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

Cluster analysis and display of genome-wide expression patterns

Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184

An Introduction to Computational Learning Theory

Kearns, M. J., & Vazirani, U. (1994) · doi:10.7551/mitpress/3897.001.0001 · Cited by 1,697

Expression of protein kinase C genes in hemopoietic cells is cell-type- and B cell-differentiation stage specific

Mischak, H., Kolch, W., Goodnight, J., Davidson, W. F., Rapp, U., Rose-John, S., & Mushinski, J. F. (1991) · The Journal of Immunology · doi:10.4049/jimmunol.147.11.3981 · Cited by 138

Gene expression profiling predicts clinical outcome of breast cancer

van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., et al. (2002) · Nature · doi:10.1038/415530a · Cited by 9,536

Confirmation of the molecular classification of diffuse large B-cell lymphoma by immunohistochemistry using a tissue microarray

Hans, C. P. (2004) · Blood · doi:10.1182/blood-2003-05-1545 · Cited by 3,703

Extrinsic versus intrinsic apoptosis pathways in anticancer chemotherapy

Fulda, S., & Debatin, K.-M. (2006) · Oncogene · doi:10.1038/sj.onc.1209608 · Cited by 2,312

Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression

Rhodes, D. R., Yu, J., Shanker, K., Deshpande, N., Varambally, R., Ghosh, D., Barrette, T., Pandey, A., & Chinnaiyan, A. M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0401994101 · Cited by 1,019

eD octor: machine learning and the future of medicine

Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Lee, M. J., & Asadi, H. (2018) · Journal of Internal Medicine · doi:10.1111/joim.12822 · Cited by 956

Microarray analysis identifies a death-from-cancer signature predicting therapy failure in patients with multiple types of cancer

Glinsky, G. V., Berezovska, O., & Glinskii, A. B. (2005) · Journal of Clinical Investigation · doi:10.1172/jci23412 · Cited by 916

The molecular signature of mediastinal large B-cell lymphoma differs from that of other diffuse large B-cell lymphomas and shares features with classical Hodgkin lymphoma

Savage, K. J. (2003) · Blood · doi:10.1182/blood-2003-06-1841 · Cited by 855

Predictive data mining in clinical medicine: Current issues and guidelines

BELLAZZI, R., & ZUPAN, B. (2008) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2006.11.006 · Cited by 823

Coexpression Analysis of Human Genes Across Many Microarray Data Sets

Lee, H. K., Hsu, A. K., Sajdak, J., Qin, J., & Pavlidis, P. (2004) · Genome Research · doi:10.1101/gr.1910904 · Cited by 770

Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection

Ang, J. C., Mirzal, A., Haron, H., & Hamed, H. N. A. (2016) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2015.2478454 · Cited by 641

Revisiting Global Gene Expression Analysis

Lovén, J., Orlando, D. A., Sigova, A. A., Lin, C. Y., Rahl, P. B., Burge, C. B., Levens, D. L., Lee, T. I., & Young, R. A. (2012) · Cell · doi:10.1016/j.cell.2012.10.012 · Cited by 616

Reliability and reproducibility issues in DNA microarray measurements

DRAGHICI, S., KHATRI, P., EKLUND, A., & SZALLASI, Z. (2006) · Trends in Genetics · doi:10.1016/j.tig.2005.12.005 · Cited by 590

Towards Making Systems Forget with Machine Unlearning

Cao, Y., & Yang, J. (2015) · 2015 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2015.35 · Cited by 622

Gene Expression Profiles Predict Complete Pathologic Response to Neoadjuvant Paclitaxel and Fluorouracil, Doxorubicin, and Cyclophosphamide Chemotherapy in Breast Cancer

Ayers, M., Symmans, W. F., Stec, J., Damokosh, A. I., Clark, E., Hess, K., Lecocke, M., Metivier, J., Booser, D., Ibrahim, N., et al. (2004) · Journal of Clinical Oncology · doi:10.1200/jco.2004.05.166 · Cited by 566

Gene expression profiling predicts clinical outcome of breast cancer

(2002) · Nature · doi:10.1038/news020128-6 · Cited by 533

Gene expression profiling of pediatric acute myelogenous leukemia

Ross, M. E., Mahfouz, R., Onciu, M., Liu, H.-C., Zhou, X., Song, G., Shurtleff, S. A., Pounds, S., Cheng, C., Ma, J., et al. (2004) · Blood · doi:10.1182/blood-2004-03-1154 · Cited by 448

Transcriptional analysis of the B cell germinal center reaction

Klein, U., Tu, Y., Stolovitzky, G. A., Keller, J. L., Haddad, J., Miljkovic, V., Cattoretti, G., Califano, A., & Dalla-Favera, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0437996100 · Cited by 409

Inhibition of FLT3 in MLL

Armstrong, S. A., Kung, A. L., Mabon, M. E., Silverman, L. B., Stam, R. W., Den Boer, M. L., Pieters, R., Kersey, J. H., Sallan, S. E., Fletcher, J. A., et al. (2003) · Cancer Cell · doi:10.1016/s1535-6108(03)00003-5 · Cited by 398

Clinical impact of the differentiation profile assessed by immunophenotyping in patients with diffuse large B-cell lymphoma

Colomo, L., López-Guillermo, A., Perales, M., Rives, S., Martı́nez, A., Bosch, F., Colomer, D., Falini, B., Montserrat, E., & Campo, E. (2003) · Blood · doi:10.1182/blood-2002-04-1286 · Cited by 391

DNA Microarrays in Clinical Oncology

Ramaswamy, S., & Golub, T. R. (2002) · Journal of Clinical Oncology · doi:10.1200/jco.2002.20.7.1932 · Cited by 379

A uniform activated B-cell–like immunophenotype might explain the poor prognosis of primary central nervous system lymphomas: analysis of 83 cases

Camilleri-Broët, S., Crinière, E., Broët, P., Delwail, V., Mokhtari, K., Moreau, A., Kujas, M., Raphaël, M., Iraqi, W., Sautès-Fridman, C., et al. (2006) · Blood · doi:10.1182/blood-2005-03-1024 · Cited by 371

Characterization of peripheral T-cell lymphomas in a single North American institution by the WHO classification

Savage, K. J., Chhanabhai, M., Gascoyne, R. D., & Connors, J. M. (2004) · Annals of Oncology · doi:10.1093/annonc/mdh392 · Cited by 363

Expression Profile–Defined Classification of Lung Adenocarcinoma Shows Close Relationship With Underlying Major Genetic Changes and Clinicopathologic Behaviors

Takeuchi, T., Tomida, S., Yatabe, Y., Kosaka, T., Osada, H., Yanagisawa, K., Mitsudomi, T., & Takahashi, T. (2006) · Journal of Clinical Oncology · doi:10.1200/jco.2005.03.8224 · Cited by 355

Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data

Jeffery, I. B., Higgins, D. G., & Culhane, A. C. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-359 · Cited by 355

Gene co-expression network topology provides a framework for molecular characterization of cellular state

Carter, S. L., Brechbühler, C. M., Griffin, M., & Bond, A. T. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth234 · Cited by 344

Addition of Rituximab to Standard Chemotherapy Improves the Survival of Both the Germinal Center B-Cell–Like and Non–Germinal Center B-Cell–Like Subtypes of Diffuse Large B-Cell Lymphoma

Fu, K., Weisenburger, D. D., Choi, W. W. L., Perry, K. D., Smith, L. M., Shi, X., Hans, C. P., Greiner, T. C., Bierman, P. J., Bociek, R. G., et al. (2008) · Journal of Clinical Oncology · doi:10.1200/jco.2007.15.9277 · Cited by 334

Molecular and genetic characterization of MHC deficiency identifies EZH2 as therapeutic target for enhancing immune recognition

Ennishi, D., Takata, K., Béguelin, W., Duns, G., Mottok, A., Farinha, P., Bashashati, A., Saberi, S., Boyle, M., Meissner, B., et al. (2019) · Cancer Discovery · doi:10.1158/2159-8290.cd-18-1090 · Cited by 318

Empowering biomedical discovery with AI agents

Gao, S., Fang, A., Huang, Y., Giunchiglia, V., Noori, A., Schwarz, J. R., Ektefaie, Y., Kondic, J., & Zitnik, M. (2024) · Cell · doi:10.1016/j.cell.2024.09.022 · Cited by 315

Class-imbalanced classifiers for high-dimensional data

Lin, W.-J., & Chen, J. J. (2013) · Briefings in Bioinformatics · doi:10.1093/bib/bbs006 · Cited by 284

On the efficiency of targeted clinical trials

Maitournam, A., & Simon, R. (2005) · Statistics in Medicine · doi:10.1002/sim.1975 · Cited by 200

Gene Expression Profiles Predict Early Relapse in Ovarian Cancer after Platinum-Paclitaxel Chemotherapy

Hartmann, L. C., Lu, K. H., Linette, G. P., Cliby, W. A., Kalli, K. R., Gershenson, D., Bast, R. C., Stec, J., Iartchouk, N., Smith, D. I., et al. (2005) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-04-1673 · Cited by 141

Tyrosine kinase inhibition in diffuse large B-cell lymphoma: molecular basis for antitumor activity and drug resistance of dasatinib

Yang, C., Lu, P., Lee, F. Y., Chadburn, A., Barrientos, J. C., Leonard, J. P., Ye, F., Zhang, D., Knowles, D. M., & Wang, Y. L. (2008) · Leukemia · doi:10.1038/leu.2008.163 · Cited by 61

Expression of C‐IAP1, C‐IAP2 and SURVIVIN discriminates different types of lymphoid malignancies

de Graaf, A. O., Han van Krieken, J., Tönnissen, E., Wissink, W., van de Locht, L., Overes, I., Dolstra, H., de Witte, T., van der Reijden, B. A., & Jansen, J. H. (2005) · British Journal of Haematology · doi:10.1111/j.1365-2141.2005.05690.x · Cited by 42

Eukaryotic genomes from a global metagenomic dataset illuminate trophic modes and biogeography of ocean plankton

Alexander, H., Hu, S. K., Krinos, A. I., Pachiadaki, M., Tully, B. J., Neely, C. J., & Reiter, T. (2021) · doi:10.1101/2021.07.25.453713 · Cited by 67

Prediction of recurrence in advanced gastric cancer patients after curative resection by gene expression profiling

Motoori, M., Takemasa, I., Yano, M., Saito, S., Miyata, H., Takiguchi, S., Fujiwara, Y., Yasuda, T., Doki, Y., Kurokawa, Y., et al. (2005) · International Journal of Cancer · doi:10.1002/ijc.20808 · Cited by 39

Novel disease targets and management approaches for diffuse large B-cell lymphoma

Wilson, W. H., Hernandez-Ilizaliturri, F. J., Dunleavy, K., Little, R. F., & O'Connor, O. A. (2010) · Leukemia & Lymphoma · doi:10.3109/10428194.2010.500045 · Cited by 34

Guidelines for using sigQC for systematic evaluation of gene signatures

Dhawan, A., Barberis, A., Cheng, W.-C., Domingo, E., West, C., Maughan, T., Scott, J. G., Harris, A. L., & Buffa, F. M. (2019) · Nature Protocols · doi:10.1038/s41596-019-0136-8 · Cited by 31

Applications of gene pair methods in clinical research: advancing precision medicine

Wu, C., Xie, X., Yang, X., Du, M., Lin, H., & Huang, J. (2025) · Molecular Biomedicine · doi:10.1186/s43556-025-00263-w · Cited by 10

Research on Artificial Intelligence, Machine and Deep Learning in Medicine: Global Characteristics, Readiness, and Equity

Klingelhöfer, D., Braun, M., Dröge, J., Groneberg, D. A., & Brüggmann, D. (2025) · Globalization and Health · doi:10.1186/s12992-025-01128-1 · Cited by 6