Machine Learning Approaches to Classify Primary and Metastatic Cancers Using Tissue of Origin-Based DNA Methylation Profiles
Metastatic cancers account for up to 90% of cancer-related deaths. The clear differentiation of metastatic cancers from primary cancers is crucial for cancer type identification and developing targeted treatment for each cancer type. DNA methylation patterns are suggested to be an intriguing target...
Main Authors: | Vijayachitra Modhukur, Shakshi Sharma, Mainak Mondal, Ankita Lawarde, Keiu Kask, Rajesh Sharma, Andres Salumets |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-07-01
|
Series: | Cancers |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-6694/13/15/3768 |
Similar Items
-
MBMethPred: a computational framework for the accurate classification of childhood medulloblastoma subgroups using data integration and AI-based approaches
by: Edris Sharif Rahmani, et al.
Published: (2023-09-01) -
ExplORRNet: An interactive web tool to explore stage-wise miRNA expression profiles and their interactions with mRNA and lncRNA in human breast and gynecological cancers
by: Ankita Lawarde, et al.
Published: (2024-03-01) -
The pan-cancer landscape of abnormal DNA methylation and intratumor microorganisms
by: Ping Zhou, et al.
Published: (2023-03-01) -
DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma
by: Duoduo Xu, et al.
Published: (2022-04-01) -
A suite of DNA methylation markers that can detect most common human cancers
by: Lukas Vrba, et al.
Published: (2018-01-01)