Gene selection from microarray data for cancer classification - a machine learning approach

By Yu Wang, Igor V. Tetko, Mark A. Hall, Eibe Frank, Axel Facius, Klaus F. X. Mayer, Hans-Werner Mewes. Comput. Biol. Chem., 2005

438 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c82e435ce579eeec4d839f24c13a50749377076a

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Citations in the corpus

A review of feature selection techniques in bioinformatics (2007, methodology): “Different types of population-based randomized heuristics are used as search engines in the major part of these papers: genetic algorithms (Li et al ., 2004; Petricoin et al ., 2002), particle swarm (Bhanot et al ., 2006) Peak Probability Contrast (Tibshirani et al ., 2004) Kolmogorov-Smirnov test (”
A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data (2015, methodology): “The difference between CFS and other methods is that it provides a “ heuristic merit ” for a feature subset instead of each feature independently [18].”
Discriminative prototype selection methods for graph embedding (2013, background): “Selecting informative prototypes from the underlying graph domain plays a vital role in graph embedding [36].”
A review of microarray datasets and applied feature selection methods (2014, methodology): “Apart from the application of multivariate filter methods [34,126,121,45], the microarray problem was also treated with more complex techniques such as wrappers and embedded methods [22,63,60,97].”
Using deep learning to enhance cancer diagnosis and classication (2013, result): “In (Wang et al., 2005b), Wang et al. showed that by combining a correlation-based feature selector with diﬀerent classiﬁcation approaches, it is possible to select relevant genes with high conﬁdence and obtain good classiﬁcation accuracy compared with other methods.”
A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis (2012, methodology): “As the search space might be too large for an exhaustive search, typically heuristic search algorithms are used for the optimization [54], [55].”
Review on Studies and Advances of Machine Learning Approaches (2013, background): “After the developments in recent years, SVM has become a hot spot of machine learning, and has been successfully applied in many fields [11-15].”