Will Systems Biology Deliver Its Promise and Contribute to the Development of New or Improved Vaccines?
Bibliographic record
Abstract
The advent of high-throughput "omics" technologies, combined with the computational and statistical methods necessary to analyze such data, have revolutionized biology, enabling a global view of the complex molecular processes and interactions that occur within a biological system.Such systems-based approaches have begun to be used in the evaluation of immune responses to vaccination, with the promise of identifying predictive biomarkers capable of rapidly evaluating vaccine efficacy, transforming our understanding of the immune mechanisms responsible for protective responses to vaccination and contributing to a new generation of rationally designed vaccines.Here we present our opinion that systems biology does indeed have a critical role in the future of vaccinology.Such approaches have shown potential in identifying transcriptional and cellular signatures of responsiveness to vaccination using diverse vaccines, adjuvants, and human populations.These findings, coupled with further mechanistic evaluation in animal models, will guide development of targeted vaccine and adjuvant formulations designed to optimally induce protective responses in populations of differing immune status. GREAT DEBATESWhat are the most interesting topics likely to come up over dinner or drinks with your colleagues?Or, more importantly, what are the topics that don't come up because they are a little too controversial?In Immune Memory and Vaccines: Great Debates, Editors Rafi Ahmed and Shane Crotty have put together a collection of articles on such questions, written by thought leaders in these fields, with the freedom to talk about the issues as they see fit.This short, innovative format aims to bring a fresh perspective by encouraging authors to be opinionated, focus on what is most interesting and current, and avoid restating introductory material covered in many other reviews.The Editors posed 13 interesting questions critical for our understanding of vaccines and immune memory to a broad group of experts in the field.In each case, several different perspectives are provided.Note that while each author knew that there were additional scientists addressing the same question, they did not know who these authors were, which ensured the independence of the opinions and perspectives expressed in each article.Our hope is that readers enjoy these articles and that they trigger many more conversations on these important topics.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".