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Publication search results
found 19 matches
- 2024
- Martyna Kurbiel, Agata M. Wijata, Jakub Nalepa:
Predicting the MGMT Promoter Methylation Status in T2-FLAIR Magnetic Resonance Imaging Scans Using Machine Learning. ICPRAM 2024: 872-879 - 2023
- Tran Nguyen Tuan Minh, Viet-Huan Le, Nguyen-Quoc-Khanh Le:
Diffusion-tensor imaging and dynamic susceptibility contrast MRIs improve radiomics-based machine learning model of MGMT promoter methylation status in glioblastomas. Biomed. Signal Process. Control. 86(Part A): 105122 (2023) - Shahriar Faghani, Bardia Khosravi, Mana Moassefi, Gian Marco Conte, Bradley J. Erickson:
A Comparison of Three Different Deep Learning-Based Models to Predict the MGMT Promoter Methylation Status in Glioblastoma Using Brain MRI. J. Digit. Imaging 36(3): 837-846 (2023) - Numan Saeed, Muhammad Ridzuan, Hussain Alasmawi, Ikboljon Sobirov, Mohammad Yaqub:
MGMT promoter methylation status prediction using MRI scans? An extensive experimental evaluation of deep learning models. Medical Image Anal. 90: 102989 (2023) - Manel Mili, Asma Ben Abdallah, Jose Javier Otero, Asma Kerkeni, Mohamed Hedi Bedoui:
Revolutionizing Brain Cancer Diagnosis: Automated Prediction of MGMT Methylation Status using Histological Images. CW 2023: 149-156 - Numan Saeed, Muhammad Ridzuan, Hussain Alasmawi, Ikboljon Sobirov, Mohammad Yaqub:
MGMT promoter methylation status prediction using MRI scans? An extensive experimental evaluation of deep learning models. CoRR abs/2304.00774 (2023) - 2022
- Saksham Gupta:
Receptor Status Prediction in Breast Cancer Patients Using Machine Learning Pipeline on DNA Methylation Data. ICBBB 2022: 38-43 - 2021
- Sapir Margalit, Yotam Abramson, Hila Sharim, Zohar Manber, Surajit Bhattacharya, Yi-Wen Chen, Eric Vilain, Hayk Barseghyan, Ran Elkon, Roded Sharan, Yuval Ebenstein:
Long reads capture simultaneous enhancer-promoter methylation status for cell-type deconvolution. Bioinform. 37(Supplement): 327-333 (2021) - Sveinn Pálsson, Stefano Cerri, Koen Van Leemput:
Prediction of MGMT Methylation Status of Glioblastoma Using Radiomics and Latent Space Shape Features. BrainLes@MICCAI (2) 2021: 222-231 - Myesha Jahin, Benji Fenech-Salerno, Nicolas Moser, Pantelis Georgiou, James L. Flanagan, Chris Toumazou, Sara de Mateo, Melpomeni Kalofonou:
Detection of MGMT methylation status using a Lab-on-Chip compatible isothermal amplification method. EMBC 2021: 7385-7389 - Sveinn Pálsson, Stefano Cerri, Koen Van Leemput:
Prediction of MGMT Methylation Status of Glioblastoma using Radiomics and Latent Space Shape Features. CoRR abs/2109.12339 (2021) - Mihir Rao:
Novel Local Radiomic Bayesian Classifiers for Non-Invasive Prediction of MGMT Methylation Status in Glioblastoma. CoRR abs/2112.03259 (2021) - 2018
- Lichy Han, Maulik R. Kamdar:
MRI to MGMT: Predicting methylation status in glioblastoma patients using convolutional recurrent neural networks. PSB 2018: 331-342 - 2017
- Vasileios G. Kanas, Evangelia I. Zacharaki, Ginu A. Thomas, Pascal O. Zinn, Vasileios Megalooikonomou, Rivka R. Colen:
Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma. Comput. Methods Programs Biomed. 140: 249-257 (2017) - Panagiotis Korfiatis, Timothy L. Kline, Daniel H. Lachance, Ian F. Parney, Jan C. Buckner, Bradley J. Erickson:
Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status. J. Digit. Imaging 30(5): 622-628 (2017) - 2011
- Wei Zhang, Haiying Zheng, Juhua Zhang:
Nucleosome positioning plays an important role in predicting the methylation status of CpG islands. BMEI 2011: 1580-1583 - 2009
- Ilya Levner, Sylvia Drabycz, Gloria Roldan, Paula De Robles, J. Gregory Cairncross, Joseph Ross Mitchell:
Predicting MGMT Methylation Status of Glioblastomas from MRI Texture. MICCAI (1) 2009: 522-530 - 2007
- Christopher Previti, Oscar Harari, Igor Zwir, Coral del Val:
Novel approaches to the prediction of CpG islands and their methylation status. SCSC 2007: 833-840 - 2006
- Fang Fang, Shicai Fan, Xuegong Zhang, Michael Q. Zhang:
Predicting methylation status of CpG islands in the human brain. Bioinform. 22(18): 2204-2209 (2006)
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retrieved on 2024-09-23 05:11 CEST from data curated by the dblp team
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