Prognostically useful gene-expression profiles in acute myeloid leukemia.

By P. Valk, R. Verhaak, M. A. Beijen, Claudia A J Erpelinck, S. V. W. V. van Doorn-Khosrovani, Judith M. Boer, H. Beverloo, M. Moorhouse, P. J. van der Spek, B. Löwenberg and 1 more. New England Journal of Medicine, 2004

1,447 citations recorded by the source corpus.

Subfields: Hematology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8e8fc6b30501d69c01890124193283f8cabdb224

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Citations in the corpus

Learning Explainable Models Using Attribution Priors (2019, methodology): “Furthermore, the pathways used by our model more closely match with biological expert knowledge – pathways included prognostically useful AML gene expression profiles, as well as important AML-related transcription factors (see Figure 2 and Appendix) (Liu et al., 2017; Valk et al., 2004).”
DNA Methylation Signatures Identify Biologically Distinct Subtypes in Acute Myeloid Leukemia (2010, methodology): “0 GeneChips. mRNA isolation, labeling, hybridization, and quality control were carried out as described previously (Valk et al., 2004 ).”
CD47 is an adverse prognostic factor and therapeutic antibody target on human acute myeloid leukemia stem cells (2009, methodology): “…described cohorts of adult AML patients: (1) a training dataset of 285 patients with diverse cytogenetic and molecular abnormalities described by Valk et al. (2004), (2) a test dataset of 242 patients with normal karyotypes described by Metzeler et al. (2008), and (3) a validation dataset of…”
Clinical utility of microarray-based gene expression profiling in the diagnosis and subclassification of leukemia: report from the International Microarray Innovations in Leukemia Study Group. (2010, methodology): “C13: AML with t(11q23)/MLL C12: AML with inv(16)/t(16;16) C11: AML with t(15;17) C10: AML with t(8;21) C9: c-ALL/Pre-B-ALL without t(9;22) C8: ALL with hyperdiploid karyotype C7: ALL with t(1;19) C6: ALL with t(12;21) C5: T-ALL C4: c-ALL/Pre-B-ALL with t(9;22) C3: Pro-B-ALL with t(11q23)/MLL C2: Mat”
Improving performance of deep learning models with axiomatic attribution priors and expected gradients (2020, methodology): “Furthermore, the pathways our model uses more closely match biological expert knowledge, that is, they included prognostically useful AML gene expression profiles as well as important AML-related transcription factors (Supplementary Section I) 34,35 .”
Effect of separate sampling on classification accuracy (2014, neutral): “The results for the dataset [16] are given in the article.”
Densely interconnected transcriptional circuits control cell states in human hematopoiesis (2011, neutral): “Impaired or blocked hematopoietic differentiation is a defining characteristic of leukemia, and the gene expression profiles of leukemias cluster strongly into subgroups that correspond to specific molecular subgroups (Bullinger et al., 2004; Tamayo et al., 2007; Valk et al., 2004).”
Feature Selection via Dependence Maximization (2012, neutral): “, 2001; West, 2003) (9, 10), and two with leukaemia (Bullinger et al., 2004; Valk et al., 2004) (16, 17).”