Systematic mapping of DNAzymes research from 1995 to 2019
Bibliographic record
Abstract
DNAzymes (catalytic DNA) have gained significant diagnostic and therapeutic applications with increasing research output over the years. Functional oligonucleotides are used as molecular recognition elements within biosensors for detection of analytes and viral infections such as SARS-CoV-2. DNAzymes are also applied for silencing and regulating cancer specific genes. However, there has not been any report on systematic analysis to track research status, reveal hotspots, and map knowledge in this field. Therefore, in the present study, research articles on DNAzymes from 1995 to 2019 were extracted from Web of Science (SCI-Expanded) after which, 1037 articles were imported into Rstudio (version 3.6.2) and analysed accordingly. The highest number of articles was published in 2019 (n = 138), while the least was in 1995 (n = 1). The articles were published across 216 journals by 2344 authors with 2337 multi-author and 7 single authors. The most prolific authors were Li Y (n = 47), Liu J (n = 46), Wang L (n = 33), Willner I (n = 33) and Zhang L (n = 33). The top three most productive countries were China (n = 2018), USA (n = 447) and Canada (n = 251). The most productive institutions were Hunan University, China (n = 141), University of Illinois, USA (n = 139) and Fuzhou University, China (n = 101). Despite the increasing interest in this field, international collaborations between institutions were very low which requires immediate attention to mitigate challenges such as limited funding, access to facilities, and existing knowledge gap.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.145 | 0.123 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".