Genetic Design of Drugs Without Side-Effects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Consider two sets of strings, ${\cal B}$ (bad genes) and ${\cal G}$ (good genes), as well as two integers $d_b$ and $d_g$ ($d_b\leq d_g$). A frequently occurring problem in computational biology (and other fields) is to find a (distinguishing) substring s of length L that distinguishes the bad strings from good strings, i.e., such that for each string $s_i\in {\cal B}$ there exists a length-L substring ti of si with $d(s, t_i)\leq d_b$ (close to bad strings), and for every substring ui of length L of every string $g_i\in {\cal G}$, $d(s, u_i)\geq d_g$ (far from good strings). We present a polynomial time approximation scheme to settle the problem; i.e., for any constant $\epsilon >0$, the algorithm finds a string s of length L such that for every $s_i\in {\cal B}$ there is a length-L substring ti of si with $d(t_i, s)\leq (1+\epsilon) d_b$, and for every substring ui of length L of every $g_i\in {\cal G}$, $d(u_i, s)\geq (1-\epsilon) d_g$ if a solution to the original pair ($d_b\leq d_g$) exists. Since there is a polynomial number of such pairs $(d_b,d_g)$, we can exhaust all the possibilities in polynomial time to find a good approximation required by the corresponding application problems.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it