Related references
Note: Only part of the references are listed.Identification of Prognostic Genes in Gliomas Based on Increased Microenvironment Stiffness
Chaang-Ray Chen et al.
CANCERS (2022)
Multimodal biomedical AI
Julian N. Acosta et al.
NATURE MEDICINE (2022)
A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening
Di He et al.
NATURE MACHINE INTELLIGENCE (2022)
Harnessing multimodal data integration to advance precision oncology
Kevin M. Boehm et al.
NATURE REVIEWS CANCER (2022)
ADD3 Deletion in Glioblastoma Predicts Disease Status and Survival
Karrie Mei-Yee Kiang et al.
FRONTIERS IN ONCOLOGY (2021)
Digital medicine and the curse of dimensionality
Visar Berisha et al.
NPJ DIGITAL MEDICINE (2021)
Investigation of REFINED CNN ensemble learning for anti-cancer drug sensitivity prediction
Omid Bazgir et al.
BIOINFORMATICS (2021)
Converting tabular data into images for deep learning with convolutional neural networks
Yitan Zhu et al.
SCIENTIFIC REPORTS (2021)
A survival model generalized to regression learning algorithms
Yuanfang Guan et al.
NATURE COMPUTATIONAL SCIENCE (2021)
Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients
Jianzhu Ma et al.
NATURE CANCER (2021)
Representation of features as images with neighborhood dependencies for compatibility with convolutional neural networks
Omid Bazgir et al.
NATURE COMMUNICATIONS (2020)
The RNA helicase DHX33 is required for cancer cell proliferation in human glioblastoma and confers resistance to PI3K/mTOR inhibition
Hongzhong Wang et al.
CELLULAR SIGNALLING (2019)
DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network
Jared L. Katzman et al.
BMC MEDICAL RESEARCH METHODOLOGY (2018)
Using deep learning to model the hierarchical structure and function of a cell
Jianzhu Ma et al.
NATURE METHODS (2018)
Investigation of model stacking for drug sensitivity prediction
Kevin Matlock et al.
BMC BIOINFORMATICS (2018)
Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models
Safoora Yousefi et al.
SCIENTIFIC REPORTS (2017)
Mitochondrial respiratory gene expression is suppressed in many cancers
Ed Reznik et al.
ELIFE (2017)
Development and Validation of a Prediction Rule for Benefit and Harm of Dual Antiplatelet Therapy Beyond 1 Year After Percutaneous Coronary Intervention
Robert W. Yeh et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2016)
PPIC, EMP3 and CHI3L1 Are Novel Prognostic Markers for High Grade Glioma
Yuan-Feng Gao et al.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2016)
Zinc finger proteins in cancer progression
Jayu Jen et al.
JOURNAL OF BIOMEDICAL SCIENCE (2016)
Secretory prostate apoptosis response (Par)-4 sensitizes multicellular spheroids (MCS) of glioblastoma multiforme cells to tamoxifen-induced cell death
Jayashree C. Jagtap et al.
FEBS OPEN BIO (2015)
Identification of high-quality cancer prognostic markers and metastasis network modules
Jie Li et al.
NATURE COMMUNICATIONS (2010)
RANDOM SURVIVAL FORESTS
Hemant Ishwaran et al.
ANNALS OF APPLIED STATISTICS (2008)
Laplacian eigenmaps for dimensionality reduction and data representation
M Belkin et al.
NEURAL COMPUTATION (2003)
Comparison of the performance of neural network methods and Cox regression for censored survival data
A Xiang et al.
COMPUTATIONAL STATISTICS & DATA ANALYSIS (2000)
Share Paper
