Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling.

By E. Yeoh, M. Ross, S. Shurtleff, W. Williams, D. Patel, R. Mahfouz, F. Behm, S. Raimondi, Mary V. Relling, Anami R. Patel and 11 more. 2002

1,999 citations recorded by the source corpus.

Subfields: Hematology, Oncology, Public Health, Environmental and Occupational Health

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Citations in the corpus

EgoNet: identification of human disease ego-network modules (2014, background): “In the past decade, extensive work has been done to identify differentially expressed genes across different phenotypes, which can be used as diagnostic markers for classifying different disease states or predicting clinical outcomes [4-7].”
Linear discriminant analysis for the small sample size problem: an overview (2014, neutral): “ALL subtype [66] Consists of 12,558 genes of subtypes of acute lymphoblastic leukemia.”
Origins of chromosome translocations in childhood leukaemia (2003, background): “These chromosomally defined subtypes have distinctive patterns of global gene expression in microarray analyse”
The menin tumor suppressor protein is an essential oncogenic cofactor for MLL-associated leukemogenesis. (2005, methodology): “1) and E2A (YAE); rabbit anti-MLLN (rpN1) (Smith et al., 1999; Yokoyama et al., 2002); rabbit anti-Drosophila Myb (generous gift from J. Lipsick); mouse monoclonal anti-actin (C4) (Chemicon International); goat antimenin (C19) (Santa Cruz Biotechnology, Inc.); and rabbit anti-menin (BL342) (Bethyl…”
Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes (2012, background): “lung cancer [6–8], lymphomas [9–13], leukemia [14,15], and”
A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue (2007, methodology): “Unsupervised learning methods, including the hierarchical cluster [4,7], self-organizing maps [8], fuzzy adaptive resonance theory (ART) [9,10] and K-means clustering [11], are widely used in functional assignment of novel genes, marker genes identification and classes discovery.”
Simple decision rules for classifying human cancers from gene expression profiles (2005, background): “Yeoh et al. (2002) suggest that MEIS1 may be directly involved in MLL-mediated alterations in the growth of the leukemia cells.”
Belief net structure learning from uncertain interventions (2007, methodology): “) We analyze a dataset from (Yeoh et al., 2002) consisting of measurements of 12,000 genes from 327 humans suffering from different forms of acute lymphoblastic leukemia (ALL).”
Inferring Pathway Activity toward Precise Disease Classification (2008, background): “We obtained previously published mRNA expression datasets covering seven different disease classification scenarios: 24 expression profiles of HeLa cells after stimulation by Tumor Necrosis Factor (TNF) [26], expression profiles of 62 primary prostate tumors and 41 normal prostate specimen [27], exp”
Focal adhesion kinase: in command and control of cell motility (2005, background): “Furthermore, elevated levels of PTK2 mRNA have been found in studies of human carcinoma tumours and in acute lymphoblastic leukaemias, as detected by large-scale gene expression profilin”
Genetic modification of primary natural killer cells overcomes inhibitory signals and induces specific killing of leukemic cells. (2005, background): “26 Leukemic lymphoid cells usually do not express 4-1BB ligand:(21) only 2 of 284 diagnostic B-lineage ALL samples studied by gene arrays at our institution expressed 4-1BB ligand transcripts.(47) Hence, 4-1BB signals can be delivered to NK cells only if the molecule is incorporated into the recepto”
Revisiting Global Gene Expression Analysis (2012, neutral): “What are the implications for classifying cell states in disease? Significant effort is being devoted to expression profiling cancer cells and these studies use standard normalization methods (Alizadeh et al., 2000; Beer et al., 2002; Berger et al., 2010; Bhattacharjee et al., 2001; Bittner et al., ”
Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection (2016, neutral): “) [49] Pairwise constraint score (Must Link and Cannot Link) with Laplacian score & Colon [64]; KNN Kendall's coefficient; Rank sum; accuracy rate & Leukemia [67]; (Benabdeslem and Hindawi, 2011) [147] Constrained Laplacian score (CLS) & Colon [64] ; (Benabdeslem and Hindawi, 2013) [146] Constrained”