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Catherine H Feng

Showing results (1-10 of 8) with videos related to

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Transactions on Artificial Intelligence|July 9, 2025
Normalization and Selecting Non-Differentially Expressed Genes Improve Machine Learning Modelling of Cross-Platform Transcriptomic DataFei Deng, Catherine H Feng, Nan Gao, et al.
Arxiv|February 20, 2025
Normalization and selecting non-differentially expressed genes improve machine learning modelling of cross-platform transcriptomic dataFei Deng, Catherine H Feng, Nan Gao, et al.
Laboratory Investigation; a Journal of Technical Methods and Pathology|September 19, 2021
Multimetric feature selection for analyzing multicategory outcomes of colorectal cancer: random forest and multinomial logistic regression modelsCatherine H Feng, Mary L Disis, Chao Cheng, et al.
Biorxiv : the Preprint Server for Biology|March 3, 2025
Towards machine learning fairness in classifying multicategory causes of deaths in colorectal or lung cancer patientsCatherine H Feng, Fei Deng, Mary L Disis, et al.
Briefings in Bioinformatics|August 12, 2025
Towards machine learning fairness in classifying multicategory causes of deaths in colorectal or lung cancer patientsCatherine H Feng, Fei Deng, Mary L Disis, et al.
Cancer Research Communications|March 23, 2023
The Ubiquitin-specific Protease USP36 Associates with the Microprocessor Complex and Regulates miRNA Biogenesis by SUMOylating DGCR8Yanping Li, Timothy S Carey, Catherine H Feng, et al.
Biorxiv : the Preprint Server for Biology|February 20, 2025
Clinico-genomic features predict distinct metastatic phenotypes in cutaneous melanomaTyler J Aprati, Chi-Ping Day, Daniel Lee, et al.
Science Advances|November 27, 2024
Genomic heterogeneity and ploidy identify patients with intrinsic resistance to PD-1 blockade in metastatic melanomaGiuseppe Tarantino, Cora A Ricker, Annette Wang, et al.
Pageof 1

Showing results (1-10 of 8) with videos related to

Sort By:
Pageof 1
Transactions on Artificial Intelligence|July 9, 2025
Normalization and Selecting Non-Differentially Expressed Genes Improve Machine Learning Modelling of Cross-Platform Transcriptomic DataFei Deng, Catherine H Feng, Nan Gao, et al.
Arxiv|February 20, 2025
Normalization and selecting non-differentially expressed genes improve machine learning modelling of cross-platform transcriptomic dataFei Deng, Catherine H Feng, Nan Gao, et al.
Laboratory Investigation; a Journal of Technical Methods and Pathology|September 19, 2021
Multimetric feature selection for analyzing multicategory outcomes of colorectal cancer: random forest and multinomial logistic regression modelsCatherine H Feng, Mary L Disis, Chao Cheng, et al.
Biorxiv : the Preprint Server for Biology|March 3, 2025
Towards machine learning fairness in classifying multicategory causes of deaths in colorectal or lung cancer patientsCatherine H Feng, Fei Deng, Mary L Disis, et al.
Briefings in Bioinformatics|August 12, 2025
Towards machine learning fairness in classifying multicategory causes of deaths in colorectal or lung cancer patientsCatherine H Feng, Fei Deng, Mary L Disis, et al.
Cancer Research Communications|March 23, 2023
The Ubiquitin-specific Protease USP36 Associates with the Microprocessor Complex and Regulates miRNA Biogenesis by SUMOylating DGCR8Yanping Li, Timothy S Carey, Catherine H Feng, et al.
Biorxiv : the Preprint Server for Biology|February 20, 2025
Clinico-genomic features predict distinct metastatic phenotypes in cutaneous melanomaTyler J Aprati, Chi-Ping Day, Daniel Lee, et al.
Science Advances|November 27, 2024
Genomic heterogeneity and ploidy identify patients with intrinsic resistance to PD-1 blockade in metastatic melanomaGiuseppe Tarantino, Cora A Ricker, Annette Wang, et al.
Pageof 1