigehiko / Altaf-Ul-Amin, Md

    Computer methods and programs in biomedicine

    2023  Volume 236, Page(s) 107543

    Abstract: Background and objective: Defining and separating cancer subtypes is essential for facilitating personalized therapy modality and prognosis of patients. The definition of subtypes has been constantly recalibrated as a result of our deepened ... ...

    Abstract Background and objective: Defining and separating cancer subtypes is essential for facilitating personalized therapy modality and prognosis of patients. The definition of subtypes has been constantly recalibrated as a result of our deepened understanding. During this recalibration, researchers often rely on clustering of cancer data to provide an intuitive visual reference that could reveal the intrinsic characteristics of subtypes. The data being clustered are often omics data such as transcriptomics that have strong correlations to the underlying biological mechanism. However, while existing studies have shown promising results, they suffer from issues associated with omics data: sample scarcity and high dimensionality while they impose unrealistic assumptions to extract useful features from the data while avoiding overfitting to spurious correlations.
    Methods: This paper proposes to leverage a recent strong generative model, Vector-Quantized Variational AutoEncoder, to tackle the data issues and extract discrete representations that are crucial to the quality of subsequent clustering by retaining only information relevant to reconstructing the input.
    Results: Extensive experiments and medical analysis on multiple datasets comprising 10 distinct cancers demonstrate the proposed clustering results can significantly and robustly improve prognosis over prevalent subtyping systems.
    Conclusion: Our proposal does not impose strict assumptions on data distribution; while, its latent features are better representations of the transcriptomic data in different cancer subtypes, capable of yielding superior clustering performance with any mainstream clustering method.
    MeSH term(s) Humans ; Neoplasms ; Gene Expression Profiling ; Transcriptome ; Cluster Analysis
    Language English
    Publishing date 2023-04-11
    Publishing country Ireland
    Document type Journal Article
    ZDB-ID 632564-6
    ISSN 1872-7565 ; 0169-2607
    ISSN (online) 1872-7565
    ISSN 0169-2607
    DOI 10.1016/j.cmpb.2023.107543
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.B 521: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  7. Article ; Online: Recent Trends in Computational Biomedical Research

    Md. Altaf-Ul-Amin / Shigehiko Kanaya / Naoaki Ono / Ming Huang

    Life, Vol 12, Iss 27, p

    2022  Volume 27

    Abstract: Recent advances in information technology have brought forth a paradigm shift in science, especially in the biology and medical fields [.] ...

    Abstract Recent advances in information technology have brought forth a paradigm shift in science, especially in the biology and medical fields [.]
    Keywords n/a ; Science ; Q
    Language English
    Publishing date 2022-12-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  8. Article ; Online: On finding natural antibiotics based on TCM formulae.

    Gao, Pei / Nasution, Ahmad Kamal / Yang, Shuo / Chen, Zheng / Ono, Naoaki / Kanaya, Shigehiko / Altaf-Ul-Amin, M D

    Methods (San Diego, Calif.)

    2023  Volume 214, Page(s) 35–45

    Abstract: Context: Novel kinds of antibiotics are needed to combat the emergence of antibacterial resistance. Natural products (NPs) have shown potential as antibiotic candidates. Current experimental methods are not yet capable of exploring the massive, ... ...

    Abstract Context: Novel kinds of antibiotics are needed to combat the emergence of antibacterial resistance. Natural products (NPs) have shown potential as antibiotic candidates. Current experimental methods are not yet capable of exploring the massive, redundant, and noise-involved chemical space of NPs. In silico approaches are needed to select NPs as antibiotic candidates.
    Objective: This study screens out NPs with antibacterial efficacy guided by both TCM and modern medicine and constructed a dataset aiming to serve the new antibiotic design.
    Method: A knowledge-based network is proposed in this study involving NPs, herbs, the concepts of TCM, and the treatment protocols (or etiologies) of infectious in modern medicine. Using this network, the NPs candidates are screened out and compose the dataset. Feature selection of machine learning approaches is conducted to evaluate the constructed dataset and statistically validate the im- portance of all NPs candidates for different antibiotics by a classification task.
    Results: The extensive experiments prove the constructed dataset reaches a convincing classification performance with a 0.9421 weighted accuracy, 0.9324 recall, and 0.9409 precision. The further visu- alizations of sample importance prove the comprehensive evaluation for model interpretation based on medical value considerations.
    MeSH term(s) Medicine, Chinese Traditional/methods ; Biological Products/pharmacology
    Chemical Substances Biological Products
    Language English
    Publishing date 2023-04-03
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1066584-5
    ISSN 1095-9130 ; 1046-2023
    ISSN (online) 1095-9130
    ISSN 1046-2023
    DOI 10.1016/j.ymeth.2023.04.001
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3239: 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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  9. Article: On QSAR-based cardiotoxicity modeling with the expressiveness-enhanced graph learning model and dual-threshold scheme.

    Wang, Huijia / Zhu, Guangxian / Izu, Leighton T / Chen-Izu, Ye / Ono, Naoaki / Altaf-Ul-Amin, M D / Kanaya, Shigehiko / Huang, Ming

    Frontiers in physiology

    2023  Volume 14, Page(s) 1156286

    Abstract: Introduction: ...

    Abstract Introduction:
    Language English
    Publishing date 2023-05-09
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2564217-0
    ISSN 1664-042X
    ISSN 1664-042X
    DOI 10.3389/fphys.2023.1156286
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Information maximization-based clustering of histopathology images using deep learning.

    Rumman, Mahfujul Islam / Ono, Naoaki / Ohuchida, Kenoki / Altaf-Ul-Amin, M D / Huang, Ming / Kanaya, Shigehiko

    PLOS digital health

    2023  Volume 2, Issue 12, Page(s) e0000391

    Abstract: Pancreatic cancer is one of the most adverse diseases and it is very difficult to treat because the cancer cells formed in the pancreas intertwine themselves with nearby blood vessels and connective tissue. Hence, the surgical procedure of treatment ... ...

    Abstract Pancreatic cancer is one of the most adverse diseases and it is very difficult to treat because the cancer cells formed in the pancreas intertwine themselves with nearby blood vessels and connective tissue. Hence, the surgical procedure of treatment becomes complicated and it does not always lead to a cure. Histopathological diagnosis is the usual approach for cancer diagnosis. However, the pancreas remains so deep inside the body that experts sometimes struggle to detect cancer in it. Computer-aided diagnosis can come to the aid of pathologists in this scenario. It assists experts by supporting their diagnostic decisions. In this research, we carried out a deep learning-based approach to analyze histopathology images. We collected whole-slide images of KPC mice to implement this work. The pancreatic abnormalities observed in KPC mice develop similar histological features to human beings. We created random patches from whole-slide images. Then, a convolutional autoencoder framework was used to embed these patches into an integrated latent space. We applied 'information maximization', a deep learning clustering technique to cluster the identical patches in an unsupervised manner since our dataset does not have annotation. Moreover, Uniform manifold approximation and projection, a nonlinear dimension reduction technique was utilized to visualize the embedded patches in a 2-dimensional space. Finally, we calculated a few internal cluster validation metrics to determine the optimal cluster set. Our work concentrated on patch-based anomaly detection in the whole slide histopathology images of KPC mice.
    Language English
    Publishing date 2023-12-08
    Publishing country United States
    Document type Journal Article
    ISSN 2767-3170
    ISSN (online) 2767-3170
    DOI 10.1371/journal.pdig.0000391
    Database MEDical Literature Analysis and Retrieval System OnLINE

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