Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning

By M. Shipp, K. Ross, Pablo Tamayo, Andrew P. Weng, Jeffery L. Kutok, Ricardo C. T. Aguiar, M. Gaasenbeek, Michael Angelo, Michael Reich, Geraldine S. Pinkus and 10 more. Nature Medicine, 2002

2,508 citations recorded by the source corpus.

Subfields: Molecular Biology, Pathology and Forensic Medicine

View Paper: https://www.semanticscholar.org/paper/373f58aa40b174e828d931395253905f724ce799

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In Molecular Biology

In Pathology and Forensic Medicine

Simone Fulda

Citations in the corpus

Applying the principles of stem-cell biology to cancer (2003, background): “This could lead to improved diagnosis and treatment by allowing physicians to better recognize and predict the ways in which distinct subtypes of cancer respond to therapie”
Subclass Mapping: Identifying Common Subtypes in Independent Disease Data Sets (2007, background): “Diffuse large B cell lymphoma (with survival data) DLBCL-C 58 1-channel HuGeneFL* GPL80 [14], (a)”
Prediction by Supervised Principal Components (2006, background): “Botstein and Brown 2001, Khan et al. 2001, Ramaswamy et al. 2001, Nguyen and Rocke 2002a, Nguyen and Rocke 2002b, Shipp et al. 2002, van ’t Veer et al. 2002, van de Vijver et al. 2002, Tibshirani et al. 2001, Nutt et al. 2003).”
Machine Learning as an Adversarial Service: Learning Black-Box Adversarial Examples (2017, background): “Machine Learning models are increasingly relied upon for safety and business critical tasks such as in medicine [24], [31], [40], robotics and automotive [29], [33], [38], security [2], [16], [37] and financial [12], [17], [35] applications.”
Simple decision rules for classifying human cancers from gene expression profiles (2005, neutral): “, 2002) DLBCL Affy 7,129 58 (D) 19 (F) (Shipp et al., 2002) Lung Affy 12,533 150 (A) 31 (M) (Gordon et al.”
Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection (2016, background): “selection & Prostate[60]; SVM; KNN (1-NN) Complexity analysis; LOOCV & Breast [61]; & DLBCL [62];”
Clustering cancer gene expression data: a comparative study (2008, neutral): “Armstrong-V1 [52] Affy Blood 72 2 24,48 12582 1081 Armstrong-V2 [52] Affy Blood 72 3 24,20,28 12582 2194 Bhattacharjee [9] Affy Lung 203 5 139,17,6,21,20 12600 1543 Chowdary [13] Affy Breast, Colon 104 2 62,42 22283 182 Dyrskjot [14] Affy Bladder 40 3 9,20,11 7129 1203 Golub-V1 [3] Affy Bone marrow ”
Reliability and reproducibility issues in DNA microarray measurements. (2006, methodology): “The studies produced two completely different gene classifiers with 13 and 17 genes each, without a single overlapping gene between them [57, 58 ].”
SMOTE for high-dimensional class-imbalanced data (2013, background): “Many papers attempted to develop classification rules using high-dimensional gene expression data that were class-imbalanced (see for example [4-6]).”
Gene co-expression network topology provides a framework for molecular characterization of cellular state (2004, background): “In clinical applications, expression data have been successful in partitioning distinct states and predicting the class of unknown samples, providing a molecular basis for the diagnosis of otherwise difficult to distinguish pathologies (Golub et al., 1999; Shipp et al., 2002).”
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”