Recent Trends in Computational Research on Diseases
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 expe...
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| Ձևաչափ: | Online |
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| Լեզու: | անգլերեն |
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MDPI - Multidisciplinary Digital Publishing Institute
2022
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| Խորագրեր: | |
| Առցանց հասանելիություն: | ONIX_20220506_9783036532301_183 |
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Չկան պիտակներ, Եղեք առաջինը, ով նշում է այս գրառումը!
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| _version_ | 1869514064689889280 |
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| collection | Directory of Open Access Books |
| description | 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). 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. |
| format | Online |
| id | doab-20.500.12854ir-81117 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-811172024-04-09T23:15:53Z Recent Trends in Computational Research on Diseases Altaf-Ul-Amin, Md. Kanaya, Shigehiko Ono, Naoaki Huang, Ming 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology 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). 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. 2022-05-06T11:28:44Z 2022-05-06T11:28:44Z 2022 book ONIX_20220506_9783036532301_183 9783036532301 9783036532318 https://directory.doabooks.org/handle/20.500.12854/81117 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/5146 https://mdpi.com/books/pdfview/book/5146 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-3231-8 10.3390/books978-3-0365-3231-8 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036532301 9783036532318 130 Basel open access |
| spellingShingle | 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Recent Trends in Computational Research on Diseases |
| title | Recent Trends in Computational Research on Diseases |
| title_full | Recent Trends in Computational Research on Diseases |
| title_fullStr | Recent Trends in Computational Research on Diseases |
| title_full_unstemmed | Recent Trends in Computational Research on Diseases |
| title_short | Recent Trends in Computational Research on Diseases |
| title_sort | recent trends in computational research on diseases |
| topic | 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20220506_9783036532301_183 |