Deciphering the complexities of human diseases and disorders by coupling induced‐pluripotent stem cells and systems genetics
Bibliographic record
Abstract
Abstract The recent discovery that adult mouse and human somatic cells can be ‘reprogrammed’ to a state of pluripotency by ectopic expression of only a few transcription factors has already made a major impact on the biomedical community. For the first time, it is possible to study diseases on an individual patient basis, which may eventually lead to the realization of personalized medicine. The utility of induced‐pluripotent stem cells (iPSCs) for modeling human diseases has greatly benefitted from established human embryonic stem cell (ESC) differentiation and tissue engineering protocols developed to generate many different cell and tissue types. The limited access to preimplantation genetic tested embryos and the difficulty in gene targeting human ESCs have restricted the use of human ESCs in modeling human disease. Afforded by iPSC technology is the ability to study disease pathogenesis as it unfolds during tissue morphogenesis. The complexities of molecular signaling and interplay with protein transduction during disease progression necessitate a systems approach to studying human diseases, whereby data can be statistically integrated by sorting out the signal to noise issues that arise from global biological changes associated with disease versus experimental noise. Using a systems approach, biomarkers can be identified that define the initiation or progression of disease and likewise can serve as putative therapeutic targets. WIREs Syst Biol Med 2012 doi: 10.1002/wsbm.1170 This article is categorized under: Models of Systems Properties and Processes > Cellular Models Laboratory Methods and Technologies > Genetic/Genomic Methods Translational, Genomic, and Systems Medicine > Translational Medicine
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".