Computational Prediction of N-linked Glycosylation Sites on Plant Proteins
Bibliographic record
Abstract
Glycosylation is an important form of protein post-translational modification where a glycan is attached to a protein via an enzymatic process.Experimental verification of glycosylation using wet lab techniques is expensive and time-consuming.While a number of computational prediction tools are available, none are trained using plant proteins.Since the mechanisms of glycosylation in plant and animal cells are known to differ, there is a need to develop a plantspecific predictor.In this thesis, we create such predictors of N-linked glycosylation using support vector machines and binary profile patterns derived from protein sequence windows as input feature data.The final classifier achieves a recall of 80.0% and 79.0% precision, as measured using a 10-fold cross-validation test.Our plant-specific classifier is more accurate on plant proteins than are other classifiers developed here and elsewhere.Finally, we have developed a web server to make the tool available to the research community.First and foremost, I would like to offer my special appreciation to my supervisor, Dr. James Green from the Department of Systems and Computer Engineering for all your continuous support and guidance in all stages of this thesis research.Dr. James Green trained me in the biological and bioinformatics fields, which helped me to have a clear objective of my project.He provided all required resources and tools as he could; that benefited me a lot, and helped me to steer in the right direction.I do strongly appreciate your support in the programming part of this research by providing server, database, coding and the necessary source codes that I could use in my own programming.Dr. James Green helped me to find and install the appropriate tool for training my classifier as well as debugging.I learned many programming skills from him.I would also like to thank you for all your brilliant comments, insightful idea, suggestions, patience and valuable supervision during my MASc.studies.More greatly, your encouragement and enthusiasm in this research helped me to improve my skills in bioinformatics and biological sciences.I would like to thank all my committee members for their warm encouragement and insightful comments that let me to grow as a
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".