Research graph
References from Random survival forest model identifies novel biomarkers of event-free survival in high-risk pediatric acute lymphoblastic leukemia. Local targets link to admitted publications; unresolved targets remain external evidence.
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Outcome in children with standard-risk b-cell acute lymphoblastic leukemia: Results of children's oncology group trial aall0331
10.1200/jco.19.01086 · 2020 · External reference
Dexamethasone and high-dose methotrexate improve outcome for children and young adults with high-risk b-acute lymphoblastic leukemia: a report from children's oncology group study aall0232
10.1200/jco.2015.62.4544 · 2016 · External reference
10.1016/j.jpeds.2018.07.039
10.1016/j.jpeds.2018.07.039 · External reference
10.3390/ijms20122929
10.3390/ijms20122929 · External reference
Genomics and pharmacogenomics of pediatric acute lymphoblastic leukemia
10.1016/j.critrevonc.2018.04.002 · 2018 · External reference
10.1016/j.pcl.2014.09.006
10.1016/j.pcl.2014.09.006 · External reference
10.1111/ped.13457
10.1111/ped.13457 · External reference
Treatment of childhood acute lymphoblastic leukemia: Prognostic factors and clinical advances
10.1007/s11899-016-0337-y · 2016 · External reference
10.2174/1568011023354290
10.2174/1568011023354290 · External reference
10.1001/jamaoncol.2017.0580
10.1001/jamaoncol.2017.0580 · External reference
Minimal residual disease-guided therapy in childhood acute lymphoblastic leukemia
10.1182/blood-2016-12-725804 · 2017 · External reference
10.1182/blood-2009-10-248146
10.1182/blood-2009-10-248146 · External reference
10.1182/blood-2008-01-132837
10.1182/blood-2008-01-132837 · External reference
Characterization of pediatric acute lymphoblastic leukemia survival patterns by age at diagnosis
10.1155/2014/865979 · 2014 · External reference
Machine learning in haematological malignancies
10.1016/s2352-3026(20)30121-6 · 2020 · External reference
10.1016/j.csbj.2014.11.005
10.1016/j.csbj.2014.11.005 · External reference
Machine learning and feature selection for drug response prediction in precision oncology applications
10.1007/s12551-018-0446-z · 2019 · External reference
10.1177/1176935119835544
10.1177/1176935119835544 · External reference
Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods
10.1186/1471-2105-14-170 · 2013 · External reference
A gene signature for breast cancer prognosis using support vector machine
2012 · External reference
Survival model in oral squamous cell carcinoma based on clinicopathological parameters, molecular markers and support vector machines
10.1016/j.eswa.2013.02.032 · 2013 · External reference
Diagnostic accuracy of different machine learning algorithms for breast cancer risk calculation: a meta-analysis
2018 · External reference
A 15-gene signature for prediction of colon cancer recurrence and prognosis based on svm
10.1016/j.gene.2016.12.016 · 2017 · External reference
Tumor characterization in breast cancer identifies immune-relevant gene signatures associated with prognosis
2019 · External reference
Machine learning techniques in breast cancer prognosis prediction: a primary evaluation
10.1002/cam4.2811 · 2020 · External reference
Predicting the prognosis of breast cancer by integrating clinical and microarray data with bayesian networks
10.1093/bioinformatics/btl230 · 2006 · External reference
Survivability prognosis for lung cancer patients at different severity stages by a risk factor-based bayesian network modeling
10.1007/s10916-020-1537-5 · 2020 · External reference
Multiparametric decision support system for the prediction of oral cancer reoccurrence
10.1109/titb.2011.2165076 · 2012 · External reference
10.1158/1078-0432.ccr-19-1247
10.1158/1078-0432.ccr-19-1247 · External reference
10.1158/1078-0432.ccr-17-0853
10.1158/1078-0432.ccr-17-0853 · External reference
10.1158/1078-0432.ccr-19-1744
10.1158/1078-0432.ccr-19-1744 · External reference
Application of machine learning in the management of acute myeloid leukemia: Current practice and future prospects
10.1182/bloodadvances.2020002997 · 2020 · External reference
10.1016/j.csbj.2020.08.019
10.1016/j.csbj.2020.08.019 · External reference
Opportunities and obstacles for deep learning in biology and medicine
2017 · External reference
From hype to reality: Data science enabling personalized medicine
10.1186/s12916-018-1122-7 · 2018 · External reference
Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification algorithms
10.1186/1471-2105-11-447 · 2010 · External reference
Factors affecting the survival of patients with colorectal cancer using random survival forest
2020 · External reference
Prognostic factors for survival in patients with gastric cancer using a random survival forest
2017 · External reference
10.1214/08-aoas169
10.1214/08-aoas169 · External reference
A selective review on random survival forests for high dimensional data
2017 · External reference
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10.1002/hep.27732
10.1002/hep.27732 · External reference
10.1053/j.gastro.2018.04.018
10.1053/j.gastro.2018.04.018 · External reference
A seven-lncrna signature predicts overall survival in esophageal squamous cell carcinoma
2018 · External reference
Survival analysis part i: basic concepts and first analyses
10.1038/sj.bjc.6601118 · 2003 · External reference
10.1182/blood-2015-03-633685
10.1182/blood-2015-03-633685 · External reference
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Rise and fall of subclones from diagnosis to relapse in pediatric b-acute lymphoblastic leukaemia
2015 · External reference
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From fastq data to high confidence variant calls: the genome analysis toolkit best practices pipeline
2013 · External reference
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10.1093/nar/gkw227
10.1093/nar/gkw227 · External reference
10.1093/nar/gkq603
10.1093/nar/gkq603 · External reference
Random forests for genetic association studies
10.2202/1544-6115.1691 · 2011 · External reference
Bias in random forest variable importance measures: Illustrations, sources and a solution
10.1186/1471-2105-8-25 · 2007 · External reference
10.1093/nar/gky1131
10.1093/nar/gky1131 · External reference
10.1093/nar/gki005
10.1093/nar/gki005 · External reference
10.1016/j.ygeno.2012.04.003
10.1016/j.ygeno.2012.04.003 · External reference
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10.1002/sim.4154
10.1002/sim.4154 · External reference
10.1002/(sici)1097-0258(19960229)15:4<361::aid-sim168>3.0.co;2-4
10.1002/(sici)1097-0258(19960229)15:4<361::aid-sim168>3.0.co;2-4 · External reference
Survival trees by goodness of split
10.1080/01621459.1993.10476296 · 1993 · External reference
Bias of the random forest out-of-bag (oob) error for certain input parameters
10.4236/ojs.2011.13024 · 2011 · External reference
10.1371/journal.pone.0201904
10.1371/journal.pone.0201904 · External reference
Variable importance in binary regression trees and forests
10.1214/07-ejs039 · 2007 · External reference
Random survival forests for high-dimensional data
10.1002/sam.10103 · 2011 · External reference
10.1002/sim.5958
10.1002/sim.5958 · External reference
Gene selection using iterative feature elimination random forests for survival outcomes
10.1109/tcbb.2012.63 · 2012 · External reference
Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival
10.1002/sim.7803 · 2019 · External reference
A random forests quantile classifier for class imbalanced data
10.1016/j.patcog.2019.01.036 · 2019 · External reference
10.1073/pnas.95.16.9471
10.1073/pnas.95.16.9471 · External reference
10.1016/s0014-5793(01)03243-4
10.1016/s0014-5793(01)03243-4 · External reference
10.1038/nature05933
10.1038/nature05933 · External reference
10.1038/sj.bjc.6604212
10.1038/sj.bjc.6604212 · External reference
10.1016/j.celrep.2018.05.016
10.1016/j.celrep.2018.05.016 · External reference
Mixed signals from the cell's antennae: primary cilia in cancer
10.15252/embr.201846589 · 2018 · External reference
10.1038/s41568-018-0023-6
10.1038/s41568-018-0023-6 · External reference
N-methyl-d-aspartate receptors in hematopoietic cells: What have we learned?
10.3389/fphys.2020.00577 · 2020 · External reference
N-methyl-d-aspartate receptor hypofunction in meg-01 cells reveals a role for intracellular calcium homeostasis in balancing megakaryocytic-erythroid differentiation
10.1055/s-0040-1708483 · 2020 · External reference
Prominent roles of ribosomal s6 kinase 4 (rsk4) in cancer
10.1016/j.prp.2021.153374 · 2021 · External reference
Overexpression of rsk4 reverses doxorubicin resistance in human breast cancer cells via pi3k/akt signalling pathway
10.1093/jb/mvaa009 · 2020 · External reference
Cleavage of endometrial alpha-integrins into their functional forms is mediated by proprotein convertase 5/6
10.1093/humrep/des203 · 2012 · External reference
10.3390/cancers12030603
10.3390/cancers12030603 · External reference
