A biomarker identification model from protein protein interaction network using natural language processing and graph convolutional network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A biomarker identification model, integrating natural language processing (NLP) and graph convolutional neural network (GCN), offers a novel approach to enhance a simple neural network’s ability to capture the contextual semantics of genes and extract spatial feature information by utilizing gene ontology (GO) annotations. First, we explore gene expression datasets to identify differentially expressed genes (DEGs) and construct a protein-protein interaction (PPI) network. By employing Word2Vec, an NLP algorithm, for vectorizing GO annotations, our model reveals complex biological relationships among genes. GO annotations are crucial as they provide comprehensive information about gene functions, biological processes, and cellular components, thus augmenting our understanding of how genes interact within the network. Integrating multi-layered GCN facilitates effective learning of complex semantic relations and spatial feature information within the PPI network. Experiments on publicly available datasets of Glioblastoma Multiforme (GBM), the most aggressive form of brain tumour, demonstrate that our model significantly enhances biomarker identification compared to existing state-of-the-art methods, showcasing its potential for advancing GBM research and clinical decision-making.
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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 | 0.000 | 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 it