ArticleGenomics, proteomics & bioinformatics2007
A modified T-test feature selection method and its application on the HapMap genotype data.
Article in Genomics, proteomics & bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
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- A novel biomarker selection method combining graph neural network and gene relationships applied to microarray data.BMC bioinformatics · 2022Article
- Novel Hypertrophic Cardiomyopathy Diagnosis Index Using Deep Features and Local Directional Pattern Techniques.Journal of imaging · 2022Article
- Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review.Frontiers in molecular neuroscience · 2022Review
- Challenges of deep learning methods for COVID-19 detection using public datasets.Informatics in medicine unlocked · 2022Article
- Cancer Detection and Prediction Using Genetic Algorithms.Computational intelligence and neuroscience · 2022Article
- ILRC: a hybrid biomarker discovery algorithm based on improved L1 regularization and clustering in microarray data.BMC bioinformatics · 2021Article
- Discrimination of simultaneous psychological and physical stressors using wristband biosignals.Computer methods and programs in biomedicine · 2021Article
- An ensemble machine learning model based on multiple filtering and supervised attribute clustering algorithm for classifying cancer samples.PeerJ. Computer science · 2021Article
- Using machine learning to quantify structural MRI neurodegeneration patterns of Alzheimer's disease into dementia score: Independent validation on 8,834 images from ADNI, AIBL, OASIS, and MIRIAD databases.Human brain mapping · 2020Article
- Automated Detection of Alzheimer's Disease Using Brain MRI Images- A Study with Various Feature Extraction Techniques.Journal of medical systems · 2019Article
- Pancreatic cancer biomarker detection by two support vector strategies for recursive feature elimination.Biomarkers in medicine · 2019Article
- Model-free feature screening for categorical outcomes: Nonlinear effect detection and false discovery rate control.PloS one · 2019Article
- The association between "Brain-Age Score" (BAS) and traditional neuropsychological screening tools in Alzheimer's disease.Brain and behavior · 2018Article
- Exploring diagnosis and imaging biomarkers of Parkinson's disease via iterative canonical correlation analysis based feature selection.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2018Article
- Applying Pattern Recognition to High-Resolution Images to Determine Cellular Signaling Status.IEEE transactions on nanobioscience · 2017Article
- Automated identification of dementia using FDG-PET imaging.BioMed research international · 2014Article
- Risk estimation and risk prediction using machine-learning methods.Human genetics · 2012Review
- Modeling effects of human single nucleotide polymorphisms on protein-protein interactions.Biophysical journal · 2009Article
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Abstract
Single nucleotide polymorphisms (SNPs) are genetic variations that determine the differences between any two unrelated individuals. Various population groups can be distinguished from each other using SNPs. For instance, the HapMap dataset has four population groups with about ten million SNPs. For more insights on human evolution, ethnic variation, and population assignment, we propose to find out which SNPs are significant in determining the population groups and then to classify different populations using these relevant SNPs as input features. In this study, we developed a modified t-test ranking measure and applied it to the HapMap genotype data. Firstly, we rank all SNPs in comparison with other feature importance measures including F-statistics and the informativeness for assignment. Secondly, we select different numbers of the most highly ranked SNPs as the input to a classifier, such as the support vector machine, so as to find the best feature subset corresponding to the best classification accuracy. Experimental results showed that the proposed method is very effective in finding SNPs that are significant in determining the population groups, with reduced computational burden and better classification accuracy.
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