Recent Trends in Computational Biomedical Research

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 (31 August 2022) | Viewed by 9754

Special Issue Editors

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
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
Dr. Naoaki Ono
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 the 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). 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 based on machine learning including auto-encoder-based deep neural networks have developed rapidly. All of these data types can be utilized in computational approaches for biomedical research such as on understanding disease mechanisms, diagnosis, prognosis, drug discovery, drug repositioning, disease biomarkers, driver mutations, copy number variations, disease pathways, medical imaging, and much more.

The first volume of this Special Issue was a great success (https://www.mdpi.com/journal/life/special_issues/computational_diseases). Now, we invite you to publish in the second volume.

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

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

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Life is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (5 papers)

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Research

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15 pages, 1675 KiB  
Article
Deep Learning Approach for Predicting the Therapeutic Usages of Unani Formulas towards Finding Essential Compounds
Life 2023, 13(2), 439; https://doi.org/10.3390/life13020439 - 03 Feb 2023
Cited by 1 | Viewed by 1277
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Recent Trends in Computational Biomedical Research)
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20 pages, 7461 KiB  
Article
Subtypes and Mechanisms of Hypertrophic Cardiomyopathy Proposed by Machine Learning Algorithms
Life 2022, 12(10), 1566; https://doi.org/10.3390/life12101566 - 09 Oct 2022
Cited by 3 | Viewed by 1419
Abstract
Hypertrophic cardiomyopathy (HCM) is a relatively common inherited cardiac disease that results in left ventricular hypertrophy. Machine learning uses algorithms to study patterns in data and develop models able to make predictions. The aim of this study is to identify HCM subtypes and [...] Read more.
Hypertrophic cardiomyopathy (HCM) is a relatively common inherited cardiac disease that results in left ventricular hypertrophy. Machine learning uses algorithms to study patterns in data and develop models able to make predictions. The aim of this study is to identify HCM subtypes and examine the mechanisms of HCM using machine learning algorithms. Clinical and laboratory findings of 143 adult patients with a confirmed diagnosis of nonobstructive HCM are analyzed; HCM subtypes are determined by clustering, while the presence of different HCM features is predicted in classification machine learning tasks. Four clusters are determined as the optimal number of clusters for this dataset. Models that can predict the presence of particular HCM features from other genotypic and phenotypic information are generated, and subsets of features sufficient to predict the presence of other features of HCM are determined. This research proposes four subtypes of HCM assessed by machine learning algorithms and based on the overall phenotypic expression of the participants of the study. The identified subsets of features sufficient to determine the presence of particular HCM aspects could provide deeper insights into the mechanisms of HCM. Full article
(This article belongs to the Special Issue Recent Trends in Computational Biomedical Research)
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15 pages, 2291 KiB  
Article
Bioactive Compounds in Garlic (Allium sativum) and Black Garlic as Antigout Agents, Using Computer Simulation
Life 2022, 12(8), 1131; https://doi.org/10.3390/life12081131 - 27 Jul 2022
Cited by 3 | Viewed by 2272
Abstract
Uric acid, which causes gout, is the end product of purine catabolism, synthesized by xanthine oxidase, guanine deaminase, adenine deaminase, purine nucleoside phosphorylase, and 5-nucleotidase II. Garlic contains bioactive compounds that have potential as antigout agents. Garlic fermentation to black garlic changes its [...] Read more.
Uric acid, which causes gout, is the end product of purine catabolism, synthesized by xanthine oxidase, guanine deaminase, adenine deaminase, purine nucleoside phosphorylase, and 5-nucleotidase II. Garlic contains bioactive compounds that have potential as antigout agents. Garlic fermentation to black garlic changes its components, which may affect its beneficial potential. This study aimed to select types of garlic (Indonesian garlic) and imported garlic, and to predict the interaction between their compounds and five target proteins through an in silico approach and a multivariate analysis, namely partial least squares-discriminant analysis (PLS-DA), to determine their different constituents. The target proteins were collected from open-access databases, and the compounds were identified using mass spectrometry data. The PLS-DA score plot succeeded in classifying the samples into three classes, with each class having a discriminatory compound. Based on the in silico studies, we predicted the best binding score of the five target proteins with seven important compounds: alliin, N-acetyl-S-allyl-L-cysteine, ajoene, pyridoxal, pyridoxamine, 4-guanidinobutyric acid, and D-glucosamine. These were mostly found in black garlic, with no different concentrations in the local and imported samples. Through this approach, we concluded that black garlic is a better candidate for antigout treatments, as several compounds were found to have good binding to the target proteins. Full article
(This article belongs to the Special Issue Recent Trends in Computational Biomedical Research)
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16 pages, 2420 KiB  
Article
Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning
Life 2022, 12(6), 776; https://doi.org/10.3390/life12060776 - 24 May 2022
Cited by 5 | Viewed by 1728
Abstract
Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to [...] Read more.
Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to explore phenotypes of patients in a derivation dataset (n = 562) based on their medical records. Thereafter, supervised ML models were trained on the derivation dataset to classify these identified phenotypes. Then, the trained classifiers were further validated on an independent validation dataset (n = 168). Finally, Shapley additive explanations were used to interpret decision making of phenotype classification. Three patient phenotypes corresponding to stratified mortality risk (high, low, and intermediate) were identified. Kaplan–Meier survival curves among the three phenotypes had significant difference (pairwise comparison p < 0.05). Hazard ratio of all-cause mortality between patients in phenotype 1 (n = 91; high risk) and phenotype 3 (n = 329; intermediate risk) was 2.08 (95%CI 1.29–3.37, p = 0.003), and 0.26 (95%CI 0.11–0.61, p = 0.002) between phenotype 2 (n = 142; low risk) and phenotype 3. For phenotypes classification by random forest, AUCs of phenotypes 1, 2, and 3 were 0.736 ± 0.038, 0.815 ± 0.035, and 0.721 ± 0.03, respectively, slightly better than the decision tree. Then, the classifier effectively identified the phenotypes for new patients in the validation dataset with significant difference on survival curves and hazard ratios. Finally, age and creatinine clearance rate were identified as the top two most important predictors. ML could effectively identify patient prognostic phenotypes, facilitating reasonable management and treatment considering prognostic condition. Full article
(This article belongs to the Special Issue Recent Trends in Computational Biomedical Research)
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Review

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19 pages, 1057 KiB  
Review
A Pipeline for Phasing and Genotype Imputation on Mixed Human Data (Parents-Offspring Trios and Unrelated Subjects) by Reviewing Current Methods and Software
Life 2022, 12(12), 2030; https://doi.org/10.3390/life12122030 - 05 Dec 2022
Viewed by 2184
Abstract
Genotype imputation has become an essential prerequisite when performing association analysis. It is a computational technique that allows us to infer genetic markers that have not been directly genotyped, thereby increasing statistical power in subsequent association studies, which consequently has a crucial impact [...] Read more.
Genotype imputation has become an essential prerequisite when performing association analysis. It is a computational technique that allows us to infer genetic markers that have not been directly genotyped, thereby increasing statistical power in subsequent association studies, which consequently has a crucial impact on the identification of causal variants. Many features need to be considered when choosing the proper algorithm for imputation, including the target sample on which it is performed, i.e., related individuals, unrelated individuals, or both. Problems could arise when dealing with a target sample made up of mixed data, composed of both related and unrelated individuals, especially since the scientific literature on this topic is not sufficiently clear. To shed light on this issue, we examined existing algorithms and software for performing phasing and imputation on mixed human data from SNP arrays, specifically when related subjects belong to trios. By discussing the advantages and limitations of the current algorithms, we identified LD-based methods as being the most suitable for reconstruction of haplotypes in this specific context, and we proposed a feasible pipeline that can be used for imputing genotypes in both phased and unphased human data. Full article
(This article belongs to the Special Issue Recent Trends in Computational Biomedical Research)
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