ArticleBMC bioinformatics2016
McTwo: a two-step feature selection algorithm based on maximal information coefficient.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed.
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- An efficient, not-only-linear correlation coefficient based on clustering.Cell systems · 2024Article
- A Feature Selection Method Based on Graph Theory for Cancer Classification.Combinatorial chemistry & high throughput screening · 2024Article
- A new hybrid algorithm for three-stage gene selection based on whale optimization.Scientific reports · 2023Article
- A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer.Sensors (Basel, Switzerland) · 2023Article
- A novel hybrid algorithm based on Harris Hawks for tumor feature gene selection.PeerJ. Computer science · 2023Article
- csORF-finder: an effective ensemble learning framework for accurate identification of multi-species coding short open reading frames.Briefings in bioinformatics · 2022Article
- A multivariate multi-step LSTM forecasting model for tuberculosis incidence with model explanation in Liaoning Province, China.BMC infectious diseases · 2022Article
- i2APP: A Two-Step Machine Learning Framework For Antiparasitic Peptides Identification.Frontiers in genetics · 2022Article
- Cancer Detection and Prediction Using Genetic Algorithms.Computational intelligence and neuroscience · 2022Article
- Article
- A novel systematic approach for cancer treatment prognosis and its applications in oropharyngeal cancer with microRNA biomarkers.Bioinformatics (Oxford, England) · 2021Article
- EnRank: An Ensemble Method to Detect Pulmonary Hypertension Biomarkers Based on Feature Selection and Machine Learning Models.Frontiers in genetics · 2021Article
- Feature Selection of OMIC Data by Ensemble Swarm Intelligence Based Approaches.Frontiers in genetics · 2021Article
- Classifying Breast Cancer Subtypes Using Deep Neural Networks Based on Multi-Omics Data.Genes · 2020Article
- EnACP: An Ensemble Learning Model for Identification of Anticancer Peptides.Frontiers in genetics · 2020Article
- Construction of subtype-specific prognostic gene signatures for early-stage non-small cell lung cancer using meta feature selection methods.Oncology letters · 2019Article
- FeatureSelect: a software for feature selection based on machine learning approaches.BMC bioinformatics · 2019Article
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8 authors.
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Abstract
backgroundHigh-throughput bio-OMIC technologies are producing high-dimension data from bio-samples at an ever increasing rate, whereas the training sample number in a traditional experiment remains small due to various difficulties. This "large p, small n" paradigm in the area of biomedical "big data" may be at least partly solved by feature selection algorithms, which select only features significantly associated with phenotypes. Feature selection is an NP-hard problem. Due to the exponentially increased time requirement for finding the globally optimal solution, all the existing feature selection algorithms employ heuristic rules to find locally optimal solutions, and their solutions achieve different performances on different datasets.
resultsThis work describes a feature selection algorithm based on a recently published correlation measurement, Maximal Information Coefficient (MIC). The proposed algorithm, McTwo, aims to select features associated with phenotypes, independently of each other, and achieving high classification performance of the nearest neighbor algorithm. Based on the comparative study of 17 datasets, McTwo performs about as well as or better than existing algorithms, with significantly reduced numbers of selected features. The features selected by McTwo also appear to have particular biomedical relevance to the phenotypes from the literature.
conclusionMcTwo selects a feature subset with very good classification performance, as well as a small feature number. So McTwo may represent a complementary feature selection algorithm for the high-dimensional biomedical datasets.
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