Incremental wrapper-based gene selection from microarray data for cancer classification

By Roberto Ruiz Sánchez, José Cristóbal Riquelme Santos, J. Aguilar–Ruiz. Pattern Recognition, 2006

903 citations recorded by the source corpus.

Gene expression microarray is a rapidly maturing technology that provides the opportunity to assay the expression levels of thousands or tens of thousands of genes in a single experiment. We present a new heuristic to select relevant gene subsets in order to further use them for the classification task. Our method is based on the statistical significance of adding a gene from a ranked-list to the final subset. The efficiency and effectiveness of our technique is demonstrated through extensive comparisons with other representative heuristics. Our approach shows an excellent performance, not only at identifying relevant genes, but also with respect to the computational cost.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/252d47be50cd4d561a99004001030cbbc5993e09

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Citations in the corpus

dbscan: Fast Density-Based Clustering with R (2019, background): “0 CHAMELEON algorithm (Karypis, Han, and Kumar 1999), along with a variety of shape data sets commonly found in clustering validation papers (Gionis, Mannila, and Tsaparas 2007; Zahn 1971; Chang and Yeung 2008; Jain and Martin 2005; Fu and Medico 2007).”
Markov blanket-embedded genetic algorithm for gene selection (2007, neutral): “In particular, we consider the FCBF [3,27], BIRS [39] and standard GA feature selection algorithms to compare the proposed MBEGA method.”
A review of microarray datasets and applied feature selection methods (2014, neutral): “Another example can be seen in [97], in which an incremental”
A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue (2007, neutral): “40 4 5 7 50 4 7 8 60 4 9 10 70 4 9 12 80 7 9 14 90 7 11 15 100 8 11 16 It is important to conduct a cross-validation for feature selection, because using a fixed set of features selected with the whole training data set induces a bias in the results [61].”
A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data (2015, neutral): “Best Incremental Ranked Subset (BIRS) [35] is an algorithm that scores genes based on their value and class label and then uses incremental ranked usefulness”
Distributed Clustering in the Anonymized Space with Local Differential Privacy (2019, methodology): “In the visualization part, we use three publicly available datasets: blobs based (Aggregation dataset [27]),circles based (pathbased dataset [28]) and moonshape based dataset (“twomoons” [29]).”
Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors (2016, methodology): “The synthetic datasets used come from research published in [3,10,11,13,14,27] and are commonl used benchmark datasets to test the performance of clustering algorithms.”
A review of feature selection techniques in bioinformatics (2007, positive): “An interesting hybrid filter-wrapper approach is introduced in (Ruiz et al ., 2006), crossing a univariately pre-ordered gene ranking with an incrementally augmenting wrapper method.”
Gene selection with guided regularized random forest (2012, neutral): “Feature selection has been widely used in many applications such as gene selection [23, 30, 18] as it can moderate the curse of dimensionality, improve interpretability [10] and avoid the eﬀort to analyze irrelevant or redundant features.”