Personalized medicine is a clinical concept that divides patients into various categories, often referred to as precision medicine, with healthcare choices, procedures, treatments, and/or products customized to the individual patient depending on their expected response or disease risk. Of the 14 Grand Challenges for Engineering, an initiative funded by the National Academy of Engineering (NAE), 'Personalized Medicine' was recognized as a central and futuristic approach to "achieve optimal individual health decisions," thus overcoming the "Engineer Better Medicines" obstacle. Diagnostic testing is also used in personalized medicine to select adequate and suitable treatments based on genetic content or other molecular or cellular examination of a patient. Modern and transformative approaches in health care can be adapted to the principles of personalized medicine. Personalized medicine is focused on system biology dynamics. It uses statistical methods to assess health threats and design personalized treatment to help patients manage risks, avoid, and reliably treat disease as it happens.
Modern developments in personalized medicine rely on technology that confirms the basic biology, DNA, RNA, or protein of a patient, eventually leading to disease confirmation. A process such as RNA-seq will demonstrate which RNA molecules are involved in specific diseases. Sequencing RNA may also provide a more comprehensive understanding of the state of health of an individual. Recent studies have linked genetic differences to RNA expression, translation, and protein levels among individuals. A more unified clinical strategy unique to the individual and their genome would be generated through personalized medicine advancements. With earlier intervention, more effective drug production, and more tailored treatments, personalized medicine will improve diagnosis. Similarly, identifying the patients' mutations can help them get personalized medicine based on the genetic pattern.
This research topic focuses on personalized medicine and is intended to analyze novel research, techniques, instruments, and algorithms in the broader area (e.g., genes, molecules, cells, tissues, organs, people, and populations) and in association with computational, animal experiments and clinical data.
• Personalized medicine approaches in drug discovery and clinical trials
• Role of Proteomics and structural biology in the development of personalized medicine
• Multi-omic contributions to improving phenotypes of disease and association of gene-phenotypes (including epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics)
• Genotype-phenotype correlations (GWAS study)
• Clinical perspectives on targeted therapies for personalized medicine
• Accelerating novel medicine and better patient care from bedside to the benchtop
• Gene ontology, Pathway, interactome and Network analysis
• Various modeling techniques (computational, statistical, mathematical, etc.)
• Machine learning, deep learning, artificial intelligence
• Computer-aided drug discovery (CADD)
• High-performance computing system application including software, web-tools, and databases development
Personalized medicine is a clinical concept that divides patients into various categories, often referred to as precision medicine, with healthcare choices, procedures, treatments, and/or products customized to the individual patient depending on their expected response or disease risk. Of the 14 Grand Challenges for Engineering, an initiative funded by the National Academy of Engineering (NAE), 'Personalized Medicine' was recognized as a central and futuristic approach to "achieve optimal individual health decisions," thus overcoming the "Engineer Better Medicines" obstacle. Diagnostic testing is also used in personalized medicine to select adequate and suitable treatments based on genetic content or other molecular or cellular examination of a patient. Modern and transformative approaches in health care can be adapted to the principles of personalized medicine. Personalized medicine is focused on system biology dynamics. It uses statistical methods to assess health threats and design personalized treatment to help patients manage risks, avoid, and reliably treat disease as it happens.
Modern developments in personalized medicine rely on technology that confirms the basic biology, DNA, RNA, or protein of a patient, eventually leading to disease confirmation. A process such as RNA-seq will demonstrate which RNA molecules are involved in specific diseases. Sequencing RNA may also provide a more comprehensive understanding of the state of health of an individual. Recent studies have linked genetic differences to RNA expression, translation, and protein levels among individuals. A more unified clinical strategy unique to the individual and their genome would be generated through personalized medicine advancements. With earlier intervention, more effective drug production, and more tailored treatments, personalized medicine will improve diagnosis. Similarly, identifying the patients' mutations can help them get personalized medicine based on the genetic pattern.
This research topic focuses on personalized medicine and is intended to analyze novel research, techniques, instruments, and algorithms in the broader area (e.g., genes, molecules, cells, tissues, organs, people, and populations) and in association with computational, animal experiments and clinical data.
• Personalized medicine approaches in drug discovery and clinical trials
• Role of Proteomics and structural biology in the development of personalized medicine
• Multi-omic contributions to improving phenotypes of disease and association of gene-phenotypes (including epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics)
• Genotype-phenotype correlations (GWAS study)
• Clinical perspectives on targeted therapies for personalized medicine
• Accelerating novel medicine and better patient care from bedside to the benchtop
• Gene ontology, Pathway, interactome and Network analysis
• Various modeling techniques (computational, statistical, mathematical, etc.)
• Machine learning, deep learning, artificial intelligence
• Computer-aided drug discovery (CADD)
• High-performance computing system application including software, web-tools, and databases development
