Artificial neural network modeling of apoptosis in gammairradiated human lymphocytes
Bibliographic record
Abstract
PURPOSE: To develop an artificial neural network (ANN) model of apoptotic response in gamma irradiated human lymphocytes. To assess the feasibility of training ANN radiobiological models using data collected with flow cytometry. MATERIALS AND METHODS: Irradiated isolated human lymphocytes were labelled with Annexin V-Fluorescein Isothiocyanate (FITC) and 7-Amino-Actinomycin D (7AAD) then analysed using flow cytometry. Twenty-four dose responses per donor from 14 donors were collected from a flow cytometer and used in model development as the training and cross-validation datasets. The general ANN model architecture was a multi-layer perceptron using the mean squared error of a cross validation dataset as the objective function. The ANN model was optimized by varying the number of hidden layers and the number of processing elements per layer. The optimized model constituted of three hidden layers with 80, 40, and 10 hidden layers in the first, second, and third layers respectively. RESULTS: The optimized model was used to simulate dose responses at the training doses of 0, 2, 4 and 8 Gray. A strong agreement between the model and measured dose responses was observed. The model was also used to simulate a dose response at 0.1 Gray and results were compared to the measured dose response from a donor not used in model development. Again, strong agreement between the model and the observed dose response was found. CONCLUSIONS: This study shows that artificial neural networks can be trained to provide high resolution, high accuracy models of multivariate radiobiological data collected by flow cytometry.
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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 | 0.001 | 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".