Editorial: Artificial Intelligence Bioinformatics: Development and Application of Tools for Omics and Inter-Omics Studies
Bibliographic record
Abstract
Artificial Intelligence Bioinformatics: Development and Application of Tools for Omics and Inter-Omics StudiesFor half a century, bioinformatics and computational biology have provided tools and data analysis approaches, so the beginning of the omics era represented a novel challenge for researchers, that converged to the area of bioinformatics from the fields of informatics, mathematics, and statistics.In most cases, the solutions offered appeared difficult to use for researchers working in biomedical areas.This occurred in particular when sophisticated approaches from the field of data science and artificial intelligence (AI), were applied to biomedical data (Lisboa et al., 2000).Machine learning, statistical learning, and soft-computing approaches, such as deep neural networks or genetic algorithms, have also become terms used in the bio world, with an incomplete comprehension however, of their potential (Pavel et al., 2016;Lin and Lane, 2017;Zeng and Lumley, 2018).In recent years, omics, multi-omics, and inter-omics experiments have presented a further step toward the investigation in biology, opening the window on personalized medicine, for example for diagnostics (Riemenschneider et al., 2016).The era of big data in medicine is imminent and represents yet a further step forward.Considering this, our Research Topic presents articles on novel developments in the field of artificial intelligence in biology and medicine, and their applications in the analysis of high-throughput data from omics and inter-omics approaches (Facchiano et al.).
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.024 | 0.031 |
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".