FusionLearn: a biomarker selection algorithm on cross-platform data
Bibliographic record
Abstract
MOTIVATION: In high dimensional genetic data analysis, the objective is to select important biomarkers which are involved in some biological processes, such as disease progression, immune response, etc. The experimental data are often collected from different platforms including microarray experiments and proteomic experiments. The conventional single-platform approach lacks the capability to learn from multiple platforms, and the resulted lists of biomarkers vary across different platforms. There is a great need to develop an algorithm which can aggregate information across platforms and provide a consolidated list of biomarkers across different platforms. RESULTS: In this paper, we introduce an R package FusionLearn, which implements a fusion learning algorithm to analyze cross-platform data. The consolidated list of biomarkers is selected by the technique of group penalization. We first apply the algorithm on a collection of breast cancer microarray experiments from the NCBI (National Centre for Biotechnology Information) microarray database and the resulted list of selected genes have higher classification accuracy rate across different datasets than the lists generated from each single dataset. Secondly, we use the software to analyze a combined microarray and proteomic dataset for the study of the growth phase versus the stationary phase in Streptomyces coelicolor. The selected biomarkers demonstrate consistent differential behavior across different platforms. AVAILABILITY AND IMPLEMENTATION: R package: https://cran.r-project.org/package=FusionLearn.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".