Certificate in Biomedical and Clinical Studies McGill University – Montreal, QC awarded Feb 2006
Bibliographic record
Abstract
Designing a novel designer nuclease scaffold for improved therapeutic genome engineering (work done primarily in the Scharenberg lab)- Assembled and identified an ideal architecture for a meganuclease – TAL effector fusion protein (megaTAL)- Characterized megaTAL cleavage activity and target site specificity- Built and tested megaTALs designed for therapeutic targets in collaboration with numerous others Redesign of meganuclease target site specificity (work done primarily in the Baker lab)- Benchmarked Rosetta protein-DNA interface design methods by generating meganuclease variants with specificity switches from their native target- Aided in project identifying meganuclease structural specificity determinants by comparing homologs with varied sequence specificity- Designed meganucleases towards novel, therapeutic DNA targets using a combination of in silico prediction and directed evolution Fred Hutchinson Cancer Research Center 2010 Graduate Research Assistant rotation with Dr. Barry Stoddard Crystallization and characterization of homing endonuclease variants- Learned crystallization techniques while trying to solve the protein structure of meganuclease variants Fred Hutchinson Cancer Research Center 2009
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.597 | 0.229 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".