Big data represents informational assets characterized by large volume – specifically, those that are too large for traditional data processing software to manage. As well as large volume, characteristic velocity and variety requires specific technology and analytical methods to derive useful insights. Big data research has been progressed rapidly in many fields, including biomedical science. Big data research in biomedical science has the potential to directly affect personal and precision medical care, reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases and improve the quality of life in general. Accordingly, biomedical professionals, now capable of collecting massive amounts of such data, look for best strategies to analyse and use these data, from very personal molecular bio-information to population-based social behaviour information.
Through advances in bioinformatics and information technology, big data research is now a hot topic in pharmacological research, especially in pharmacogenomic studies. The field of pharmacogenomics is an area of great potential for the big genomic data revolution. Present pharmacogenomics research (for instance, Next Generation Sequencing technologies) delivers large genomic data sets and information on cellular and human responses to therapies. In addition, pharmacogenomics data are usually analyzed together with a plethora of phenomics, dietary, lifestyle and clinical data, among others. Additionally, the results of pharmacogenetic research efforts have been integrated into clinical guidelines, esp. in cancer diagnosis therapy.
A parallel trend in big data research is the development of real-world database/registry and the technical challenges of the potential integration and annotation of data derived from genomic-level data sets in such structures. The public health implications of such integration have been prefigured by the US FDA, which increasingly recognizes the critical role of high-volume, real-world data, including - among others - electronic medical record data, post-marketing surveillances, claim-based databases, as an important reference of drug approval and pharmacovigilance. These trends will only continue with the recent release of new data sets containing linked genotypes such as insurance databases and health-information system data.
In this Research Topic - “Big data, pharmacogenomics and real-world research in pharmacology” - we aim for submissions of Original Research, Reviews, or Hypothesis articles in the following fields:
1. Primary advances in the study of pharmacogenomics and pharmacogenetics using big- data approaches
2. Studies of potential advances in real-world registry technology for cohort studies in therapeutics, including submissions where genomic data/gene-level variation has been proposed as a treatment influencing factor
3. Claim-based health database or health-information system, including submissions on research involving the integration of omics-level, biological data sets
4. Post-marketing drugs surveillance using population-based database or large registries, including submissions where genomic data/gene-level variation has been proposed to influence the safety profile of drugs.
5- Phenomics/genomics interactions relevant in pharmacogenetics and pharmacogenomics using big- data approaches
Big data represents informational assets characterized by large volume – specifically, those that are too large for traditional data processing software to manage. As well as large volume, characteristic velocity and variety requires specific technology and analytical methods to derive useful insights. Big data research has been progressed rapidly in many fields, including biomedical science. Big data research in biomedical science has the potential to directly affect personal and precision medical care, reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases and improve the quality of life in general. Accordingly, biomedical professionals, now capable of collecting massive amounts of such data, look for best strategies to analyse and use these data, from very personal molecular bio-information to population-based social behaviour information.
Through advances in bioinformatics and information technology, big data research is now a hot topic in pharmacological research, especially in pharmacogenomic studies. The field of pharmacogenomics is an area of great potential for the big genomic data revolution. Present pharmacogenomics research (for instance, Next Generation Sequencing technologies) delivers large genomic data sets and information on cellular and human responses to therapies. In addition, pharmacogenomics data are usually analyzed together with a plethora of phenomics, dietary, lifestyle and clinical data, among others. Additionally, the results of pharmacogenetic research efforts have been integrated into clinical guidelines, esp. in cancer diagnosis therapy.
A parallel trend in big data research is the development of real-world database/registry and the technical challenges of the potential integration and annotation of data derived from genomic-level data sets in such structures. The public health implications of such integration have been prefigured by the US FDA, which increasingly recognizes the critical role of high-volume, real-world data, including - among others - electronic medical record data, post-marketing surveillances, claim-based databases, as an important reference of drug approval and pharmacovigilance. These trends will only continue with the recent release of new data sets containing linked genotypes such as insurance databases and health-information system data.
In this Research Topic - “Big data, pharmacogenomics and real-world research in pharmacology” - we aim for submissions of Original Research, Reviews, or Hypothesis articles in the following fields:
1. Primary advances in the study of pharmacogenomics and pharmacogenetics using big- data approaches
2. Studies of potential advances in real-world registry technology for cohort studies in therapeutics, including submissions where genomic data/gene-level variation has been proposed as a treatment influencing factor
3. Claim-based health database or health-information system, including submissions on research involving the integration of omics-level, biological data sets
4. Post-marketing drugs surveillance using population-based database or large registries, including submissions where genomic data/gene-level variation has been proposed to influence the safety profile of drugs.
5- Phenomics/genomics interactions relevant in pharmacogenetics and pharmacogenomics using big- data approaches
