Antibody Engineering and Therapeutics Conference
Bibliographic record
Abstract
The Antibody Engineering and Therapeutics conference, which serves as the annual meeting of The Antibody Society, will be held in Huntington Beach, CA from Sunday December 8 through Thursday December 12, 2013. The scientific program will cover the full spectrum of challenges in antibody research and development, and provide updates on recent progress in areas from basic science through approval of antibody therapeutics. Keynote presentations will be given by Leroy Hood (Institute of System Biology), who will discuss a systems approach for studying disease that is enabled by emerging technology; Douglas Lauffenburger (Massachusetts Institute of Technology), who will discuss systems analysis of cell communication network dynamics for therapeutic biologics design; David Baker (University of Washington), who will describe computer-based design of smart protein therapeutics; and William Schief (The Scripps Research Institute), who will discuss epitope-focused immunogen design. In this preview of the conference, the workshop and session chairs share their thoughts on what conference participants may learn in sessions on: (1) three-dimensional structure antibody modeling; (2) identifying clonal lineages from next-generation data sets of expressed VH gene sequences; (3) antibodies in cardiometabolic medicine; (4) the effects of antibody gene variation and usage on the antibody response; (5) directed evolution; (6) antibody pharmacokinetics, distribution and off-target toxicity; (7) use of knowledge-based design to guide development of complementarity-determining regions and epitopes to engineer or elicit the desired antibody; (8) optimizing antibody formats for immunotherapy; (9) antibodies in a complex environment; (10) polyclonal, oligoclonal and bispecific antibodies; (11) antibodies to watch in 2014; and (12) polyreactive antibodies and polyspecificity.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.130 | 0.074 |
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".