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Radiomics

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Distinct brain morphometry patterns revealed by deep learning improve prediction of post-stroke aphasia severity

Teghipco, A., Newman-Norlund, R., Fridriksson, J., Rorden, C., & Bonilha, L. (2024) · Communications Medicine · doi:10.1038/s43856-024-00541-8 · Cited by 11

Multi-resolution characterization of molecular taxonomies in bulk and single-cell transcriptomics data

Reed, E. R., & Monti, S. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab552 · Cited by 11

Identification of telomere-related genes associated with aging-related molecular clusters and the construction of a diagnostic model in Alzheimer's disease based on a bioinformatic analysis

Ruan, Y., Lv, W., Li, S., Cheng, Y., Wang, D., Zhang, C., & Shimizu, K. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106922 · Cited by 11

Default mode and frontoparietal network dynamics: Associations with familial risk for depression and stress sensitivity

Belleau, E. L., Kremens, R., Bolton, T. A. W., Bondy, E., Pisoni, A., Auerbach, R. P., & Pizzagalli, D. A. (2023) · Journal of Mood and Anxiety Disorders · doi:10.1016/j.xjmad.2023.100001 · Cited by 12

Proteomic profiling identifies muscle-invasive bladder cancers with distinct biology and responses to platinum-based chemotherapy

Contreras‐Sanz, A., Negri, G. L., Reike, M. J., Oo, H. Z., Scurll, J., Miko, S. E. S., Nielsen, K., Ikeda, K., Wang, G., Jackson, C. L., et al. (2025) · Nature Communications · doi:10.1038/s41467-024-55665-1 · Cited by 11

Multi-Omics clustering by integrating clinical features from large language model

Ye, X., Shi, T., Huang, D., & Sakurai, T. (2025) · Methods · doi:10.1016/j.ymeth.2025.03.017 · Cited by 12

Treatment effects of Xuebijing injection in patients with sepsis by clinical phenotype: a post hoc analysis of the EXIT-SEP trial

Lou, X., Chen, H., Shi, N., Yu, R., Li, S., Yang, Y., Liu, S., Xie, J., & Qiu, H. (2025) · EClinicalMedicine · doi:10.1016/j.eclinm.2025.103341 · Cited by 11

Olfactory Dysfunction After SARS-CoV-2 Infection in the RECOVER Adult Cohort

Horwitz, L. I., Becker, J., Huang, W., Akintonwa, T., Hornig-Rohan, M. M., Maranga, G., Adams, D. R., Albers, M. W., Ayache, M. B., Berry, J., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.33815 · Cited by 11

Hemodynamic Estimation Based on Consensus Clustering

Badillo, S., Varoquaux, G. P., & Ciuciu, P. (2013) · International Workshop on Pattern Recognition in NeuroImaging · doi:10.1109/prni.2013.61 · Cited by 11

Proteomic and Phosphoproteomic Maps of Lung Squamous Cell Carcinoma From Chinese Patients

Pan, L., Wang, X., Yang, L., Zhao, L., Zhai, L., Xu, J., Yang, Y., Mao, Y., Cheng, S., Xiao, T., et al. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.00963 · Cited by 11

Ensemble clustering method based on the resampling similarity measure for gene expression data

Kim, S. C., & Lee, J. W. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206071842 · Cited by 16

Consensus Clustering for Microarray Gene Expression Data

Muthukalathi, S., Ramanujam, R., & Thalamuthu, A. (2014) · Bonfring International Journal of Data Mining · doi:10.9756/bijdm.6140 · Cited by 3

Consensus Clustering in Gene Expression

Galdi, P., Napolitano, F., & Tagliaferri, R. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24462-4_5 · Cited by 11

Consensus Clustering for Time Course Gene Expression Microarray Data

Kim, S., & Bae, J. S. (2005) · Communications for Statistical Applications and Methods · doi:10.5351/ckss.2005.12.2.335

Analysis of gene expression data: clustering and beyond

Yakhini, Z., Ben‐Dor, A., Kim, S. K., & Ron Shamir, A. (1999) · Nature Genetics · doi:10.1038/14430 · Cited by 1