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Academic Journal

Machine-Learning-Based Prediction of Treatment Outcomes Using MR Imaging-Derived Quantitative Tumor Information in Patients with Sinonasal Squamous Cell Carcinomas: A Preliminary Study.

Authors : Fujima, Noriyuki¹ (AUTHOR) ; Shimizu, Yukie¹ (AUTHOR) ; Yoshida, Daisuke¹ (AUTHOR)

Source: Cancers. Jun2019, Vol. 11 Issue 6, p800. 1p.

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Academic Journal

Comprehensive Outline of Whole Exome Sequencing Data Analysis Tools Available in Clinical Oncology.

Authors : Bartha, Áron^1,2 (AUTHOR) ; Győrffy, Balázs^1,2 (AUTHOR)

Source: Cancers. Nov2019, Vol. 11 Issue 11, p1725-1725. 1p.

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Academic Journal

Mining-Guided Machine Learning Analyses Revealed the Latest Trends in Neuro-Oncology.

Authors : Hana, Taijun¹ (AUTHOR) ; Tanaka, Shota¹ (AUTHOR) ; Nejo, Takahide¹ (AUTHOR)

Source: Cancers. Feb2019, Vol. 11 Issue 2, p178. 1p.

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Academic Journal

Applications of Bioinformatics in Cancer.

Authors : Brenner, Chad^1,2,3 (AUTHOR)

Source: Cancers. Nov2019, Vol. 11 Issue 11, p1630-1630. 1p.

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Academic Journal

A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine Learning.

Authors : Polano, Maurizio¹ (AUTHOR) ; Chierici, Marco² (AUTHOR) ; Dal Bo, Michele¹ (AUTHOR)

Source: Cancers. Oct2019, Vol. 11 Issue 10, p1562. 1p.

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Academic Journal

Use of Machine-Learning Algorithms in Intensified Preoperative Therapy of Pancreatic Cancer to Predict Individual Risk of Relapse.

Authors : Sala Elarre, Pablo¹ (AUTHOR) ; Oyaga-Iriarte, Esther² (AUTHOR) ; Yu, Kenneth H.^3,4 (AUTHOR)

Source: Cancers. May2019, Vol. 11 Issue 5, p606. 1p.

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Academic Journal

Validation of miRNAs as Breast Cancer Biomarkers with a Machine Learning Approach.

Authors : Rehman, Oneeb¹ (AUTHOR) ; Zhuang, Hanqi¹ (AUTHOR) ; Muhamed Ali, Ali¹ (AUTHOR)

Source: Cancers. Mar2019, Vol. 11 Issue 3, p431-431. 1p.

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Academic Journal

Breast Cancer Prognosis Using a Machine Learning Approach.

Authors : Ferroni, Patrizia^1,2 (AUTHOR) ; Zanzotto, Fabio M.³ (AUTHOR) ; Riondino, Silvia^1,4 (AUTHOR)

Source: Cancers. Mar2019, Vol. 11 Issue 3, p328-328. 1p.

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Academic Journal

Developing a Prognostic Gene Panel of Epithelial Ovarian Cancer Patients by a Machine Learning Model.

Authors : Lu, Tzu-Pin¹ (AUTHOR) ; Kuo, Kuan-Ting² (AUTHOR) ; Chen, Ching-Hsuan¹ (AUTHOR)

Source: Cancers. Feb2019, Vol. 11 Issue 2, p270. 1p.

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Academic Journal

An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer.

Authors : Long, Nguyen Phuoc¹ (AUTHOR) ; Jung, Kyung Hee² (AUTHOR) ; Anh, Nguyen Hoang¹ (AUTHOR)

Source: Cancers. Feb2019, Vol. 11 Issue 2, p155. 1p.

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Machine-Learning-Based Prediction of Treatment Outcomes Using MR Imaging-Derived Quantitative Tumor Information in Patients with Sinonasal Squamous Cell Carcinomas: A Preliminary Study.

Comprehensive Outline of Whole Exome Sequencing Data Analysis Tools Available in Clinical Oncology.

Mining-Guided Machine Learning Analyses Revealed the Latest Trends in Neuro-Oncology.

Applications of Bioinformatics in Cancer.

A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine Learning.

Use of Machine-Learning Algorithms in Intensified Preoperative Therapy of Pancreatic Cancer to Predict Individual Risk of Relapse.

Validation of miRNAs as Breast Cancer Biomarkers with a Machine Learning Approach.

Breast Cancer Prognosis Using a Machine Learning Approach.

Developing a Prognostic Gene Panel of Epithelial Ovarian Cancer Patients by a Machine Learning Model.

An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer.

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