SVM based prediction of RNA‐binding proteins using binding residues and evolutionary information
Bibliographic record
Abstract
RNA-binding proteins (RBPs) play crucial role in transcription and gene-regulation. This paper describes a support vector machine (SVM) based method for discriminating and classifying RNA-binding and non-binding proteins using sequence features. With the threshold of 30% interacting residues, RNA-binding amino acid prediction method PPRINT achieved the Matthews correlation coefficient (MCC) of 0.32. BLAST and PSI-BLAST identified RBPs with the coverage of 32.63 and 33.16%, respectively, at the e-value of 1e-4. The SVM models developed with amino acid, dipeptide and four-part amino acid compositions showed the MCC of 0.60, 0.46, and 0.53, respectively. This is the first study in which evolutionary information in form of position specific scoring matrix (PSSM) profile has been successfully used for predicting RBPs. We achieved the maximum MCC of 0.62 using SVM model based on PSSM called PSSM-400. Finally, we developed different hybrid approaches and achieved maximum MCC of 0.66. We also developed a method for predicting three subclasses of RNA binding proteins (e.g., rRNA, tRNA, mRNA binding proteins). The performance of the method was also evaluated on an independent dataset of 69 RBPs and 100 non-RBPs (NBPs). An additional benchmarking was also performed using gene ontology (GO) based annotation. Based on the hybrid approach a web-server RNApred has been developed for predicting RNA binding proteins from amino acid sequences (http://www.imtech.res.in/raghava/rnapred/).
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".