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dc.contributor.editorAltaf-Ul-Amin, Md.
dc.contributor.editorKanaya, Shigehiko
dc.contributor.editorOno, Naoaki
dc.contributor.editorHuang, Ming
dc.description.abstractRecent 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). 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 modelling such as machine learning have developed rapidly. All of these types of 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 have published 8 excellent papers dedicated to a variety of computational problems in the biomedical field from the genomic level to the whole-person physiological level.
dc.subject.classificationbic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues
dc.subject.classificationbic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
dc.subject.otherwater temperature
dc.subject.otherheart rate variability
dc.subject.otherquantitative analysis
dc.subject.otherhypertrophic cardiomyopathy
dc.subject.otherdata mining
dc.subject.otherautomated curation
dc.subject.othermolecular mechanisms
dc.subject.otheratrial fibrillation
dc.subject.othersudden cardiac death
dc.subject.otherheart failure
dc.subject.otherleft ventricular outflow tract obstruction
dc.subject.othercardiac fibrosis
dc.subject.othermyocardial ischemia
dc.subject.othercompound–protein interaction
dc.subject.othermachine learning
dc.subject.otherdrug discovery
dc.subject.otherherbal medicine
dc.subject.otherdata augmentation
dc.subject.otherdeep learning
dc.subject.otherECG quality assessment
dc.subject.otherdrug–target interactions
dc.subject.otherprotein–protein interactions
dc.subject.otherchronic diseases
dc.subject.otherdrug repurposing
dc.subject.othermaximum flow
dc.subject.otheradenosine methylation
dc.subject.otherRNA modification
dc.subject.otherneuronal development
dc.subject.othergenetic variation
dc.subject.othercopy number variants
dc.subject.otherdisease-related traits
dc.subject.othersequential order
dc.subject.otherassociation test
dc.subject.otherblood pressure
dc.subject.othercuffless measurement
dc.subject.otherlongitudinal experiment
dc.subject.othernonlinear regression
dc.titleRecent Trends in Computational Research on Diseases

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