Cargando…
A supervised machine learning approach identifies gene‐regulating factor‐mediated competing endogenous RNA networks in hormone‐dependent cancers
Competing endogenous RNAs (ceRNAs) have become an emerging topic in cancer research due to their role in gene regulatory networks. To date, traditional ceRNA bioinformatic studies have investigated microRNAs as the only factor regulating gene expression. Growing evidence suggests that genomic (e.g.,...
Autores principales: | Jayarathna, Dulari K., Rentería, Miguel E., Batra, Jyotsna, Gandhi, Neha S. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9542250/ https://www.ncbi.nlm.nih.gov/pubmed/35757968 http://dx.doi.org/10.1002/jcb.30300 |
Ejemplares similares
-
Integrative competing endogenous RNA network analyses identify novel lncRNA and genes implicated in metastatic breast cancer
por: Jayarathna, Dulari K., et al.
Publicado: (2023) -
Identifying Complex lncRNA/Pseudogene–miRNA–mRNA Crosstalk in Hormone-Dependent Cancers
por: Jayarathna, Dulari K., et al.
Publicado: (2021) -
Integrative Transcriptome-Wide Analyses Uncover Novel Risk-Associated MicroRNAs in Hormone-Dependent Cancers
por: Jayarathna, Dulari K., et al.
Publicado: (2021) -
Supervised machine learning with Python: develop rich Python coding practices while exploring supervised machine learning
por: Smith, Taylor
Publicado: (2019) -
Comparing supervised and semi-supervised Machine Learning Models on Diagnosing Breast Cancer
por: Al-Azzam, Nosayba, et al.
Publicado: (2021)