Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Yeping Lina Qiu"'
Autor:
Sandra Steyaert, Yeping Lina Qiu, Yuanning Zheng, Pritam Mukherjee, Hannes Vogel, Olivier Gevaert
Publikováno v:
Communications Medicine, Vol 3, Iss 1, Pp 1-15 (2023)
Steyaert, Qiu et al. develop a deep learning framework for multimodal data fusion for adult and pediatric brain tumors. Multimodal data models combining histopathology imaging and gene expression data outperform single data models in predicting progn
Externí odkaz:
https://doaj.org/article/9707d4a0fb3549e0948a146a37d960f8
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
RNA-sequencing data from tumours can be used to predict the prognosis of patients. Here, the authors show that a neural network meta-learning approach can be useful for predicting prognosis from a small number of samples.
Externí odkaz:
https://doaj.org/article/42bb85ba63e2410989419cfb8d38bbf2
Autor:
Sandra Steyaert, Yeping Lina Qiu, Yuanning Zheng, Pritam Mukherjee, Hannes Vogel, Olivier Gevaert
The introduction of deep learning in both imaging and genomics has significantly advanced the analysis of biomedical data. For complex diseases such as cancer different data modalities may reveal different disease characteristics, and the integration
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::519dcd72c19f44defa9c43ee359cf5f2
https://doi.org/10.1101/2022.09.21.22280223
https://doi.org/10.1101/2022.09.21.22280223
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
Nature Communications volume 11, Article number: 6350
Nature Communications
Nature Communications volume 11, Article number: 6350
Nature Communications
RNA sequencing has emerged as a promising approach in cancer prognosis as sequencing data becomes more easily and affordably accessible. However, it remains challenging to build good predictive models especially when the sample size is limited and th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b9d08fae53dd1c4e1e220fa829931cbc
https://doaj.org/article/42bb85ba63e2410989419cfb8d38bbf2
https://doaj.org/article/42bb85ba63e2410989419cfb8d38bbf2
RNA sequencing has emerged as a promising approach in cancer prognosis as sequencing data becomes more easily and affordably accessible. However, it remains challenging to build good predictive models especially when the sample size is limited and th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b333f4d754459f6b13a015ce55f5450
https://doi.org/10.1101/2020.04.21.053918
https://doi.org/10.1101/2020.04.21.053918
Publikováno v:
Neuro-Oncology
Brain tumors are the most common solid tumors affecting children, and its prognosis has been a great challenge for physicians and researchers. With the advances in high-throughput sequencing technology and digital pathology, more quantitative data is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a1e9284fd115726408ee2ed2c46899c4
https://doi.org/10.1093/neuonc/noaa222.708
https://doi.org/10.1093/neuonc/noaa222.708
MotivationThe presence of missing values is a frequent problem encountered in genomic data analysis. Lost data can be an obstacle to downstream analyses that require complete data matrices. State-of-the-art imputation techniques including Singular Va
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e2193b8895bc434d7b7f51054ce72d5b
https://doi.org/10.1101/406066
https://doi.org/10.1101/406066