The early introduction of dynamic programming into computational biology
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
In 1994–1995, DIMACS sponsored a theme year on computational biology. Among the numerous seminars and workshops was one organized by Alberto Apostolico and Raffaele Giancarlo, recapitulated in their 1998 paper, on the history and motivations for sequence comparison. In my participation in this event, I was led to consider some of the early interactions, at the Centre de recherches mathematiques (CRM) and elsewhere, in the field now known as computational biology. A short time earlier, I had read a 1989 paper by Walter Goad on the impact of Stanislaw Ulam in this field. The penultimate sentence in this article was a quote from Ulam himself ‘I started all this’, which puzzled me greatly. As far as I knew, Ulam had no impact in the early development of the field, and while he gave talks on it at least from 1971 (cf. Ulam, 1972), the distance he defined was already published (Levenshtein, 1965) and the problem he proposed had already been solved, for all intents and purposes, in the molecular biology literature (Needleman and Wunsch, 1970) and elsewhere (Vintsyuk, 1968). I had also read a joint interview of Ulam and Mark Kac by Feigenbaum (1982), and this led me to reflect on this misperception on the part of Ulam (and of Goad), and to crystallize the realization that ironically, Kac, his colleague of many years, had played a crucial, if indirect, role in the earliest development of the field, especially that associated with the Centre de recherches mathematiques (CRM). Despite the fact that Kac had no personal research interest in the field, his encouragement of a number of junior mathematicians, and his role, intentional or not, in bringing researchers together, recur as important influences in several aspects of computational biology, and explain why I dedicate this article to his memory. In presenting some of these thoughts to the DIMACS workshop, focusing on the early 1970s, my understanding of this period was broadened by comments from a number of participants, particularly Jerrold Griggs and Pavel Pevzner, and clarified by the presentation immediately
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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 | 0.000 | 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