ArticlePloS one2018
RETRACTED: An improved DBSCAN algorithm based on cell-like P systems with promoters and inhibitors.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Retraction: An improved DBSCAN algorithm based on cell-like P systems with promoters and inhibitors.PloS one · 2026Article
- Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis.Journal of cheminformatics · 2023Article
- Machine Learning Prediction of Adenovirus D8 Conjunctivitis Complications from Viral Whole-Genome Sequence.Ophthalmology science · 2022Article
- Spiking Neural P Systems with Membrane Potentials, Inhibitory Rules, and Anti-Spikes.Entropy (Basel, Switzerland) · 2022Article
Corrections and comments
- Retraction · 2026-03-19Compromised Peer Review · Concerns/Issues about Peer Review · · See also: https://pubpeer.com/publications/635F9E65EC87A378565D9AB83BC092
- Retracted
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Density-based spatial clustering of applications with noise (DBSCAN) algorithm can find clusters of arbitrary shape, while the noise points can be removed. Membrane computing is a novel research branch of bio-inspired computing, which seeks to discover new computational models/framework from biological cells. The obtained parallel and distributed computing models are usually called P systems. In this work, DBSCAN algorithm is improved by using parallel evolution mechanism and hierarchical membrane structure in cell-like P systems with promoters and inhibitors, where promoters and inhibitors are utilized to regulate parallelism of objects evolution. Experiment results show that the proposed algorithm performs well in big cluster analysis. The time complexity is improved to O(n), in comparison with conventional DBSCAN of O(n2). The results give some hints to improve conventional algorithms by using the hierarchical framework and parallel evolution mechanism in membrane computing models.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.