ReviewJournal of healthcare engineering2021
Gene Expression-Assisted Cancer Prediction Techniques.
Review in Journal of healthcare engineering, 2021. 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 It has been retracted, and should not be counted.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 21 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
21 citing papers in PubMed.
- Article
- RETRACTED: Skin cancer segmentation and classification by implementing a hybrid FrCN-(U-NeT) technique with machine learning.PloS one · 2025Article
- Efficient lung cancer detection using computational intelligence and ensemble learning.PloS one · 2024Article
- Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Developing an Efficient Cancer Detection and Prediction Tool Using Convolution Neural Network Integrated with Neural Pattern Recognition.BioMed research international · 2023Article
- Retracted: Gene Expression-Assisted Cancer Prediction Techniques.Journal of healthcare engineering · 2023Article
- Appropriate Supervised Machine Learning Techniques for Mesothelioma Detection and Cure.BioMed research international · 2022Article
- Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network.BioMed research international · 2022Article
- Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task.BioMed research international · 2022Article
- Correlation-Based Mutual Information Model for Analysis of Lung Cancer CT Image.BioMed research international · 2022Article
- Deep-Learning-Based Cancer Profiles Classification Using Gene Expression Data Profile.Journal of healthcare engineering · 2022Article
- Multidimensional CNN-Based Deep Segmentation Method for Tumor Identification.BioMed research international · 2022Article
- Lung Cancer Classification and Prediction Using Machine Learning and Image Processing.BioMed research international · 2022Article
- Supervised Approach to Identify Autism Spectrum Neurological Disorder via Label Distribution Learning.Computational intelligence and neuroscience · 2022Article
- Lightweight Deep Learning Classification Model for Identifying Low-Resolution CT Images of Lung Cancer.Computational intelligence and neuroscience · 2022Article
- Multiresolution-Based Singular Value Decomposition Approach for Breast Cancer Image Classification.BioMed research international · 2022Article
- Feature Subset Selection with Optimal Adaptive Neuro-Fuzzy Systems for Bioinformatics Gene Expression Classification.Computational intelligence and neuroscience · 2022Article
- Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data.BioMed research international · 2022Article
- Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection.BioMed research international · 2022Article
- Attention Layer-Based Multidimensional Feature Extraction for Diagnosis of Lung Cancer.BioMed research international · 2022Article
Corrections and comments
- Retraction · 2023-10-11Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is one of the deadliest diseases and with its growing number, its detection and treatment become essential. Researchers have developed various methods based on gene expression. Gene expression is a process that is used to convert deoxyribose nucleic acid (DNA) to ribose nucleic acid (RNA) and then RNA to protein. This protein serves so many purposes, such as creating cells, drugs for cancer, and even hybrid species. As genes carry genetic information from one generation to another, some gene deformity is also transferred to the next generation. Therefore, the deformity needs to be detected. There are many techniques available in the literature to predict cancerous and noncancerous genes from gene expression data. This is an important development from the point of diagnostics and giving a prognosis for the condition. This paper will present a review of some of those techniques from the literature; details about the various datasets on which these techniques are implemented and the advantages and disadvantages.
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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.