Systems Biology Approaches to Mining High Throughput Biological Data
Bibliographic record
Abstract
With advances in high throughput measurement techniques, large-scale biological data have been and will continuously be produced, for example, gene expression data, proteinprotein interaction (PPI) data, tandem mass spectra data, microRNA expression data, lncRNA expression data, and biomolecule-disease association data.Such data contain insightful information for understanding the mechanism of molecular biological systems and have proved useful in diagnosis, treatment, and drug design for genetic disorders or complex diseases.For this focus issue, we have invited the researchers to contribute original research articles which develop or improve systems biology approaches to mining high throughput biological data.With high throughput data, it is appealing to develop systems biology approaches to understand important biological processes.In the paper "Differential Expression Analysis in RNA-Seq by a Naive Bayes Classifier with Local Normalization," Y. Dou et al. developed a new tool for the identification of differentially expressed genes with RNA-Seq data, named GExposer.This tool introduced a local normalization algorithm to reduce the bias of nonrandomly positioned read depth.The Naive Bayes classifier was employed to integrate fold change, transcript length, and GCcontent to identify differentially expressed genes.Results on several independent tests showed that GExposer had better performance than other methods.In the paper "K-Profiles: A Nonlinear Clustering Method for Pattern Detection in High Dimensional Data," K. Wang et al. designed the nonlinear
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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 | 0.032 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".