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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.

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 --- compound-protein interaction --- drug-target interactions --- protein-protein interactions

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Pharmaceutical industry --- Drug development. --- Quality control. --- Drug Development. --- Efficiency, Organizational. --- Total Quality Management. --- Development of drugs --- Drugs --- New drug development --- Pharmacology --- Pharmacy --- Development --- Lean Six Sigma --- Sigma Metrics --- Six Sigma --- Continuous Quality Management --- Lean Six Sigmas --- Management, Continuous Quality --- Management, Total Quality --- Metric, Sigma --- Metrics, Sigma --- Sigma Metric --- Sigma, Six --- Sigmas, Six --- Six Sigma, Lean --- Six Sigmas --- Six Sigmas, Lean --- Quality Control --- Administrative Efficiency --- Organizational Productivity --- Efficiency, Administrative --- Productivity, Organizational --- Program Efficiency --- Efficiency, Program --- Organizational Efficiency --- Program Efficiencies --- Medication Development --- Pharmaceutical Development --- Computational Prediction of Drug-Target Interactions --- Drug Target Prediction --- Development, Drug --- Development, Medication --- Development, Pharmaceutical --- Drug Target Predictions --- Prediction, Drug Target --- Target Prediction, Drug

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"The development of a robust drug product requires juggling many competing priorities such as overcoming scientific challenges, following regulatory requirements, and managing business-related concerns. Unfortunately, despite large resources spent on R&D, multifactor productivity of pharmaceuticals is on the decline for several years now. Because of this business reality, pharmaceutical companies have seen a notable change in the traditional operating model and footprint over the past couple of decades. Outsourcing, in particular, has emerged as a successful business model for many pharmaceutical companies looking for ways to strategically increase their R&D capabilities and to augment their in-house resources. How to Integrate Quality by Efficient Design (QbED) in Product Development bridges the gap between theory and practice when it comes to strategic decision-making in a pharmaceutical research scenario. This book will introduce the concept of QbED and focus on various aspects such as patient-centric product designs, platform-based manufacturing technologies, business acuity, and regulatory strategies to balance the challenges in outsourcing with the need for strategic and statistically sound experiments rooted in good science. Detailed discussions will cover pharmaceutical business models, regulatory approval process, quality by design (QbD), business analytics, and manufacturing excellence specifically for small molecules and solid oral dosage forms. With the addition of case studies, flowcharts, diagrams, and data visualizations, How to Integrate Quality by Efficient Design (QbED) in Product Development will be a practical reference to help professionals working in the area of pharmaceutical drug development, strategy, and outsourcing management"--

Ethics and addiction --- Pharmaceutical industry --- Drug development. --- Drug Development. --- Efficiency, Organizational. --- Total Quality Management. --- Quality control. --- Lean Six Sigma --- Sigma Metrics --- Six Sigma --- Continuous Quality Management --- Lean Six Sigmas --- Management, Continuous Quality --- Management, Total Quality --- Metric, Sigma --- Metrics, Sigma --- Sigma Metric --- Sigma, Six --- Sigmas, Six --- Six Sigma, Lean --- Six Sigmas --- Six Sigmas, Lean --- Quality Control --- Administrative Efficiency --- Organizational Productivity --- Efficiency, Administrative --- Productivity, Organizational --- Program Efficiency --- Efficiency, Program --- Organizational Efficiency --- Program Efficiencies --- Medication Development --- Pharmaceutical Development --- Computational Prediction of Drug-Target Interactions --- Drug Target Prediction --- Development, Drug --- Development, Medication --- Development, Pharmaceutical --- Drug Target Predictions --- Prediction, Drug Target --- Target Prediction, Drug --- Development of drugs --- Drugs --- New drug development --- Pharmacology --- Pharmacy --- Development

Choose an application

• Reference Manager
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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.

Technology: general issues --- History of engineering & technology --- 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

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Neuropsychopharmacology. --- Drug development. --- Central Nervous System Agents. --- Drug Development. --- Translational Medical Research. --- Development of drugs --- Drugs --- New drug development --- Pharmacology --- Pharmacy --- Neuro-psychopharmacology --- Neuropharmacology --- Psychopharmacology --- Knowledge Translation --- Translational Medical Science --- Translational Medicine --- Translational Research, Medical --- Translational Research --- Knowledge Translations --- Medical Research, Translational --- Medical Science, Translational --- Medical Sciences, Translational --- Medical Translational Research --- Medicine, Translational --- Research, Medical Translational --- Research, Translational --- Research, Translational Medical --- Science, Translational Medical --- Sciences, Translational Medical --- Translation, Knowledge --- Translational Medical Sciences --- Translational Researchs --- Translations, Knowledge --- National Center for Advancing Translational Sciences (U.S.) --- Medication Development --- Pharmaceutical Development --- Computational Prediction of Drug-Target Interactions --- Drug Target Prediction --- Development, Drug --- Development, Medication --- Development, Pharmaceutical --- Drug Target Predictions --- Prediction, Drug Target --- Target Prediction, Drug --- Central Nervous System Drugs --- Development --- Translational Research, Biomedical.