A Review on Determination and Future of the Predictive and Personalized Medicine
Bibliographic record
Abstract
Medicine contents’ have extended to predictive, personalized, preventive and participatory medicine (P4). ‘Personalized medicine' focuses on the prediction of potential benefits or risks for individuals as possible as in detailed. Biomarker discovery, biocomputing and nanotechnology have opened a new horizon on ‘personalized medicine’ (including disease detection, diagnosis and therapy by using individual's molecular profile) and ‘predictive medicine’ (to predict disease development, progression and clinical outcome, by using the genetic and molecular information). Personalized medicine can be applied to a lot of different areas. P4 medicine, based on use of marker-assisted diagnosis and targeted therapies, comes from an individual's molecular profile, will form a new way on drugs development and medicine administration. Genetic screening aimed to identify carrier and affected individuals in a particular population. Molecular diagnostic test, including genome-derived tests are getting more attention within the medicine with genotyping, RNA expression analyses, metabolic profiling, and other biomarkers. Genomics research has getting more attention on the biomedical research, translational science, and personalized medicine; divided into 3 main parts: 1) genomics to biology, 2) genomics to health, and 3) genomics to society. We conducted a literature search via PubMed databases with using “personalized medicine”, and “application areas of P4” keywords, and summarized some of new studies. Personalized medicine is described as an individualized treatment based on the individual's genetic variants. In other words, “for predicting health, preventing and preempting disease, and personalizing treatment depending on the each person’ unique biology", has a speedy improvement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".