Recent Trends in Computational Research on Diseases

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Biochemistry, Biophysics and Computational Biology".

Deadline for manuscript submissions: closed (20 October 2021) | Viewed by 20979

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Guest Editor
Division of Information Science, Nara Institute of Science and Technology, Ikoma 630-0192, Japan
Interests: systems biology; metabolomics; biological databases; data mining; computational research on drugs and diseases
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Division of Information Science, Nara Institute of Science and Technology, Ikoma 630-0192, Japan
Interests: bioinformatics; biodatabase; metabolome; systems biology; omics
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Division of Information Science, Nara Institute of Science and Technology, Ikoma 630-0192, Japan
Interests: bioinformatics; systems biology
Special Issues, Collections and Topics in MDPI journals
1. School of Data Science, Nagoya City University, Nagoya 467-8501, Japan
2. Division of Information Science, Nara Institute of Science and Technology, Ikoma 630-0192, Japan
Interests: health informatics for health promotion and disease management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in information technology have brought forth a paradigm shift in science, especially in the biology and medical fields. Statistical methodologies based on high-performance computing and big data analysis are now indispensable for qualitative and quantitative understanding of experimental results. In fact, the last few decades have witnessed drastic improvements in high-throughput experiments in health science, for example, mass spectrometry, DNA microarray, next generation sequencing, etc. Those methods have been providing massive data involving four major branches of omics (genomics, transcriptomics, proteomics, and metabolomics). Information about amino acid sequences, protein structures, and molecular structures are fundamental data for the prediction of bioactivity of chemical compounds when screening drugs. On the other hand, cell imaging, clinical imaging, and personal healthcare devices are also providing important data concerning the human body and disease. In parallel, various methods of mathematical modeling such as machine learning have developed so rapidly. All of the types of these data can be utilized in computational approaches to understand disease mechanisms, diagnosis, prognosis, drug discovery, drug repositioning, disease biomarkers, driver mutations, copy number variations, disease pathways, and much more.

In this Special Issue, we are inviting papers on novel models, methods, algorithms, and important innovations focusing on computational research on disease.

Dr. Md. Altaf-Ul-Amin
Prof. Dr. Shigehiko Kanaya
Dr. Naoaki Ono
Dr. Ming Huang
Guest Editors

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Keywords

  • multiomics diagnosis of disease
  • computer-aided diagnosis
  • machine learning for biomedical science
  • disease bioinformatics
  • big data analysis on drugs and disease

Published Papers (9 papers)

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Editorial

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3 pages, 155 KiB  
Editorial
Recent Trends in Computational Biomedical Research
by Md. Altaf-Ul-Amin, Shigehiko Kanaya, Naoaki Ono and Ming Huang
Life 2022, 12(1), 27; https://doi.org/10.3390/life12010027 - 24 Dec 2021
Cited by 2 | Viewed by 1756
Abstract
Recent advances in information technology have brought forth a paradigm shift in science, especially in the biology and medical fields [...] Full article
(This article belongs to the Special Issue Recent Trends in Computational Research on Diseases)

Research

Jump to: Editorial

14 pages, 768 KiB  
Article
Discussion of Cuffless Blood Pressure Prediction Using Plethysmograph Based on a Longitudinal Experiment: Is the Individual Model Necessary?
by Koshiro Kido, Zheng Chen, Ming Huang, Toshiyo Tamura, Wei Chen, Naoaki Ono, Masachika Takeuchi, Md. Altaf-Ul-Amin and Shigehiko Kanaya
Life 2022, 12(1), 11; https://doi.org/10.3390/life12010011 - 22 Dec 2021
Cited by 5 | Viewed by 2303
Abstract
Using the Plethysmograph (PPG) signal to estimate blood pressure (BP) is attractive given the convenience and possibility of continuous measurement. However, due to the personal differences and the insufficiency of data, the dilemma between the accuracy for a small dataset and the robustness [...] Read more.
Using the Plethysmograph (PPG) signal to estimate blood pressure (BP) is attractive given the convenience and possibility of continuous measurement. However, due to the personal differences and the insufficiency of data, the dilemma between the accuracy for a small dataset and the robustness as a general method remains. To this end, we scrutinized the whole pipeline from the feature selection to regression model construction based on a one-month experiment with 11 subjects. By constructing the explanatory features consisting of five general PPG waveform features that do not require the identification of dicrotic notch and diastolic peak and the heart rate, three regression models, which are partial least square, local weighted partial least square, and Gaussian Process model, were built to reflect the underlying assumption about the nature of the fitting problem. By comparing the regression models, it can be confirmed that an individual Gaussian Process model attains the best results with 5.1 mmHg and 4.6 mmHg mean absolute error for SBP and DBP and 6.2 mmHg and 5.4 mmHg standard deviation for SBP and DBP. Moreover, the results of the individual models are significantly better than the generalized model built with the data of all subjects. Full article
(This article belongs to the Special Issue Recent Trends in Computational Research on Diseases)
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13 pages, 523 KiB  
Article
SMCKAT, a Sequential Multi-Dimensional CNV Kernel-Based Association Test
by Nastaran Maus Esfahani, Daniel Catchpoole and Paul J. Kennedy
Life 2021, 11(12), 1302; https://doi.org/10.3390/life11121302 - 26 Nov 2021
Cited by 1 | Viewed by 1338
Abstract
Copy number variants (CNVs) are the most common form of structural genetic variation, reflecting the gain or loss of DNA segments compared with a reference genome. Studies have identified CNV association with different diseases. However, the association between the sequential order of CNVs [...] Read more.
Copy number variants (CNVs) are the most common form of structural genetic variation, reflecting the gain or loss of DNA segments compared with a reference genome. Studies have identified CNV association with different diseases. However, the association between the sequential order of CNVs and disease-related traits has not been studied, to our knowledge, and it is still unclear that CNVs function individually or whether they work in coordination with other CNVs to manifest a disease or trait. Consequently, we propose the first such method to test the association between the sequential order of CNVs and diseases. Our sequential multi-dimensional CNV kernel-based association test (SMCKAT) consists of three parts: (1) a single CNV group kernel measuring the similarity between two groups of CNVs; (2) a whole genome group kernel that aggregates several single group kernels to summarize the similarity between CNV groups in a single chromosome or the whole genome; and (3) an association test between the CNV sequential order and disease-related traits using a random effect model. We evaluate SMCKAT on CNV data sets exhibiting rare or common CNVs, demonstrating that it can detect specific biologically relevant chromosomal regions supported by the biomedical literature. We compare the performance of SMCKAT with MCKAT, a multi-dimensional kernel association test. Based on the results, SMCKAT can detect more specific chromosomal regions compared with MCKAT that not only have CNV characteristics, but the CNV order on them are significantly associated with the disease-related trait. Full article
(This article belongs to the Special Issue Recent Trends in Computational Research on Diseases)
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11 pages, 1766 KiB  
Article
Genome-Wide Scanning of Potential Hotspots for Adenosine Methylation: A Potential Path to Neuronal Development
by Sanjay Kumar, Lung-Wen Tsai, Pavan Kumar, Rajni Dubey, Deepika Gupta, Anjani Kumar Singh, Vishnu Swarup and Himanshu Narayan Singh
Life 2021, 11(11), 1185; https://doi.org/10.3390/life11111185 - 05 Nov 2021
Cited by 2 | Viewed by 2035
Abstract
Methylation of adenosines at N6 position (m6A) is the most frequent internal modification in mRNAs of the human genome and attributable to diverse roles in physiological development, and pathophysiological processes. However, studies on the role of m6A in neuronal development are sparse and [...] Read more.
Methylation of adenosines at N6 position (m6A) is the most frequent internal modification in mRNAs of the human genome and attributable to diverse roles in physiological development, and pathophysiological processes. However, studies on the role of m6A in neuronal development are sparse and not well-documented. The m6A detection remains challenging due to its inconsistent pattern and less sensitivity by the current detection techniques. Therefore, we applied a sliding window technique to identify the consensus site (5′-GGACT-3′) n ≥ 2 and annotated all m6A hotspots in the human genome. Over 6.78 × 107 hotspots were identified and 96.4% were found to be located in the non-coding regions, suggesting that methylation occurs before splicing. Several genes, RPS6K, NRP1, NRXN, EGFR, YTHDF2, have been involved in various stages of neuron development and their functioning. However, the contribution of m6A in these genes needs further validation in the experimental model. Thus, the present study elaborates the location of m6A in the human genome and its function in neuron physiology. Full article
(This article belongs to the Special Issue Recent Trends in Computational Research on Diseases)
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13 pages, 1935 KiB  
Article
A Maximum Flow-Based Approach to Prioritize Drugs for Drug Repurposing of Chronic Diseases
by Md. Mohaiminul Islam, Yang Wang and Pingzhao Hu
Life 2021, 11(11), 1115; https://doi.org/10.3390/life11111115 - 20 Oct 2021
Cited by 4 | Viewed by 1770
Abstract
The discovery of new drugs is required in the time of global aging and increasing populations. Traditional drug development strategies are expensive, time-consuming, and have high risks. Thus, drug repurposing, which treats new/other diseases using existing drugs, has become a very admired tactic. [...] Read more.
The discovery of new drugs is required in the time of global aging and increasing populations. Traditional drug development strategies are expensive, time-consuming, and have high risks. Thus, drug repurposing, which treats new/other diseases using existing drugs, has become a very admired tactic. It can also be referred to as the re-investigation of the existing drugs that failed to indicate the usefulness for the new diseases. Previously published literature used maximum flow approaches to identify new drug targets for drug-resistant infectious diseases but not for drug repurposing. Therefore, we are proposing a maximum flow-based protein–protein interactions (PPIs) network analysis approach to identify new drug targets (proteins) from the targets of the FDA (Food and Drug Administration) drugs and their associated drugs for chronic diseases (such as breast cancer, inflammatory bowel disease (IBD), and chronic obstructive pulmonary disease (COPD)) treatment. Experimental results showed that we have successfully turned the drug repurposing into a maximum flow problem. Our top candidates of drug repurposing, Guanidine, Dasatinib, and Phenethyl Isothiocyanate for breast cancer, IBD, and COPD were experimentally validated by other independent research as the potential candidate drugs for these diseases, respectively. This shows the usefulness of the proposed maximum flow approach for drug repurposing. Full article
(This article belongs to the Special Issue Recent Trends in Computational Research on Diseases)
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14 pages, 1553 KiB  
Article
Electrocardiogram Quality Assessment with a Generalized Deep Learning Model Assisted by Conditional Generative Adversarial Networks
by Xue Zhou, Xin Zhu, Keijiro Nakamura and Mahito Noro
Life 2021, 11(10), 1013; https://doi.org/10.3390/life11101013 - 26 Sep 2021
Cited by 10 | Viewed by 2261
Abstract
The electrocardiogram (ECG) is widely used for cardiovascular disease diagnosis and daily health monitoring. Before ECG analysis, ECG quality screening is an essential but time-consuming and experience-dependent work for technicians. An automatic ECG quality assessment method can reduce unnecessary time loss to help [...] Read more.
The electrocardiogram (ECG) is widely used for cardiovascular disease diagnosis and daily health monitoring. Before ECG analysis, ECG quality screening is an essential but time-consuming and experience-dependent work for technicians. An automatic ECG quality assessment method can reduce unnecessary time loss to help cardiologists perform diagnosis. This study aims to develop an automatic quality assessment system to search qualified ECGs for interpretation. The proposed system consists of data augmentation and quality assessment parts. For data augmentation, we train a conditional generative adversarial networks model to get an ECG segment generator, and thus to increase the number of training data. Then, we pre-train a deep quality assessment model based on a training dataset composed of real and generated ECG. Finally, we fine-tune the proposed model using real ECG and validate it on two different datasets composed of real ECG. The proposed system has a generalized performance on the two validation datasets. The model’s accuracy is 97.1% and 96.4%, respectively for the two datasets. The proposed method outperforms a shallow neural network model, and also a deep neural network models without being pre-trained by generated ECG. The proposed system demonstrates improved performance in the ECG quality assessment, and it has the potential to be an initial ECG quality screening tool in clinical practice. Full article
(This article belongs to the Special Issue Recent Trends in Computational Research on Diseases)
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12 pages, 1075 KiB  
Article
Identification of Targeted Proteins by Jamu Formulas for Different Efficacies Using Machine Learning Approach
by Sony Hartono Wijaya, Farit Mochamad Afendi, Irmanida Batubara, Ming Huang, Naoaki Ono, Shigehiko Kanaya and Md. Altaf-Ul-Amin
Life 2021, 11(8), 866; https://doi.org/10.3390/life11080866 - 23 Aug 2021
Cited by 3 | Viewed by 2299
Abstract
Background: We performed in silico prediction of the interactions between compounds of Jamu herbs and human proteins by utilizing data-intensive science and machine learning methods. Verifying the proteins that are targeted by compounds of natural herbs will be helpful to select natural herb-based [...] Read more.
Background: We performed in silico prediction of the interactions between compounds of Jamu herbs and human proteins by utilizing data-intensive science and machine learning methods. Verifying the proteins that are targeted by compounds of natural herbs will be helpful to select natural herb-based drug candidates. Methods: Initially, data related to compounds, target proteins, and interactions between them were collected from open access databases. Compounds are represented by molecular fingerprints, whereas amino acid sequences are represented by numerical protein descriptors. Then, prediction models that predict the interactions between compounds and target proteins were constructed using support vector machine and random forest. Results: A random forest model constructed based on MACCS fingerprint and amino acid composition obtained the highest accuracy. We used the best model to predict target proteins for 94 important Jamu compounds and assessed the results by supporting evidence from published literature and other sources. There are 27 compounds that can be validated by professional doctors, and those compounds belong to seven efficacy groups. Conclusion: By comparing the efficacy of predicted compounds and the relations of the targeted proteins with diseases, we found that some compounds might be considered as drug candidates. Full article
(This article belongs to the Special Issue Recent Trends in Computational Research on Diseases)
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21 pages, 2439 KiB  
Article
Shared Molecular Mechanisms of Hypertrophic Cardiomyopathy and Its Clinical Presentations: Automated Molecular Mechanisms Extraction Approach
by Mila Glavaški and Lazar Velicki
Life 2021, 11(8), 785; https://doi.org/10.3390/life11080785 - 03 Aug 2021
Cited by 5 | Viewed by 2818
Abstract
Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiovascular disease with a prevalence of 1 in 500 people and varying clinical presentations. Although there is much research on HCM, underlying molecular mechanisms are poorly understood, and research on the molecular mechanisms of its [...] Read more.
Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiovascular disease with a prevalence of 1 in 500 people and varying clinical presentations. Although there is much research on HCM, underlying molecular mechanisms are poorly understood, and research on the molecular mechanisms of its specific clinical presentations is scarce. Our aim was to explore the molecular mechanisms shared by HCM and its clinical presentations through the automated extraction of molecular mechanisms. Molecular mechanisms were congregated by a query of the INDRA database, which aggregates knowledge from pathway databases and combines it with molecular mechanisms extracted from abstracts and open-access full articles by multiple machine-reading systems. The molecular mechanisms were extracted from 230,072 articles on HCM and 19 HCM clinical presentations, and their intersections were found. Shared molecular mechanisms of HCM and its clinical presentations were represented as networks; the most important elements in the intersections’ networks were found, centrality scores for each element of each network calculated, networks with reduced level of noise generated, and cooperatively working elements detected in each intersection network. The identified shared molecular mechanisms represent possible mechanisms underlying different HCM clinical presentations. Applied methodology produced results consistent with the information in the scientific literature. Full article
(This article belongs to the Special Issue Recent Trends in Computational Research on Diseases)
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13 pages, 3304 KiB  
Article
Impact of Water Temperature on Heart Rate Variability during Bathing
by Jianbo Xu and Wenxi Chen
Life 2021, 11(5), 378; https://doi.org/10.3390/life11050378 - 22 Apr 2021
Cited by 2 | Viewed by 2517
Abstract
Background: Heart rate variability (HRV) is affected by many factors. This paper aims to explore the impact of water temperature (WT) on HRV during bathing. Methods: The bathtub WT was preset at three conditions: i.e., low WT (36–38 °C), medium WT (38–40 °C), [...] Read more.
Background: Heart rate variability (HRV) is affected by many factors. This paper aims to explore the impact of water temperature (WT) on HRV during bathing. Methods: The bathtub WT was preset at three conditions: i.e., low WT (36–38 °C), medium WT (38–40 °C), and high WT (40–42 °C), respectively. Ten subjects participated in the data collection. Each subject collected five electrocardiogram (ECG) recordings at each preset bathtub WT condition. Each recording was 18 min long with a sampling rate of 200 Hz. In total, 150 ECG recordings and 150 WT recordings were collected. Twenty HRV features were calculated using 1-min ECG segments each time. The k-means clustering analysis method was used to analyze the rough trends based on the preset WT. Analyses of the significant differences were performed using the multivariate analysis of variance of t-tests, and the mean and standard deviation (SD) of each HRV feature based on the WT were calculated. Results: The statistics show that with increasing WT, 11 HRV features are significantly (p < 0.05) and monotonously reduced, four HRV features are significantly (p < 0.05) and monotonously rising, two HRV features are rising first and then reduced, two HRV features (fuzzy and approximate entropy) are almost unchanged, and vLF power is rising. Conclusion: The WT has an important impact on HRV during bathing. The findings in the present work reveal an important physiological factor that affects the dynamic changes of HRV and contribute to better quantitative analyses of HRV in future research works. Full article
(This article belongs to the Special Issue Recent Trends in Computational Research on Diseases)
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