ArticleEnvironmental technology & innovation2021
Viral reverse engineering using Artificial Intelligence and big data COVID-19 infection with Long Short-term Memory (LSTM).
Article in Environmental technology & innovation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Computer-aided analysis of radiological images for cancer diagnosis: performance analysis on benchmark datasets, challenges, and directions.EJNMMI reports · 2024Review
- Long-COVID diagnosis: From diagnostic to advanced AI-driven models.European journal of radiology · 2022Review
- Microscopic segmentation and classification of COVID-19 infection with ensemble convolutional neural network.Microscopy research and technique · 2022Article
- An intelligence design for detection and classification of COVID19 using fusion of classical and convolutional neural network and improved microscopic features selection approach.Microscopy research and technique · 2021Article
- Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19.Pathogens (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This research presents a reverse engineering approach to discover the patterns and evolution behavior of SARS-CoV-2 using AI and big data. Accordingly, we have studied five viral families (
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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.