Simple and rewireable biomolecular building blocks for DNA machine-learning algorithms
Bibliographic record
Abstract
ABSTRACT Deep learning algorithms, such as neural networks, enable the processing of complex datasets with many related variables, and have applications in disease diagnosis, cell profiling, and drug discovery. Beyond its use in electronic computers, neural networks have been implemented using programmable biomolecules such as DNA. This confers unique advantages such as greater portability, ability to operate without electricity, and direct analysis of patterns of biomolecules in solution. Analogous to past bottlenecks in electronic computers, the computing power of DNA-based neural networks is limited by the ability to add more computing units, i.e. neurons. This limitation exists because current architectures require many nucleic acids to model a single neuron. Each addition of a neuron to the network compounds existing problems such as long assembly times, high background signal, and cross-talk between components. Here we test three strategies to solve this limitation and improve the scalability of DNA-based neural networks: (i) enzymatic synthesis to generate high-purity neurons, (ii) spatial patterning of neuron clusters based on their network position, and (iii) encoding neuron connectivity on a universal single-stranded DNA backbone. We show that neurons implemented via these strategies activate quickly, with high signal-to-background ratio, and respond to varying input concentrations and weights. Using this neuron design, we implemented basic neural network motifs such as cascading, fan-in, and fan-out circuits. Since this design is modular, easy to synthesize, and compatible with multiple neural network architectures, we envision it will help scale DNA-based neural networks in a variety of settings. This will enable portable computing power for applications such as portable diagnostics, compact data storage, and autonomous decision making for lab-on-a-chips.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".