ArticleBiomedical signal processing and control2023
Identification and classification of coronavirus genomic signals based on linear predictive coding and machine learning methods.
Article in Biomedical signal processing and control, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed, 18 citations in OpenAlex.
- EIOFX-DT: Leveraging graph centrality metrics for feature extraction and classification of viral genetic sequences.Biotechnology reports (Amsterdam, Netherlands) · 2026Article
- Hybrid deep learning framework for accurate classification of high dimensional genomic data.Scientific reports · 2026Article
- PRCFX-DT: a new graph-based approach for feature selection and classification of genomic sequences.BMC bioinformatics · 2025Article
- COVID-19 infection segmentation using hybrid deep learning and image processing techniques.Scientific reports · 2023Article
- A Study and Analysis of Disease Identification using Genomic Sequence Processing Models: An Empirical Review.Current genomics · 2023Review
- A hybrid deep learning approach for COVID-19 detection based on genomic image processing techniques.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Corona disease has become one of the problems and challenges of humankind over the past two years. One of the problems that existed from the first days of this epidemic was clinical symptoms similar to other infectious viruses such as colds and influenza. Therefore, diagnosis of this disease and its coping and treatment approaches is also been difficult. In this study, Attempts has been made to investigate the origin of this disease and the genetic structure of the virus leading to it. For this purpose, signal processing and linear predictive coding approaches were used which are widely used in data compression. A pattern recognition model was presented for the detection and separation of covid samples from the influenza virus case studies. This model, which was based on support vector machine classifier, was tested successfully on several datasets collected from different countries. The obtained results performed on all datasets by more than 98% accuracy. The proposed model, in addition to its good performance accuracy, can be a step forward in quantifying and digitizing medical big data information.
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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.