Software Suite for Gene and Protein Annotation Prediction and Similarity Search
Bibliographic record
Abstract
In the computational biology community, machine learning algorithms are key instruments for many applications, including the prediction of gene-functions based upon the available biomolecular annotations. Additionally, they may also be employed to compute similarity between genes or proteins. Here, we describe and discuss a software suite we developed to implement and make publicly available some of such prediction methods and a computational technique based upon Latent Semantic Indexing (LSI), which leverages both inferred and available annotations to search for semantically similar genes. The suite consists of three components. BioAnnotationPredictor is a computational software module to predict new gene-functions based upon Singular Value Decomposition of available annotations. SimilBio is a Web module that leverages annotations available or predicted by BioAnnotationPredictor to discover similarities between genes via LSI. The suite includes also SemSim, a new Web service built upon these modules to allow accessing them programmatically. We integrated SemSim in the Bio Search Computing framework (http://www.bioinformatics.deib. polimi.it/bio-seco/seco/), where users can exploit the Search Computing technology to run multi-topic complex queries on multiple integrated Web services. Accordingly, researchers may obtain ranked answers involving the computation of the functional similarity between genes in support of biomedical knowledge discovery.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.038 |
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".