ReviewComputational and structural biotechnology journal2018
A Review of Matched-pairs Feature Selection Methods for Gene Expression Data Analysis.
Review in Computational and structural biotechnology journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05911412 (Development of the Neuroimaging Biomarker-based Prediction Model of Anxiety-related Disorders and Novel Treatment Strategies), which is not on this map. Cited by 21 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.
Development of the Neuroimaging Biomarker-based Prediction Model of Anxiety-related Disorders and Novel Treatment Strategies: Effect of Mindfulness-based Cognitive Therapy Using Neuroscience on the Brain
Who cites it
21 citing papers in PubMed.
- Article
- A multi-classification deep neural network for cancer type identification from high-dimension, small-sample and imbalanced gene microarray data.Scientific reports · 2025Article
- penalizedclr: an R package for penalized conditional logistic regression for integration of multiple omics layers.BMC bioinformatics · 2024Article
- Uncovering hub genes and immunological characteristics for heart failure utilizing RRA, WGCNA and Machine learning.International journal of cardiology. Heart & vasculature · 2024Article
- Article
- Stable multivariate lesion symptom mapping.Aperture neuro · 2024Article
- Integration of Microarray Data and Single-Cell Sequencing Analysis to Explore Key Genes Associated with Macrophage Infiltration in Heart Failure.Journal of inflammation research · 2024Article
- Integrative analysis of bioinformatics and machine learning to identify cuprotosis-related biomarkers and immunological characteristics in heart failure.Frontiers in cardiovascular medicine · 2024Article
- Optimizing prognostic factors of five-year survival in gastric cancer patients using feature selection techniques with machine learning algorithms: a comparative study.BMC medical informatics and decision making · 2023Article
- Identification of mClinical epigenetics · 2023Article
- SVM-DO: identification of tumor-discriminating mRNA signatures via support vector machines supported by Disease Ontology.Turkish journal of biology = Turk biyoloji dergisi · 2023Article
- A Computational Approach to Identification of Candidate Biomarkers in High-Dimensional Molecular Data.Diagnostics (Basel, Switzerland) · 2022Article
- Algorithmic Stability and Generalization of an Unsupervised Feature Selection Algorithm.Advances in neural information processing systems · 2021Article
- NonLoss: a novel analytical method for differential biological module identification from single-cell transcriptome.Annals of translational medicine · 2021Article
- Augmented sequence features and subcellular localization for functional characterization of unknown protein sequences.Medical & biological engineering & computing · 2021Article
- Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology.International journal of molecular sciences · 2021Review
- NonClasGP-Pred: robust and efficient prediction of non-classically secreted proteins by integrating subset-specific optimal models of imbalanced data.Microbial genomics · 2020Article
- Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction.PloS one · 2020Article
- A Bioinformatics Tool for the Prediction of DNA N6-Methyladenine Modifications Based on Feature Fusion and Optimization Protocol.Frontiers in bioengineering and biotechnology · 2020Article
- Developing a Multi-Layer Deep Learning Based Predictive Model to Identify DNA N4-Methylcytosine Modifications.Frontiers in bioengineering and biotechnology · 2020Article
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
With the rapid accumulation of gene expression data from various technologies, e.g., microarray, RNA-sequencing (RNA-seq), and single-cell RNA-seq, it is necessary to carry out dimensional reduction and feature (signature genes) selection in support of making sense out of such high dimensional data. These computational methods significantly facilitate further data analysis and interpretation, such as gene function enrichment analysis, cancer biomarker detection, and drug targeting identification in precision medicine. Although numerous methods have been developed for feature selection in bioinformatics, it is still a challenge to choose the appropriate methods for a specific problem and seek for the most reasonable ranking features. Meanwhile, the paired gene expression data under matched case-control design (MCCD) is becoming increasingly popular, which has often been used in multi-omics integration studies and may increase feature selection efficiency by offsetting similar distributions of confounding features. The appropriate feature selection methods specifically designed for the paired data, which is named as matched-pairs feature selection (MPFS), however, have not been maturely developed in parallel. In this review, we compare the performance of 10 feature-selection methods (eight MPFS methods and two traditional unpaired methods) on two real datasets by applied three classification methods, and analyze the algorithm complexity of these methods through the running of their programs. This review aims to induce and comprehensively present the MPFS in such a way that readers can easily understand its characteristics and get a clue in selecting the appropriate methods for their analyses.
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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.