Predicting chemotherapy response using a variational autoencoder approach

Background: Multiple studies have shown the utility of transcriptome-wide RNA-seq profiles as features for machine learning-based prediction of response to chemotherapy in cancer. While tumor transcriptome profiles are publicly available for thousands of tumors for many cancer types, a relatively mo...

Full description

Bibliographic Details
Main Authors:	Ramsey, S.A (Author), Wei, Q. (Author)
Format:	Article
Language:	English
Published:	BioMed Central Ltd 2021
Subjects:	Auto encoders Biological Phenomena Bladder carcinoma Breast invasive carcinoma Cancer cellular, subcellular and molecular biological phenomena and functions Chemotherapy Chemotherapy drug response classification Classification (of information) Colon adenocarcinoma Colon adenocarcinomas Decision trees Deep neural networks Diseases Drug response Encoding (symbols) Forecasting Gene expression genetics human Humans machine learning Machine Learning neoplasm Neoplasms Neural Networks, Computer Pancreatic adenocarcinoma Sarcoma Signal encoding Supervised learning TCGA transcriptome Transcriptome Transcriptomes Tumors Variational auto-encoder
Online Access:	View Fulltext in Publisher

Internet

View Fulltext in Publisher

Predicting chemotherapy response using a variational autoencoder approach

Internet

Similar Items