Using machine learning approaches for multi-omics data analysis: A review
With the development of modern high-throughput omic measurement platforms, it has
become essential for biomedical studies to undertake an integrative (combined) approach to …
become essential for biomedical studies to undertake an integrative (combined) approach to …
Integrated multi-omics analyses in oncology: a review of machine learning methods and tools
In recent years, high-throughput sequencing technologies provide unprecedented
opportunity to depict cancer samples at multiple molecular levels. The integration and …
opportunity to depict cancer samples at multiple molecular levels. The integration and …
Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification
Background Machine learning is a sub-field of artificial intelligence, which utilises large data
sets to make predictions for future events. Although most algorithms used in machine …
sets to make predictions for future events. Although most algorithms used in machine …
Integrative methods for analyzing big data in precision medicine
We provide an overview of recent developments in big data analyses in the context of
precision medicine and health informatics. With the advance in technologies capturing …
precision medicine and health informatics. With the advance in technologies capturing …
Circulating cell-free DNA in breast cancer: size profiling, levels, and methylation patterns lead to prognostic and predictive classifiers
Blood circulating cell-free DNA (ccfDNA) is a suggested biosource of valuable clinical
information for cancer, meeting the need for a minimally-invasive advancement in the route …
information for cancer, meeting the need for a minimally-invasive advancement in the route …
moBRCA-net: a breast cancer subtype classification framework based on multi-omics attention neural networks
JM Choi, H Chae - BMC bioinformatics, 2023 - Springer
Background Breast cancer is a highly heterogeneous disease that comprises multiple
biological components. Owing its diversity, patients have different prognostic outcomes; …
biological components. Owing its diversity, patients have different prognostic outcomes; …
BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data
Y Guo, S Liu, Z Li, X Shang - BMC bioinformatics, 2018 - Springer
Background The classification of cancer subtypes is of great importance to cancer disease
diagnosis and therapy. Many supervised learning approaches have been applied to cancer …
diagnosis and therapy. Many supervised learning approaches have been applied to cancer …
A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subty**
A Sathyanarayanan, R Gupta… - Briefings in …, 2020 - academic.oup.com
Oncogenesis and cancer can arise as a consequence of a wide range of genomic
aberrations including mutations, copy number alterations, expression changes and …
aberrations including mutations, copy number alterations, expression changes and …
NCG 5.0: updates of a manually curated repository of cancer genes and associated properties from cancer mutational screenings
Abstract The Network of Cancer Genes (NCG, http://ncg. kcl. ac. uk/) is a manually curated
repository of cancer genes derived from the scientific literature. Due to the increasing …
repository of cancer genes derived from the scientific literature. Due to the increasing …
Machine learning analysis of TCGA cancer data
In recent years, machine learning (ML) researchers have changed their focus towards
biological problems that are difficult to analyse with standard approaches. Large initiatives …
biological problems that are difficult to analyse with standard approaches. Large initiatives …