Unsupervised Idealization of Nano-Electronic Sensors Recordings with Concept Drifts: A Compressive Feature Learning Approach for Non-Stationary Single-Molecule Data Analysis
Bibliographic record
Abstract
Single-molecule nanocircuits based on field-effect transistors (smFETs) are emerging and promising nano-bioelectronic sensors for the functional detection of molecular dynamics involved in biochemical transformations, in particular for applications in cancer thanks to a potentially better understanding of some hidden and complex molecular interactions. In fact, functionalized carbon nanotubes have been recently exploited to probe molecular events occurring at a single molecule scale with ultra high sensitivity and specificity, such as nucleic acids hybridization, enzyme folding in catalysis reactions, or protein-nucleic acids interactions. Extracting the kinetics and thermodynamics from such single-molecule dynamics implies robust analytic tools that can handle the complexity of the sensed reaction system changing between transient and steady-state molecular conformations, but also some challenging signal specificities, such as the multi-source composition of the recorded signals, the mixed and high-level noises, and the sensor baseline drift, leading to non-stationary time series. We present a new smFET data analysis framework, based on a compressive feature learning scheme to optimize unsupervised idealization of smFET traces, by a precise and accurate molecular events detection and states characterization algorithm, tailored for non-stationary signals at high sampling rate and long acquisition periods, without any prior knowledge on the data generating process nor signal pre-filtering. Experimental results show the accuracy and robustness of our trace idealization algorithm to stochastic state-space models, and better performances than commonly used hidden Markov models.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".