Research graph
References from SubNExT: Towards accurate, efficient and robust gene expression classification for breast cancer subtyping. Local targets link to admitted publications; unresolved targets remain external evidence.
10.1016/j.meomic.2024.100039
10.1016/j.meomic.2024.100039 · External reference
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10.52202/068431-0037 · External reference
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2019 · External reference
10.1109/cvpr52688.2022.01167
10.1109/cvpr52688.2022.01167 · 2022 · External reference
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10.1145/2939672.2939785 · External reference
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2024 · External reference
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A new golden age in computer architecture: empowering the machine-learning revolution
10.1109/mm.2018.112130030 · 2018 · External reference
10.1038/nature14539
10.1038/nature14539 · External reference
Dealing with dimensionality: the application of machine learning to multi-omics data
10.1093/bioinformatics/btad021 · 2023 · External reference
A review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data
10.3390/pr9081466 · 2021 · External reference
10.1016/j.compbiomed.2020.103761
10.1016/j.compbiomed.2020.103761 · External reference
10.1016/j.mlwa.2023.100468
10.1016/j.mlwa.2023.100468 · External reference
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2017 · External reference
10.1016/j.csbj.2020.08.005
10.1016/j.csbj.2020.08.005 · External reference
A bio-inspired convolution neural network architecture for automatic breast cancer detection and classification using RNA-seq gene expression data
2023 · External reference
Convolutional neural network for human cancer types prediction by integrating protein interaction networks and omics data
10.1038/s41598-021-98814-y · 2021 · External reference
Transformer for gene expression modeling (t-GEM): an interpretable deep learning model for gene expression-based phenotype predictions
2022 · External reference
10.1016/j.eswa.2023.120047
10.1016/j.eswa.2023.120047 · External reference
10.1016/j.compbiomed.2023.106643
10.1016/j.compbiomed.2023.106643 · External reference
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10.21236/ad0256582 · 1961 · External reference
10.1016/0893-6080(89)90020-8
10.1016/0893-6080(89)90020-8 · External reference
10.1101/gr.192502
10.1101/gr.192502 · External reference
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2018 · External reference
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1995 · External reference
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10.1016/j.eswa.2023.122666
10.1016/j.eswa.2023.122666 · External reference
Deepinsight: a methodology to transform a non-image data to an image for convolution neural network architecture
10.1038/s41598-019-47765-6 · 2019 · External reference
10.1126/science.1127647
10.1126/science.1127647 · External reference
Machine learning methods for cancer classification using gene expression data: a review
2023 · External reference
10.1038/s41467-020-20430-7
10.1038/s41467-020-20430-7 · External reference
10.1038/ng.2764
10.1038/ng.2764 · External reference
10.3390/cancers16030583
10.3390/cancers16030583 · External reference
10.1186/s12920-015-0129-6
10.1186/s12920-015-0129-6 · External reference
10.1016/j.asoc.2019.105524
10.1016/j.asoc.2019.105524 · External reference
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Visualizing high-dimensional data using t-SNE
2008 · External reference
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1996 · External reference
10.1080/14786440109462720
10.1080/14786440109462720 · External reference
Independent component analysis: an introduction
10.1016/j.aci.2018.08.006 · 2021 · External reference
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2015 · External reference
10.1126/science.290.5500.2319
10.1126/science.290.5500.2319 · External reference
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2016 · External reference
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2021 · External reference
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2018 · External reference
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2019 · External reference
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2019 · External reference
10.1016/j.csbj.2023.12.027
10.1016/j.csbj.2023.12.027 · External reference
Sparsity regularization enhances gene selection and leukemia subtype classification via logistic regression
10.1016/j.leukres.2025.107663 · 2025 · External reference
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2020 · External reference
10.1016/j.inffus.2021.11.005
10.1016/j.inffus.2021.11.005 · External reference
Dropout: a simple way to prevent neural networks from overfitting
2014 · External reference
10.1109/cvpr52733.2024.02591
10.1109/cvpr52733.2024.02591 · External reference
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10.1109/iccv.2015.123
10.1109/iccv.2015.123 · External reference
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An empirical study on the correlation between early stopping patience and epochs in deep learning
10.1051/itmconf/20246401003 · 2024 · External reference
Performance comparison of deep learning autoencoders for cancer subtype detection using multi-omics data
2021 · External reference
10.1109/cvpr.2016.485
10.1109/cvpr.2016.485 · External reference
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Robust neural networks are more interpretable for genomics
2019 · External reference
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Evaluating deep learning for predicting epigenomic profiles
10.1038/s42256-022-00570-9 · 2022 · External reference
Benchmarking machine learning robustness in covid-19 genome sequence classification
10.1038/s41598-023-31368-3 · 2023 · External reference
10.1016/j.csbj.2021.05.040
10.1016/j.csbj.2021.05.040 · External reference
Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package
2015 · External reference
10.1016/b978-0-44-323761-4.00012-2
10.1016/b978-0-44-323761-4.00012-2 · External reference
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Scikit-learn: machine learning in Python
2012 · External reference
10.2307/2333709
10.2307/2333709 · External reference
10.2307/2331554
10.2307/2331554 · External reference
10.1111/j.1749-6632.1950.tb53974.x
10.1111/j.1749-6632.1950.tb53974.x · External reference
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1934 · External reference
10.3390/electronics8030292
10.3390/electronics8030292 · External reference
10.1186/s40537-021-00444-8
10.1186/s40537-021-00444-8 · External reference
Interchromosomal interactions: a genomic love story of kissing chromosomes
2018 · External reference
10.1038/nrg2452
10.1038/nrg2452 · External reference
10.3390/app13095521
10.3390/app13095521 · External reference
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2021 · External reference
10.4018/979-8-3693-6517-5.ch003
10.4018/979-8-3693-6517-5.ch003 · External reference
10.1016/j.eswa.2023.122666
10.1016/j.eswa.2023.122666 · External reference
10.52202/075280-2657
10.52202/075280-2657 · External reference
10.1016/j.dajour.2024.100470
10.1016/j.dajour.2024.100470 · External reference
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Efficient deep learning: a survey on making deep learning models smaller, faster, and better
10.1145/3578938 · 2023 · External reference
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Multitask deep learning-based pipeline for gas leakage detection via e-nose and thermal imaging multimodal fusion
10.3390/chemosensors11070364 · 2023 · External reference
Definitions, methods, and applications in interpretable machine learning
10.1073/pnas.1900654116 · 2019 · External reference
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10.1145/2939672.2939778
10.1145/2939672.2939778 · External reference
10.1016/j.ins.2021.05.055
10.1016/j.ins.2021.05.055 · External reference
Xgboost, a novel explainable AI technique, in the prediction of myocardial infarction: a UK biobank cohort study
10.1177/11795468221133611 · 2022 · External reference
10.1038/s41576-022-00532-2
10.1038/s41576-022-00532-2 · External reference
10.18653/v1/2022.acl-long.269
10.18653/v1/2022.acl-long.269 · 2022 · External reference
10.1007/s11263-019-01228-7
10.1007/s11263-019-01228-7 · External reference
Interpretation and visualization techniques for deep learning models in medical imaging
10.1088/1361-6560/abcd17 · 2021 · External reference
Deepfeature: feature selection in nonimage data using convolutional neural network
10.1093/bib/bbab297 · 2021 · External reference
Deepinsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics
10.1038/s41598-023-29644-3 · 2023 · External reference
Designing interpretable deep learning applications for functional genomics: a quantitative analysis
10.1093/bib/bbae449 · 2024 · External reference
10.1109/cvpr52733.2024.01100
10.1109/cvpr52733.2024.01100 · External reference
Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data
10.1186/s12920-019-0624-2 · 2019 · External reference
