LEADER 04415nam 2200937z- 450 001 9910566465403321 005 20231214133329.0 035 $a(CKB)5680000000037726 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/81117 035 $a(EXLCZ)995680000000037726 100 $a20202205d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRecent Trends in Computational Research on Diseases 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (130 p.) 311 $a3-0365-3230-7 311 $a3-0365-3231-5 330 $aRecent 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. 606 $aTechnology: general issues$2bicssc 606 $aHistory of engineering & technology$2bicssc 610 $awater temperature 610 $abathing 610 $aECG 610 $aheart rate variability 610 $aquantitative analysis 610 $at-test 610 $ahypertrophic cardiomyopathy 610 $adata mining 610 $aautomated curation 610 $amolecular mechanisms 610 $aatrial fibrillation 610 $asudden cardiac death 610 $aheart failure 610 $aleft ventricular outflow tract obstruction 610 $acardiac fibrosis 610 $amyocardial ischemia 610 $acompound-protein interaction 610 $aJamu 610 $amachine learning 610 $adrug discovery 610 $aherbal medicine 610 $adata augmentation 610 $adeep learning 610 $aECG quality assessment 610 $adrug-target interactions 610 $aprotein-protein interactions 610 $achronic diseases 610 $adrug repurposing 610 $amaximum flow 610 $aadenosine methylation 610 $am6A 610 $aRNA modification 610 $aneuronal development 610 $agenetic variation 610 $acopy number variants 610 $adisease-related traits 610 $asequential order 610 $aassociation test 610 $ablood pressure 610 $acuffless measurement 610 $alongitudinal experiment 610 $aplethysmograph 610 $anonlinear regression 615 7$aTechnology: general issues 615 7$aHistory of engineering & technology 700 $aAltaf-Ul-Amin$b Md$4edt$01328529 702 $aKanaya$b Shigehiko$4edt 702 $aOno$b Naoaki$4edt 702 $aHuang$b Ming$4edt 702 $aAltaf-Ul-Amin$b Md$4oth 702 $aKanaya$b Shigehiko$4oth 702 $aOno$b Naoaki$4oth 702 $aHuang$b Ming$4oth 906 $aBOOK 912 $a9910566465403321 996 $aRecent Trends in Computational Research on Diseases$93038651 997 $aUNINA