ArticleMedical & biological engineering & computing2019
Genetic algorithm based cancerous gene identification from microarray data using ensemble of filter methods.
Article in Medical & biological engineering & computing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Improving bank customer churn prediction with feature reduction using GA.Scientific reports · 2025Article
- The potential role of machine learning and deep learning in differential diagnosis of Alzheimer's disease and FTD using imaging biomarkers: A review.The neuroradiology journal · 2025Review
- MPGH-FS: A Hybrid Feature Selection Framework for Robust Multi-Temporal OBIA Classification.Sensors (Basel, Switzerland) · 2025Article
- Enhanced leukemia prediction using hybrid ant colony and ant lion optimization for gene selection and classification.MethodsX · 2025Article
- Adaptive genetic algorithm based deep feature selector for cancer detection in lung histopathological images.Scientific reports · 2025Article
- Article
- Article
- Article
- A systematic literature review on meta-heuristic based feature selection techniques for text classification.PeerJ. Computer science · 2024Article
- Classification of cancer cells and gene selection based on microarray data using MOPSO algorithm.Journal of cancer research and clinical oncology · 2023Article
- Human activity recognition from sensor data using spatial attention-aided CNN with genetic algorithm.Neural computing & applications · 2023Article
- Binary Simulated Normal Distribution Optimizer for feature selection: Theory and application in COVID-19 datasets.Expert systems with applications · 2022Article
- Improving the generalization of unsupervised feature learning by using data from different sources on gene expression data for cancer diagnosis.Medical & biological engineering & computing · 2022Article
- Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification.Medical & biological engineering & computing · 2022Article
- Optimal Deep Learning Enabled Prostate Cancer Detection Using Microarray Gene Expression.Journal of healthcare engineering · 2022Article
- A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification.Sensors (Basel, Switzerland) · 2021Article
- A weighted ensemble-based active learning model to label microarray data.Medical & biological engineering & computing · 2020Article
- A two-stage approach towards protein secondary structure classification.Medical & biological engineering & computing · 2020Article
- Machine Learning Based Computational Gene Selection Models: A Survey, Performance Evaluation, Open Issues, and Future Research Directions.Frontiers in genetics · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microarray datasets play a crucial role in cancer detection. But the high dimension of these datasets makes the classification challenging due to the presence of many irrelevant and redundant features. Hence, feature selection becomes irreplaceable in this field because of its ability to remove the unrequired features from the system. As the task of selecting the optimal number of features is an NP-hard problem, hence, some meta-heuristic search technique helps to cope up with this problem. In this paper, we propose a 2-stage model for feature selection in microarray datasets. The ranking of the genes for the different filter methods are quite diverse and effectiveness of rankings is datasets dependent. First, we develop an ensemble of filter methods by considering the union and intersection of the top-n features of ReliefF, chi-square, and symmetrical uncertainty. This ensemble allows us to combine all the information of the three rankings together in a subset. In the next stage, we use genetic algorithm (GA) on the union and intersection to get the fine-tuned results, and union performs better than the latter. Our model has been shown to be classifier independent through the use of three classifiers-multi-layer perceptron (MLP), support vector machine (SVM), and K-nearest neighbor (K-NN). We have tested our model on five cancer datasets-colon, lung, leukemia, SRBCT, and prostate. Experimental results illustrate the superiority of our model in comparison to state-of-the-art methods. Graphical abstract ᅟ.
Indexed as
Identifiers
30069674What 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.