• How Does Feature Selection Benefit Machine Learning Tasks?🔍
• Select Features for Classifying High|Dimensional Data🔍
• Tuning ReliefF for Genome|Wide Genetic Analysis🔍
• Feature selection revisited in the single|cell era🔍
• Ultra High|Dimensional Nonlinear Feature Selection for Big ...🔍
• Accurate and fast feature selection workflow for high|dimensional ...🔍
• Optimal features selection in the high dimensional data based on ...🔍
• Anti|correlated feature selection prevents false discovery of ...🔍

How Does Feature Selection Benefit Machine Learning Tasks?

Dimensionality reduction reduces the dimensions of features. By contrast, the set of features made by feature selection must be a subset of the original set of ...

Select Features for Classifying High-Dimensional Data - MathWorks

There are two main approaches to reducing features: feature selection and feature transformation. Feature selection algorithms select a subset of features from ...

Tuning ReliefF for Genome-Wide Genetic Analysis - Research

Moore, J.H.: Genome-wide analysis of epistasis using multifactor dimensionality reduction: feature selection and construction in the domain of human genetics.

Feature selection revisited in the single-cell era - arXiv

& An, L. Ensemble dimensionality reduction and feature gene extraction for single-cell rna-seq data. Nature Communications 11, 1–9 (2020). 88. Zhang, J. & Feng, ...

Computing, Algorithms & Genomics - Cross-Disciplinary Fellowships

This course explores this general framework and introduces several methods for dimensionality reduction, feature selection, and clustering.

Ultra High-Dimensional Nonlinear Feature Selection for Big ...

Although state-of-the-art feature selection algorithms, such as mini- mum redundancy maximum relevance (mRMR) [7], have been proven to be effective and ...

Seurat - Guided Clustering Tutorial - Satija Lab

Identification of highly variable features (feature selection) ... We next calculate a subset of features that exhibit high cell-to-cell variation ...

Accurate and fast feature selection workflow for high-dimensional ...

Including PCA and Recursive Feature Elimination (PCA-RFE-RF), we observed a strong feature reduction (7–10 components) and a better standard deviation (5.4).

Optimal features selection in the high dimensional data based on ...

Bio-informatics and gene expression analysis face major hurdles when dealing with high-dimensional data, where the number of variables or ...

Anti-correlated feature selection prevents false discovery of ... - Nature

l Boxplots indicating the total number of clusters identified by each method of feature selection (box colors) and clustering (noted in panels).

Feature Selection and Dimension Reduction for Single Cell RNA ...

Thus, many of the reads counted in scRNA-Seq are duplicates of a single mRNA molecule in the original cell [7]. Early scRNA-Seq studies using ...

Dimension reduction methods for microarray data - AIMS Press

Various feature selection and feature extraction techniques have been proposed in the literature to identify the genes, that have direct impact ...

A robust ensemble feature selection approach to prioritize genes ...

To address this challenge, researchers have developed feature selection methods to enhance performance, reduce overfitting, and ensure resource efficiency.

Feature Selection: A Data Perspective - ACM Digital Library

Feature selection, as a data preprocessing strategy, has been proven to be effective and efficient in preparing data (especially high-dimensional data) for ...

Feature selection for genomic signal processing - Princeton University

For many bioinformatic applications, data are represented as vectors of extremely high dimension. This motivates the research on feature selection. In the ...

Variable Selection and Supervised Dimension Reduction for Large ...

in high-dimensional genomic data in terms of both variable selection and dimension reduction. The methods in the previous section focused on variable selection ...

Erratum: Feature selection and dimension reduction for single-cell ...

Erratum: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model (Genome Biology (2019) 20 (295) DOI: 10.1186/s13059-019- ...

Chapter 4 Dimensionality reduction | Translational Bioinformatics ...

4.5 Feature selection and extraction for machine learning modeling ... There are roughly two groups of feature selection methods: wrapper methods and filter ...

Dimensionality Reduction - CiteSeerX

We introduce the field of dimensionality reduction by dividing it into two parts: feature extraction and feature selection. ... [7, 30, 31, 55]. See Figure ...

CSHL Computational Genomics 2023 - 7 Workshop - Stephanie Hicks

Feature selection using highly variable genes; Dimensionality reduction using PCA; Data visualization using tSNE or UMAP; Unsupervised clustering (your choice ...