Benchmarking Quantum and Classical Machine Learning Models on Oncological Data | Paper Citation Record

Observation 1a893cc5-8961-4216-ba51-e104974a5f16 · outbound

This paper cites Cross-scale interactions, nonlinearities, and forecasting catastrophic events,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Cross-scale interactions, nonlinearities, and forecasting catastrophic events,

Reference 1

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Observation dabe135a-0350-4c00-ac9f-e0e31be1c8f6 · outbound

This paper cites Towards foundation models that learn across biological scales,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Towards foundation models that learn across biological scales,

Reference 2

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This paper cites Chapter 2 - types of omics data: Genomics, metagenomics, epige- nomics, transcriptomics, proteomics, metabolomics, and phenomics,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Chapter 2 - types of omics data: Genomics, metagenomics, epige- nomics, transcriptomics, proteomics, metabolomics, and phenomics,

Reference 3

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This paper cites A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology,

Reference 4

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This paper cites Evaluation of prognostic and predictive models in the oncology clinic,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Evaluation of prognostic and predictive models in the oncology clinic,

Reference 5

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Observation 5f3e4d98-feeb-4d03-afc8-ce9a22cf45bf · outbound

This paper cites AI in Oncology: Transforming Cancer Detection through Machine Learning and Deep Learning Applications.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data AI in Oncology: Transforming Cancer Detection through Machine Learning and Deep Learning Applications

Reference 6

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This paper cites Artificial intelligence in oncology: Current landscape, challenges, and future directions,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Artificial intelligence in oncology: Current landscape, challenges, and future directions,

Reference 7

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This paper cites Bias and class imbalance in oncologic data—towards inclusive and transferrable ai in large scale oncology data sets,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Bias and class imbalance in oncologic data—towards inclusive and transferrable ai in large scale oncology data sets,

Reference 8

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This paper cites Missing data in multi-omics integration: Recent advances through artificial intelligence,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Missing data in multi-omics integration: Recent advances through artificial intelligence,

Reference 9

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This paper cites Learning from noisy labels with deep neural networks: A survey,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Learning from noisy labels with deep neural networks: A survey,

Reference 10

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This paper cites High-dimensional data analysis: The curses and blessings of dimensionality,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data High-dimensional data analysis: The curses and blessings of dimensionality,

Reference 11

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This paper cites A universal law of robustness via isoperimetry,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data A universal law of robustness via isoperimetry,

Reference 12

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This paper cites Quantum computing for oncology,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Quantum computing for oncology,

Reference 13

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This paper cites A Framework for Quantum Advantage.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data A Framework for Quantum Advantage

Reference 14

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This paper cites Challenges and opportunities in quantum machine learning,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Challenges and opportunities in quantum machine learning,

Reference 15

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This paper cites Better than classical? The subtle art of benchmarking quantum machine learning models.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Better than classical? The subtle art of benchmarking quantum machine learning models

Reference 16

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Observation 6a6e3b1f-428d-4823-ba7c-6920058c6d81 · outbound

This paper cites Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers

Reference 17

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This paper cites How quantum computing can enhance biomarker discovery,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data How quantum computing can enhance biomarker discovery,

Reference 18

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This paper cites Generalization in quantum machine learning from few training data,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Generalization in quantum machine learning from few training data,

Reference 19

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Observation 3a4c4df5-1224-4fac-9df6-bb75cf887060 · outbound

This paper cites The power of quantum neural networks,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data The power of quantum neural networks,

Reference 20

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Observation 0282bf62-b5d4-4dc4-a9b9-9b45baa5424f · outbound

This paper cites Quantum machine learning advantages beyond hardness of evaluation,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Quantum machine learning advantages beyond hardness of evaluation,

Reference 21

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This paper cites Is quantum advantage the right goal for quantum machine learning?.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Is quantum advantage the right goal for quantum machine learning?

Reference 22

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This paper cites Power of data in quantum machine learning,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Power of data in quantum machine learning,

Reference 23

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Observation d931d917-e6c7-4035-8530-58822f9e5fa6 · outbound

This paper cites Quantum computing for genomics: conceptual challenges and practical perspectives,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Quantum computing for genomics: conceptual challenges and practical perspectives,

Reference 24

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Observation b33c2be4-c71b-4173-bf8a-2a636e3c0388 · outbound

This paper cites A systematic review of quantum machine learning for digital health,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data A systematic review of quantum machine learning for digital health,

Reference 25

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This paper cites Bit-bit encoding, optimizer-free training and sub-net initialization: techniques for scalable quantum machine learning.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Bit-bit encoding, optimizer-free training and sub-net initialization: techniques for scalable quantum machine learning

Reference 26

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This paper cites How many qubits does a machine learning problem require?.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data How many qubits does a machine learning problem require?

Reference 27

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This paper cites Ribeiro, A.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Ribeiro, A

Reference 28

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Observation 5f9ef7b8-7a00-4d91-b19a-fac51fdb5039 · outbound

This paper cites Breast Cancer Wisconsin (Diagnostic),.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Breast Cancer Wisconsin (Diagnostic),

Reference 29

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This paper cites Nuclear feature extraction for breast tumor diagnosis,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Nuclear feature extraction for breast tumor diagnosis,

Reference 30

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This paper cites Design and analysis of quantum powered support vector machines for malignant breast cancer diagnosis,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Design and analysis of quantum powered support vector machines for malignant breast cancer diagnosis,

Reference 31

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This paper cites Clinical data classification with noisy intermediate scale quantum computers,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Clinical data classification with noisy intermediate scale quantum computers,

Reference 32

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Observation ee4e2ef8-6d70-4a7d-a49f-9d3b2368216b · outbound

This paper cites Demonstration of breast cancer detection using qsvm on ibm quantum processors,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Demonstration of breast cancer detection using qsvm on ibm quantum processors,

Reference 33

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Observation c8032755-c38a-4380-adc3-fc0a8da0906b · outbound

This paper cites Quantum machine learning for breast cancer detection: a comparative study with conven- tional machine learning methods,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Quantum machine learning for breast cancer detection: a comparative study with conven- tional machine learning methods,

Reference 34

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Observation 4e2c99d7-a0bc-431b-9107-48e9a22ee4c5 · outbound

This paper cites A novel feature selection method based on quantum support vector machine,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data A novel feature selection method based on quantum support vector machine,

Reference 35

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Observation 5ba5ca2f-619a-4bd5-826c-0af869afeac6 · outbound

This paper cites Comparison of machine learning and quantum machine learning for breast cancer detection,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Comparison of machine learning and quantum machine learning for breast cancer detection,

Reference 36

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This paper cites Integrating xai with quan- tum machine learning models for interpretable breast cancer clas- sification,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Integrating xai with quan- tum machine learning models for interpretable breast cancer clas- sification,

Reference 37

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This paper cites Harnessing quantum-classical techniques for improved breast cancer prediction,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Harnessing quantum-classical techniques for improved breast cancer prediction,

Reference 38

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This paper cites Quantum processor-inspired machine learning in the biomedical sciences,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Quantum processor-inspired machine learning in the biomedical sciences,

Reference 39

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This paper cites Potential of quantum machine learning for solving the real-world problem of cancer classification,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Potential of quantum machine learning for solving the real-world problem of cancer classification,

Reference 40

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This paper cites Available: https://doi.org/10.1016/j.patter.2021.100246.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Available: https://doi.org/10.1016/j.patter.2021.100246

Reference 41

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This paper cites Investigating the application of quantum machine learning in breast cancer: A systematic review: Quantum machine learning in bc,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Investigating the application of quantum machine learning in breast cancer: A systematic review: Quantum machine learning in bc,

Reference 42

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This paper cites Biomarker discovery with quantum neural networks: a case-study in ctla4-activation pathways,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Biomarker discovery with quantum neural networks: a case-study in ctla4-activation pathways,

Reference 43

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Observation 36d7a13b-93e9-4575-8ebc-804d1cb4bcac · outbound

This paper cites Mlomics: Cancer multi-omics database for machine learning,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Mlomics: Cancer multi-omics database for machine learning,

Reference 44

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This paper cites Multi-omic and quantum machine learning integration for lung subtypes classification,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Multi-omic and quantum machine learning integration for lung subtypes classification,

Reference 45

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This paper cites Quantum machine and deep learning for medical image classification: A systematic review of trends, methodologies, and future directions,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Quantum machine and deep learning for medical image classification: A systematic review of trends, methodologies, and future directions,

Reference 46

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This paper cites Quantum machine learning in medical image analysis: A survey,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Quantum machine learning in medical image analysis: A survey,

Reference 47

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This paper cites Universal adversarial perturbations for multiple classification tasks with quantum classifiers,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Universal adversarial perturbations for multiple classification tasks with quantum classifiers,

Reference 48

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This paper cites Medmnist v2 - a large-scale lightweight benchmark for 2d and 3d biomedical image classification,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Medmnist v2 - a large-scale lightweight benchmark for 2d and 3d biomedical image classification,

Reference 49

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This paper cites Extreme Learning Machines for Attention-based Multiple Instance Learning in Whole-Slide Image Classification.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Extreme Learning Machines for Attention-based Multiple Instance Learning in Whole-Slide Image Classification

Reference 50

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This paper cites Quantum Methods for Neural Networks and Application to Medical Image Classification,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Quantum Methods for Neural Networks and Application to Medical Image Classification,

Reference 51

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This paper cites Quantum machine learning approaches for high-dimensional cancer genomics data analysis,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Quantum machine learning approaches for high-dimensional cancer genomics data analysis,

Reference 52

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This paper cites Hybrid quantum-classical neural network for breast cancer detection,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Hybrid quantum-classical neural network for breast cancer detection,

Reference 53

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Observation 84777311-d522-4bad-86ea-0d6dc0e01614 · outbound

This paper cites Deep residual learning for image recognition,.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Deep residual learning for image recognition,

Reference 54

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Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Scikit-learn: Machine learning in Python,

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Benchmarking Quantum and Classical Machine Learning Models on Oncological Data A systematic review of quantum image processing: Representation, applications and future perspectives,

Reference 56

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Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Imagenet: A large-scale hierarchical image database,

Reference 57

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Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Effect of data encoding on the expressive power of variational quantum-machine-learning models,

Reference 58

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Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Encoding patterns for quantum algorithms,

Reference 59

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Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Computational power of random quantum circuits in arbitrary geometries,

Reference 60

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Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Limitations of Amplitude Encoding on Quantum Classification

Reference 61

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Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Does provable absence of barren plateaus imply classical simulability?

Reference 62

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Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Barren plateaus in quantum neural network training landscapes,

Reference 63

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Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Hyperparameter importance and optimization of quantum neural networks across small datasets,

Reference 64

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Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Variational quantum algorithms,

Reference 65

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Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Shot optimization in quantum machine learning architectures to accelerate training,

Reference 66

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Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Adaptive shot allocation for fast convergence in variational quantum algorithms,

Reference 67

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Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Adaptive shot allocation for fast convergence in variational quantum algorithms

Reference 68

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Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Challenges of variational quantum optimization with measurement shot noise,

Reference 70

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Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Accurate cancer classification using expressions of very few genes,

Reference 71

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This paper cites Available: https://doi.org/10.1515/jisys-2020-0089.

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data Available: https://doi.org/10.1515/jisys-2020-0089

Reference 2021

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