A parallel graph-based approach for protein sequence motif discovery
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
This thesis pro','ides a parallel, graph based approach to discover conservecl regons such as motifs n Protein sequences.The motif discover.vproblem has gainecl lot of significa'ce i'r biologlcal scence o'er the past decade.Recently, r,arious approaches have been used srrccessl,lll' to discorer motifs.some of theur are basecl on proba- rilistic appr-oach and the otirers on a combinatorial approach.Tiris thesis rolou,s a graph-based approach to solve this problern, in partcular.using the icea of de Bruijr gaphs The de Bruij'graph has been successfully aroptec i. rle past to solve prob- lens such as locai multiple aligrment and DNA flagment assembly.The proposed algorithni hanesses the power of the de Bruju graph to <scover the corserred re- gior.rs in a protei sequence.The sequentia.lalgorithm has z0% matcires of the rrotil.s*ith the r'fEME and 65% patter'rratches with the Gibbs motif sampler.The algo- rthrn s redesigned and parallelized on the high perfbrma.ncecomputers a'ailable on the Wcstern Canada Reserr"h Grid (WestGricl).Perfbrmance analysis was urade on a pure distributed memorl' luachine using ol' message passing anc on a hybricl ra- chinr: using shared and clistributed access space.Experiments shou,ed that the hyrrid i.rpementation runs 3 times a"s fast s the pure distributec memorv implenentafion.this thcsjs.I rvould lke to thank m1' supcrvisor Dr. Parinaa Thulasiranan for her continuous guidar:rce and encouragenent to develop tris rvork.We acknou'erge the partial support fiom Natural Science and Engineering R.e- search Councl(NSERC) of Canada.\tly sincere thanks to the managenent of \\:cstgricl lbr allowing
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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