Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks

By J. Khan, Jun S. Wei, Markus Ringnér, L. Saal, Marc Ladanyi, Frank Westermann, F. Berthold, M. Schwab, C. Antonescu, C. Peterson and 1 more. 2001

2,841 citations recorded by the source corpus.

Subfields: Molecular Biology

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Citations in the corpus

Methods for interpreting and understanding deep neural networks (2017, methodology): “Sensitivity analysis has been regularly used in scientific applications of machine learning such as medical diagnosis [29], ecological modeling [20], or mutagenicity prediction [6].”
Joint covariate selection and joint subspace selection for multiple classification problems (2010, background): “6, consider a DNA microarray analysis problem in which the covariates are levels of gene expression and the responses are phenotypes or cellular processes (Khan et al. 2001).”
Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems (2011, background): “The Small Round Blue-Cell Tumor Data of childhood (SRBCT, [44]) includes 4 different types of tumors with 23, 20, 12 and 8 microarrays per class and 2308 genes.”
From Boolean to probabilistic Boolean networks as models of genetic regulatory networks (2002, background): “For example, cancer classification using a variety of methods has been used to exploit the class-separating power of expression data: leukemias [6], various cancers [7], small, round, blue-cell cancers [8], and hereditary breast cancer [9].”
Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications (2007, background): “The second test-bed contains six microarray datasets: 9-tumors [29], Brain-tumor2 [30], Leukemia-1 [2], prostate-tumors [31], DLBCL [32] and SRBCT [33].”
Accurate Cancer Classification Using Expressions of Very Few Genes (2007, methodology): “There are, in total, 63 training samples and 25 testing samples already provided in [2]; five of the testing samples are not SRBCTs.”
A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression (2004, background): “On the SRBCT, ALL, ALL/AML-4-class and ALL/AML-3-class datasets, the best accuracy is > 93%.”
RNA Sequencing and Analysis (2015, neutral): “Interestingly, in some cases, the sample heterogeneity can have advantages in transcriptome profiling by identifying novel pathways, implicating cellular origins of disease, or identifying previously unknown pathological sites (Alizadeh et al. 2000; Khan et al. 2001; Sorlie et al. 2001).”
Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions (2003, methodology): “We present results obtained for two publicly available microarray datasets: the two-class leukemia set (Golub et al., 1999), and the four-class small, round blue-cell tumor (SRBCT) set (Khan et al., 2001; Yeo and Poggio, 2001).”