Bio-Inspired Generative Network with Knowledge Integration
Bibliographic record
Abstract
Generating realistic synthetic gene expression data that captures the complex interdependencies and biological context of cellular systems remains a significant challenge. Existing methods often struggle to reproduce intricate co-expression patterns and incorporate prior biological knowledge effectively. To address these limitations, we propose BioGen-KI, a novel bio-inspired generative network with knowledge integration. Our framework leverages a hybrid deep learning architecture that integrates embeddings learned from biological knowledge graphs (e.g., gene regulatory networks, pathway databases) with a conditional generative adversarial network (cGAN). The knowledge graph embeddings guide the generator to produce synthetic expression profiles that respect known biological relationships, while conditioning on contextual information (e.g., cell type, experimental condition) allows for targeted data synthesis. Furthermore, we introduce a biologically informed discriminator that evaluates not only the statistical realism but also the biological plausibility of the generated data, encouraging the preservation of pathway coherence and relevant gene interactions. We demonstrate the efficacy of BioGen-KI by generating synthetic gene expression datasets that exhibit improved statistical similarity to real data and, critically, better preservation of biologically meaningful relationships compared to baseline GAN models and methods relying solely on statistical characteristics. Evaluation on downstream tasks, such as clustering and differential gene expression analysis, highlights the utility of BioGen-KI-generated data for enhancing the robustness and interpretability of biological data analysis. This work presents a significant step towards generating more biologically faithful synthetic gene expression data for research and development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".