An autoencoder learning method for predicting breast cancer subtypes.
Heterogeneity of breast cancer poses several challenges for detection and treatment. With next-generation sequencing, we can now map the transcriptional profile of each patient's breast tissue, which has the potential for identifying and characterizing cancer subtypes. However, the large dimens...
Saved in:
| Main Authors: | Zahra Rostami, Kavitha Mukund, Maryam Masnadi-Shirazi, Shankar Subramaniam |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0327773 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
An autoencoder learning method for predicting breast cancer subtypes
by: Zahra Rostami, et al.
Published: (2025-01-01) -
Moanna: Multi-Omics Autoencoder-Based Neural Network Algorithm for Predicting Breast Cancer Subtypes
by: Richard Lupat, et al.
Published: (2023-01-01) -
Plasmin Cascade Mediates Thrombotic Events in SARS-CoV-2 Infection via Complement and Platelet-Activating Systems
by: Kavitha Mukund, et al.
Published: (2020-01-01) -
Histone Signatures Predict Therapeutic Efficacy in Breast Cancer
by: Shamim A. Mollah, et al.
Published: (2020-01-01) -
Novel cancer subtyping method guided by tumor-normal sample in latent space of transcriptomic variational autoencoder
by: Hongzhi Wang, et al.
Published: (2025-07-01)