SFWN: A Novel Semi-Supervised Feature Weighted Neural Network for Gene Data Feature Learning and Mining With Graph Modeling
Bibliographic record
Abstract
Gene expression data can serve for analyzing the genes with changed expressions, the correlation between genes and the influence of different circumstance on gene activities. However, labeling a large number of gene expression data is laborious and time-consuming. The insufficient labeled data pose a challenge to construct the deep learning model. Currently, some graph neural networks (GNN) based on semi-supervised learning mechanism only focus on the feature space and sample space of gene expression data, possibly affecting the accuracy. This article puts forward a novel semi-supervised graph neural network model (SFWN). Firstly, we use the external knowledge of gene expression data for constructing a feature graph, a similarity kernel, and a sample graph for the first time. Later, a novel semi-supervised learning algorithm (SGA) is proposed to extract the data relationship and obtain the global sample structure better. A graph sparse module (SGCN) is also proposed to process sparse representation with gene expression data classification. To overcome the over smoothing problem, a new feature calculation method based on two spaces is proposed to feature representation analysis and calculation in this model. According to a lot of experiments and ablation studies conducted on several public datasets, SFWN exhibits a better effect and is superior to the state-of-the-art approaches (the accuracy and F1-Score are 0.9993 and 0.9899, respectively). Experimental results showed that the proposed SFWN model has strong gene expression feature learning and representation ability, and may provide a new insight and tool for relevant disease diagnosis and clinic practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".