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Neural networks often struggle with high-dimensional but small sample-size tabular datasets.
The reduction of a graph to canonical form and the algebra which appears therein
Boris Weisfeiler and Andrei Leman · 1968
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Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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Classification of human lung carcinomas by mrna expression profiling reveals distinct adenocarcinoma subclasses
Arindam Bhattacharjee, William G Richards, Jane Staunton, Cheng Li, Stefano Monti, Priya Vasa, Christine Ladd, Javad Beheshti, Raphael Bueno, Michael Gillette, et al · 2001
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Random forests
Leo Breiman · 2001
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Cancer predisposition in mutant mice defective in multiple genetic pathways: uncovering important genetic interactions
Lisiane B Meira, Antonio MC Reis, David L Cheo, Dorit Nahari, Dennis K Burns, and Errol C Friedberg · 2001
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Gene expression correlates of clinical prostate cancer behavior
Dinesh Singh, Phillip G Febbo, Kenneth Ross, Donald G Jackson, Judith Manola, Christine Ladd, Pablo Tamayo, Andrew A Renshaw, Anthony V D’Amico, Jerome P Richie, et al · 2002
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Gene expression profiling of gliomas strongly predicts survival
William A Freije, F Edmundo Castro-Vargas, Zixing Fang, Steve Horvath, Timothy Cloughesy, Linda M Liau, Paul S Mischel, and Stanley F Nelson · 2004
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Microarray gene expression profiling of b-cell chronic lymphocytic leukemia subgroups defined by genomic aberrations and vh mutation status
Christian Haslinger, Norbert Schweifer, Stephan Stilgenbauer, Hartmut Dohner, Peter Lichter, Norbert Kraut, Christian Stratowa, and Roger Abseher · 2004
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Statistical comparisons of classifiers over multiple data sets
Janez Demšar · 2006
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Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis
Hyunsoo Kim and Haesun Park · 2007
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Airway epithelial gene expression in the diagnostic evaluation of smokers with suspect lung cancer
Avrum Spira, Jennifer E Beane, Vishal Shah, Katrina Steiling, Gang Liu, Frank Schembri, Sean Gilman, Yves-Martine Dumas, Paul Calner, Paola Sebastiani, et al · 2007
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Network-constrained regularization and variable selection for analysis of genomic data
Caiyan Li and Hongzhe Li · 2008
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Multiple mutations in genetic cardiovascular disease: a marker of disease severity?
Matthew Kelly and Christopher Semsarian · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups
Christina Curtis, Sohrab P Shah, Suet-Feung Chin, Gulisa Turashvili, Oscar M Rueda, Mark J Dunning, Doug Speed, Andy G Lynch, Shamith Samarajiwa, Yinyin Yuan, et al · 2012
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Systematic identification of genomic markers of drug sensitivity in cancer cells
Mathew J Garnett, Elena J Edelman, Sonja J Heidorn, Chris D Greenman, Anahita Dastur, King Wai Lau, Patricia Greninger, I Richard Thompson, Xi Luo, Jorge Soares, et al · 2012
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A framework for regularized non-negative matrix factorization, with application to the analysis of gene expression data
Leo Taslaman and Björn Nilsson · 2012
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Genomics of drug sensitivity in cancer (gdsc): a resource for therapeutic biomarker discovery in cancer cells
Wanjuan Yang, Jorge Soares, Patricia Greninger, Elena J Edelman, Howard Lightfoot, Simon Forbes, Nidhi Bindal, Dave Beare, James A Smith, I Richard Thompson, et al · 2012
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Deciphering signatures of mutational processes operative in human cancer
Ludmil B Alexandrov, Serena Nik-Zainal, David C Wedge, Peter J Campbell, and Michael R Stratton · 2013
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Searching for exotic particles in high-energy physics with deep learning
Pierre Baldi, Peter Sadowski, and Daniel Whiteson · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response
Hui Gao, Joshua M Korn, Stéphane Ferretti, John E Monahan, Youzhen Wang, Mallika Singh, Chao Zhang, Christian Schnell, Guizhi Yang, Yun Zhang, et al · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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The molecular signatures database hallmark gene set collection
Arthur Liberzon, Chet Birger, Helga Thorvaldsdóttir, Mahmoud Ghandi, Jill P Mesirov, and Pablo Tamayo · 2015
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The cancer genome atlas (tcga): an immeasurable source of knowledge
Katarzyna Tomczak, Patrycja Czerwińska, and Maciej Wiznerowicz · 2015
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Cutting edge: Critical role of glycolysis in human plasmacytoid dendritic cell antiviral responses
Gagan Bajwa, Ralph J DeBerardinis, Baomei Shao, Brian Hall, J David Farrar, and Michelle A Gill · 2016
Cited alongside, same era.
Should we really use post-hoc tests based on mean-ranks?
Alessio Benavoli, Giorgio Corani, and Francesca Mangili · 2016
Cited alongside, same era.
A landscape of pharmacogenomic interactions in cancer
Francesco Iorio, Theo A Knijnenburg, Daniel J Vis, Graham R Bignell, Michael P Menden, Michael Schubert, Nanne Aben, Emanuel Gonçalves, Syd Barthorpe, Howard Lightfoot, et al · 2016
Cited alongside, same era.
Sparse-input neural networks for high-dimensional nonparametric regression and classification
Jean Feng and Noah Simon · 2017
Cited alongside, same era.
Lightgbm: A highly efficient gradient boosting decision tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 2017
Cited alongside, same era.
Slaps: Self-supervision improves structure learning for graph neural networks
Bahare Fatemi, Layla El Asri, and Seyed Mehran Kazemi · 2021
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Revisiting deep learning models for tabular data
Yu. V. Gorishniy, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko · 2021
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Well-tuned simple nets excel on tabular datasets
Arlind Kadra, Marius Lindauer, Frank Hutter, and Josif Grabocka · 2021
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The lhc olympics 2020 a community challenge for anomaly detection in high energy physics
Gregor Kasieczka, Benjamin Nachman, David Shih, Oz Amram, Anders Andreassen, Kees Benkendorfer, Blaz Bortolato, Gustaaf Brooijmans, Florencia Canelli, Jack H Collins, et al · 2021
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Combining machine learning and computational chemistry for predictive insights into chemical systems
John A Keith, Valentin Vassilev-Galindo, Bingqing Cheng, Stefan Chmiela, Michael Gastegger, Klaus-Robert Muller, and Alexandre Tkatchenko · 2021
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Semi-supervised classification with graph convolutional networks
Thomas Kipf and Max Welling · 2017
Cited alongside, same era.
Deep neural networks for high dimension, low sample size data
Bo Liu, Ying Wei, Yu Zhang, and Qiang Yang · 2017
Cited alongside, same era.
Diet networks: Thin parameters for fat genomics
Adriana Romero, Pierre Luc Carrier, Akram Erraqabi, Tristan Sylvain, Alex Auvolat, Etienne Dejoie, Marc-André Legault, Marie-Pierre Dubé, Julie G. Hussin, and Yoshua Bengio · 2017
Cited alongside, same era.
C9orf72-mediated ALS and FTD: multiple pathways to disease
Rubika Balendra and Adrian M Isaacs · 2018
Cited alongside, same era.
Feature selection: A data perspective
Jundong Li, Kewei Cheng, Suhang Wang, Fred Morstatter, Robert P Trevino, Jiliang Tang, and Huan Liu · 2018
Cited alongside, same era.
Few-shot learning with graph neural networks
Victor Garcia Satorras and Joan Bruna · 2018
Cited alongside, same era.
A survey on deep transfer learning
Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, and Chunfang Liu · 2018
Cited alongside, same era.
Lassonet: Neural networks with feature sparsity
Ismael Lemhadri, Feng Ruan, and Rob Tibshirani · 2021
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Gcexplainer: Human-in-the-loop concept-based explanations for graph neural networks
Lucie Charlotte Magister, Dmitry Kazhdan, Vikash Singh, and Pietro Lio’ · 2021
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Are neural rankers still outperformed by gradient boosted decision trees?
Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, and Marc-Alexander Najork · 2021
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The role of machine learning in clinical research: transforming the future of evidence generation
E Hope Weissler, Tristan Naumann, Tomas Andersson, Rajesh Ranganath, Olivier Elemento, Yuan Luo, Daniel F Freitag, James Benoit, Michael C Hughes, Faisal Khan, et al · 2021
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Towards open-world feature extrapolation: An inductive graph learning approach
Qitian Wu, Chenxiao Yang, and Junchi Yan · 2021
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Chemtables: a dataset for semantic classification on tables in chemical patents
Zenan Zhai, Christian Druckenbrodt, Camilo Thorne, Saber A Akhondi, Dat Quoc Nguyen, Trevor Cohn, and Karin Verspoor · 2021
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Deep neural networks and tabular data: A survey
Vadim Borisov, Tobias Leemann, Kathrin Sessler, Johannes Haug, Martin Pawelczyk, and Gjergji Kasneci · 2022
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How attentive are graph attention networks?
Shaked Brody, Uri Alon, and Eran Yahav · 2022
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Learning enhanced representations for tabular data via neighborhood propagation
Kounianhua Du, Weinan Zhang, Ruiwen Zhou, Yangkun Wang, Xilong Zhao, Jiarui Jin, Quan Gan, Zheng Zhang, and David Paul Wipf · 2022
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Why do tree-based models still outperform deep learning on typical tabular data?
Léo Grinsztajn, Edouard Oyallon, and Gaël Varoquaux · 2022
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Tabpfn: A transformer that solves small tabular classification problems in a second
Noah Hollmann, Samuel G. Müller, Katharina Eggensperger, and Frank Hutter · 2022
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Differentiable graph module (dgm) for graph convolutional networks
Anees Kazi, Luca Cosmo, Seyed-Ahmad Ahmadi, Nassir Navab, and Michael M Bronstein · 2022
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Encoding concepts in graph neural networks
Lucie Charlotte Magister, Pietro Barbiero, Dmitry Kazhdan, Federico Siciliano, Gabriele Ciravegna, Fabrizio Silvestri, Mateja Jamnik, and Pietro Lio · 2022
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Probabilistic machine learning: an introduction
Kevin P Murphy · 2022
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Stunt: Few-shot tabular learning with self-generated tasks from unlabeled tables
Jaehyun Nam, Jihoon Tack, Kyungmin Lee, Hankook Lee, and Jinwoo Shin · 2022
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Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases
Paul Scherer, Maja Trebacz, Nikola Simidjievski, Ramon Viñas, Zohreh Shams, Helena Andres Terre, Mateja Jamnik, and Pietro Liò · 2022
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Transtab: Learning transferable tabular transformers across tables
Zifeng Wang and Jimeng Sun · 2022
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Locally sparse neural networks for tabular biomedical data
Junchen Yang, Ofir Lindenbaum, and Yuval Kluger · 2022
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Table2graph: Transforming tabular data to unified weighted graph
Kaixiong Zhou, Zirui Liu, Rui Chen, Li Li, Soo-Hyun Choi, and Xia Hu · 2022
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Machine learning methods for cancer classification using gene expression data: A review
Fadiyah Ahmed Alharbi and Aleksandar Vakanski · 2023
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Transfer learning with deep tabular models
Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, and Micah Goldblum · 2023
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Weight predictor network with feature selection for small sample tabular biomedical data
Andrei Margeloiu, Nikola Simidjievski, Pietro Lio, and Mateja Jamnik · 2023
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Tabular deep learning when d >> n d>>n by using an auxiliary knowledge graph
Camilo Ruiz, Hongyu Ren, Kexin Huang, and Jure Leskovec · 2023
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Fsnet: Feature selection network on high-dimensional biological data
Dinesh Singh and Makoto Yamada · 2023
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