[HTML][HTML] PathME: pathway based multi-modal sparse autoencoders for clustering of patient-level multi-omics data
A Lemsara, S Ouadfel, H Fröhlich - BMC bioinformatics, 2020 - Springer
Background Recent years have witnessed an increasing interest in multi-omics data,
because these data allow for better understanding complex diseases such as cancer on a …
because these data allow for better understanding complex diseases such as cancer on a …
Representation learning for the clustering of multi-omics data
G Viaud, P Mayilvahanan… - IEEE/ACM Transactions …, 2021 - ieeexplore.ieee.org
The integration of several sources of data for the identification of subtypes of diseases has
gained attention over the past few years. The heterogeneity and the high dimensions of the …
gained attention over the past few years. The heterogeneity and the high dimensions of the …
Integrative clustering of multi-level omics data for disease subtype discovery using sequential double regularization
With the rapid advances in technologies of microarray and massively parallel sequencing,
data of multiple omics sources from a large patient cohort are now frequently seen in many …
data of multiple omics sources from a large patient cohort are now frequently seen in many …
MODEC: an unsupervised clustering method integrating omics data for identifying cancer subtypes
Y Zhang, H Kiryu - Briefings in Bioinformatics, 2022 - academic.oup.com
The identification of cancer subtypes can help researchers understand hidden genomic
mechanisms, enhance diagnostic accuracy and improve clinical treatments. With the …
mechanisms, enhance diagnostic accuracy and improve clinical treatments. With the …
Capturing the latent space of an Autoencoder for multi-omics integration and cancer subtyping
S Paul - Computers in Biology and Medicine, 2022 - Elsevier
Abstract Background and Objective: The motivation behind cancer subtyping is to identify
subgroups of cancer patients with distinguishable phenotypes of clinical importance. It can …
subgroups of cancer patients with distinguishable phenotypes of clinical importance. It can …
Robust clustering of noisy high-dimensional gene expression data for patients subtyping
P Coretto, A Serra, R Tagliaferri - Bioinformatics, 2018 - academic.oup.com
Motivation One of the most important research areas in personalized medicine is the
discovery of disease sub-types with relevance in clinical applications. This is usually …
discovery of disease sub-types with relevance in clinical applications. This is usually …
Integrated multi-omics analysis using variational autoencoders: application to pan-cancer classification
Omics data are normally high dimensional with large number of molecular features and
relatively small number of available samples with clinical labels. The “curse of …
relatively small number of available samples with clinical labels. The “curse of …
Integrating multidimensional data for clustering analysis with applications to cancer patient data
Advances in high-throughput genomic technologies coupled with large-scale studies
including The Cancer Genome Atlas (TCGA) project have generated rich resources of …
including The Cancer Genome Atlas (TCGA) project have generated rich resources of …
[HTML][HTML] Multilevel omic data integration in cancer cell lines: advanced annotation and emergent properties
Y Liu, V Devescovi, S Chen, C Nardini - BMC systems biology, 2013 - Springer
Background High-throughput (omic) data have become more widespread in both quantity
and frequency of use, thanks to technological advances, lower costs and higher precision …
and frequency of use, thanks to technological advances, lower costs and higher precision …
Multi-omics clustering for cancer subtyping based on latent subspace learning
The increased availability of high-throughput technologies has enabled biomedical
researchers to learn about disease etiology across multiple omics layers, which shows …
researchers to learn about disease etiology across multiple omics layers, which shows …