Machine Learning Facilitates Imputation of Gene Expression Levels across Multiple Environments
Bibliographic record
Abstract
Gene expression level reflects the active biological processes in a live cell. It is of great importance to quantify gene expression levels across multiple environments. However, for technical reasons, the expression level in some environments/strains of species may not be measured correctly because of sequence diversity or technical reasons in mRNA-seq, qPCR, or microarray. Therefore, it would be highly beneficial if we could infer the missing expression level from existing data, and this process of filling in such missing values is called imputation. Imputation is a very active field in machine learning, and many tech companies use imputation to infer customer preferences for products/movies, etc. Here we apply multiple state-of-the-art imputation methods and compare their performance in predicting gene expression levels across multiple environments. Using a multi-environment expression dataset of Saccharomyces cerevisiae across 13 environments, we randomly removed 5%, 20%, 50%, and 75% of the expression level from the dataset and applied various imputation methods to predict the missing values and use root mean squared error for comparison of model performances. We found that SVD works the best among the five methods, followed by KNN with five nearest neighbors and KNN with two nearest neighbors. In contrast, univariate mean and univariate median works the worse and perform similarly. Although the latter two univariate methods were very commonly used in practice, our result highlights the benefit of using machine learning methods for imputation for better predictions of expression levels across environments.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".