AlignDx: Enabling Automated, Cloud-Based Workflows for Streamlined Bioinformatic-Focused Pathogen Surveillance
Bibliographic record
Abstract
The rising trends in infectious disease burden, alongside the recent COVID-19 pandemic, underline the need for effective public health disease mitigation strategies like pathogen surveillance. Improvements to surveillance systems can be realized by incorporating a variety of surveillance data sources such as comprehensive genomics and simpler point-of-care approaches. In this thesis, a novel bioinformatic-focused surveillance platform is presented for executing scientific workflows in cloud-based environments. The platform in question, AlignDx, addresses gaps in available surveillance systems via its modular component-based design providing security, workflow management, summary reports and data archiving. Two workflows were created and tested using this platform. First, a metagenomics next-generation sequencing workflow was developed for human pathogenic virus surveillance. Using a clinical nasopharyngeal RNA-seq test dataset, the workflow performed well in classification of severe acute respiratory syndrome coronavirus 2. Also, a lateral flow assay workflow was developed for mass automated point-of-care pathogen surveillance. Using an original test dataset of serially diluted LFA images, under controlled lighting, the workflow performed well in correctly classifying tests according to their manually curated results. Overall, the AlignDx platform is an effective system for automated surveillance applications and its constituent workflows are flexible and primed for further 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".