15 papers · ranked by Valyu relevance
D’Ó’ Reilly
Feature selection prevents over-fitting in predictive models. This study aimed to present an effective feature selection method that leads to a reliable classification of fall-risk in older subjects using static force-platform data across four conditions only. 528 features were generated from a publicly available…
Michail Tsagris, Zacharias Papadovasilakis, Kleanthi Lakiotaki, Ioannis Tsamardinos
Feature selection seeks to identify a minimal-size subset of features that is maximally predictive of the outcome of interest. It is particularly important for biomarker discovery from high-dimensional molecular data, where the features could correspond to gene expressions, Single Nucleotide Polymorphisms (SNPs)…
Ngan Thi Dong, Megha Khosla
The identification of biomarkers or predictive features that are indicative of a specific biological or disease state is a major research topic in biomedical applications. Several feature selection(FS) methods ranging from simple univariate methods to recent deep-learning methods have been proposed to select a minimal…
Tümay Capraz, Wolfgang Huber
A fundamental step in many analyses of high-dimensional data is dimension reduction. Two basic approaches are introduction of new, synthetic coordinates, and selection of extant features. Advantages of the latter include interpretability, simplicity, transferability and modularity. A common criterion for unsupervised…
Minzhe Zhang, Xiao Yang
Feature selection in high-dimensional biological data, where the number of features far exceeds the number of samples, has long posed a significant methodological challenge. This study evaluates two recently developed feature selection methods, Stabl and Nullstrap, under a simulation framework designed to replicate…
Rahi Jain, Wei Xu
Feature selection is important in high dimensional data analysis. The wrapper approach is one of the ways to perform feature selection, but it is computationally intensive as it builds and evaluates models of multiple subsets of features. The existing wrapper approaches primarily focus on shortening the path to find an…
Karan Uppal, Eva K. Lee
Recent studies have shown that the ensemble feature selection approaches are essential for generating robust classifiers. Existing methods for aggregating feature lists from different methods require use of arbitrary thresholds for selecting the top ranked features and do not account for classification accuracy while…
Rahi Jain, Wei Xu
Feature selection (FS) reduces the dimensions of high dimensional data. Among many FS approaches, ensemble-based feature selection (EFS) is one of the commonly used approaches. The rank aggregation (RA) step influences the feature selection of EFS. Currently, the EFS approach relies on using a single RA algorithm to…
Damir Zhakparov, Kathleen Moriarty, Damian Roqueiro, Katja Baerenfaller
High-dimensional Bulk RNA sequencing (RNAseq) datasets pose a considerable challenge in identifying biologically relevant features for downstream analyses and data mining efforts. The standard approach involves differential gene expression (DGE) analysis, but its effectiveness can be limited depending on the data due…
Adel Mehrpooya, Farid Saberi-Movahed, Najmeh Azizizadeh, Mohammad Rezaei-Ravari + 3 more
The extraction of predictive features from the complex high-dimensional multi-omic data is necessary for decoding and overcoming the therapeutic responses in systems pharmacology. Developing computational methods to reduce high-dimensional space of features in in vitro, in vivo and clinical data is essential to…
Insha Ullah, Kerrie Mengersen, Anthony Pettitt, Benoit Liquet
High-dimensional datasets, where the number of variables ‘p’ is much larger compared to the number of samples ‘n’, are ubiquitous and often render standard classification and regression techniques unreliable due to overfitting. An important research problem is feature selection — ranking of candidate variables based on…
Daniel Rawlinson, Chenxi Zhou, Kim-Anh Lê Cao, Lachlan J.M. Coin
Application of transcriptomics, proteomics and metabolomics technologies to clinical cohorts has uncovered a variety of signatures for predicting disease. Many of these signatures require the full ‘omics data for evaluation on unseen samples, either explicitly or implicitly through library size normalisation.…
Erik D. VonKaenel, Lisa M. Bramer, Javier E. Flores, Thomas O Metz + 2 more
In recent years, high dimensional omics analyses have become more commonplace for investigating complex biological systems. Typically, these studies attempt to identify key biomolecules associated with a particular biological process. Often, machine learning (ML) is used to identify these biomolecules, typically by…
Rahi Jain, Wei Xu
Feature selection (FS) is critical for high dimensional data analysis. Ensemble based feature selection (EFS) is a commonly used approach to develop FS techniques. Rank aggregation (RA) is an essential step of EFS where results from multiple models are pooled to estimate feature importance. However, the literature…
Majid Mohammadi, Hossein Sharifi Noghabi, Ghosheh Abed Hodtani, Habib Rajabi Mashhadi
One of the central challenges in cancer research is identifying significant genes among thousands of others on a microarray. Since preventing outbreak and progression of cancer is the ultimate goal in bioinformatics and computational biology, detection of genes that are most involved is vital and crucial. In this…