Stable feature selection for biomarker discovery
Feature selection techniques have been used as the workhorse in biomarker discovery
applications for a long time. Surprisingly, the stability of feature selection with respect to …
applications for a long time. Surprisingly, the stability of feature selection with respect to …
Challenges in biomarker discovery: combining expert insights with statistical analysis of complex omics data
JE McDermott, J Wang, H Mitchell… - Expert opinion on …, 2013 - Taylor & Francis
Introduction: The advent of high throughput technologies capable of comprehensive
analysis of genes, transcripts, proteins and other significant biological molecules has …
analysis of genes, transcripts, proteins and other significant biological molecules has …
Application of biological domain knowledge based feature selection on gene expression data
In the last two decades, there have been massive advancements in high throughput
technologies, which resulted in the exponential growth of public repositories of gene …
technologies, which resulted in the exponential growth of public repositories of gene …
Review of feature selection approaches based on grouping of features
With the rapid development in technology, large amounts of high-dimensional data have
been generated. This high dimensionality including redundancy and irrelevancy poses a …
been generated. This high dimensionality including redundancy and irrelevancy poses a …
CogNet: classification of gene expression data based on ranked active-subnetwork-oriented KEGG pathway enrichment analysis
Most of the traditional gene selection approaches are borrowed from other fields such as
statistics and computer science, However, they do not prioritize biologically relevant genes …
statistics and computer science, However, they do not prioritize biologically relevant genes …
GediNET for discovering gene associations across diseases using knowledge based machine learning approach
The most common approaches to discovering genes associated with specific diseases are
based on machine learning and use a variety of feature selection techniques to identify …
based on machine learning and use a variety of feature selection techniques to identify …
TextNetTopics: text classification based word grouping as topics and topics' scoring
M Yousef, D Voskergian - Frontiers in Genetics, 2022 - frontiersin.org
Medical document classification is one of the active research problems and the most
challenging within the text classification domain. Medical datasets often contain massive …
challenging within the text classification domain. Medical datasets often contain massive …
PriPath: identifying dysregulated pathways from differential gene expression via grouping, scoring, and modeling with an embedded feature selection approach
Background Cell homeostasis relies on the concerted actions of genes, and dysregulated
genes can lead to diseases. In living organisms, genes or their products do not act alone but …
genes can lead to diseases. In living organisms, genes or their products do not act alone but …
miRdisNET: discovering microRNA biomarkers that are associated with diseases utilizing biological knowledge-based machine learning
During recent years, biological experiments and increasing evidence have shown that
microRNAs play an important role in the diagnosis and treatment of human complex …
microRNAs play an important role in the diagnosis and treatment of human complex …
Over-optimism in bioinformatics: an illustration
M Jelizarow, V Guillemot, A Tenenhaus… - …, 2010 - academic.oup.com
Motivation: In statistical bioinformatics research, different optimization mechanisms
potentially lead to 'over-optimism'in published papers. So far, however, a systematic critical …
potentially lead to 'over-optimism'in published papers. So far, however, a systematic critical …