Chapter title |
Wellness and Health Omics Linked to the Environment: The WHOLE Approach to Personalized Medicine
|
---|---|
Chapter number | 1 |
Book title |
Systems Analysis of Human Multigene Disorders
|
Published in |
Advances in experimental medicine and biology, February 2016
|
DOI | 10.1007/978-1-4614-8778-4_1 |
Pubmed ID | |
Book ISBNs |
978-1-4614-8777-7, 978-1-4614-8778-4
|
Authors |
Greg Gibson, Gibson, Greg |
Abstract |
The WHOLE approach to personalized medicine represents an effort to integrate clinical and genomic profiling jointly into preventative health care and the promotion of wellness. Our premise is that genotypes alone are insufficient to predict health outcomes, since they fail to account for individualized responses to the environment and life history. Instead, integrative genomic approaches incorporating whole genome sequences and transcriptome and epigenome profiles, all combined with extensive clinical data obtained at annual health evaluations, have the potential to provide more informative wellness classification. As with traditional medicine where the physician interprets subclinical signs in light of the person's health history, truly personalized medicine will be founded on algorithms that extract relevant information from genomes but will also require interpretation in light of the triggers, behaviors, and environment that are unique to each person. This chapter discusses some of the major obstacles to implementation, from development of risk scores through integration of diverse omic data types to presentation of results in a format that fosters development of personal health action plans. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 25% |
United Kingdom | 1 | 13% |
Unknown | 5 | 63% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 5 | 63% |
Scientists | 3 | 38% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Korea, Republic of | 1 | 3% |
Unknown | 29 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 9 | 30% |
Researcher | 7 | 23% |
Student > Bachelor | 3 | 10% |
Student > Master | 3 | 10% |
Professor | 2 | 7% |
Other | 4 | 13% |
Unknown | 2 | 7% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 7 | 23% |
Computer Science | 5 | 17% |
Biochemistry, Genetics and Molecular Biology | 4 | 13% |
Agricultural and Biological Sciences | 4 | 13% |
Psychology | 2 | 7% |
Other | 5 | 17% |
Unknown | 3 | 10% |