A deep neural network approach to predicting clinical outcomes of neuroblastoma patients
Background: The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and disease recurrence. Such methods are also important to better understand the biological mechanisms u...
Main Authors: | Tranchevent, Léon-Charles, Azuaje, Francisco, Rajapakse, Jagath Chandana |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/146977 |
Similar Items
-
A deep neural network approach to predicting clinical outcomes of neuroblastoma patients
by: Léon-Charles Tranchevent, et al.
Published: (2019-12-01) -
Network-based integration of multi-omics data for clinical outcome prediction in neuroblastoma
by: Wang, Conghao, et al.
Published: (2023) -
Decoding task specific and task general functional architectures of the brain
by: Gupta, Sukrit, et al.
Published: (2022) -
Prediction of G4 formation in live cells with epigenetic data: a deep learning approach
by: Korsakova, Anna, et al.
Published: (2023) -
Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models
by: Baum, Katharina, et al.
Published: (2020)