Bibliographic record
Abstract
Introduction The neologism “omics” encompasses several new disciplines, such as genomics, transcriptomics, proteomics, metabolomics, and epigenomics, associated with the molecular analysis of biological events (Figure 25.1). Recently, a combination of whole genome sequencing; to RNA sequencing; and to proteomic, metabolomic, and autoantibody profiling has been realized from an individual to obtain an “integrative personal omics profile” (iPOP) [1]. This individual profile offers the perspective of a personal medicine and predicts future health and disease possibilities. For fertility comprehension, omics offers all the tools required for an in-depth study of the different steps required for a successful reproductive process. Since about 15% of couples have fertility problems, omics offers also diagnostic possibilities for assisted reproduction. For example, follicular cells (as well as cumulus cells) transcriptomes can help to predict which oocyte has the best potential to develop into an embryo or to determine the embryo(s) most likely to result in a pregnancy. Omics include all high-throughput techniques of every cellular metabolic component. At the DNA level, genomics refers to whole genome sequencing, DNA polymorphisms (such as single-nucleotide polymorphism [SNP]) or sequence variation (such as insertion, deletion, duplication, and copy number variants), etc. For RNA, transcriptomics studies the transcribed genome including mainly messenger RNA (mRNA) but also the ribosomal RNA (rRNA), transfer RNA (tRNA), and other non-coding RNA. When mRNAs have been translated, proteomics studies proteins, especially their structures and functions. Metabolomics involves the study of cellular activities with small-molecule metabolites profiles. Finally, epigenetics refers to the comprehension of different events (such as DNA methylation and histone modification) which influence gene expression without altering the DNA sequence. Taken together, integration of all these omics fields in a systems biology perspective is the ultimate research goal in order to give the complete picture of a cell. However, achieving this concept remains a big challenge.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".