Multimodal deep learning for biomedical data fusion: a review

SR Stahlschmidt, B Ulfenborg… - Briefings in …, 2022 - academic.oup.com
Biomedical data are becoming increasingly multimodal and thereby capture the underlying
complex relationships among biological processes. Deep learning (DL)-based data fusion …

[HTML][HTML] State of the field in multi-omics research: from computational needs to data mining and sharing

M Krassowski, V Das, SK Sahu, BB Misra - Frontiers in Genetics, 2020 - frontiersin.org
Multi-omics, variously called integrated omics, pan-omics, and trans-omics, aims to combine
two or more omics data sets to aid in data analysis, visualization and interpretation to …

[HTML][HTML] A benchmark study of deep learning-based multi-omics data fusion methods for cancer

D Leng, L Zheng, Y Wen, Y Zhang, L Wu, J Wang… - Genome biology, 2022 - Springer
Background A fused method using a combination of multi-omics data enables a
comprehensive study of complex biological processes and highlights the interrelationship of …

[HTML][HTML] DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data

OB Poirion, Z Jing, K Chaudhary, S Huang… - Genome medicine, 2021 - Springer
Multi-omics data are good resources for prognosis and survival prediction; however, these
are difficult to integrate computationally. We introduce DeepProg, a novel ensemble …

[HTML][HTML] Global computational alignment of tumor and cell line transcriptional profiles

A Warren, Y Chen, A Jones, T Shibue, WC Hahn… - Nature …, 2021 - nature.com
Cell lines are key tools for preclinical cancer research, but it remains unclear how well they
represent patient tumor samples. Direct comparisons of tumor and cell line transcriptional …

[HTML][HTML] Morphological and molecular characteristics of spheroid formation in HT-29 and Caco-2 colorectal cancer cell lines

E Gheytanchi, M Naseri, F Karimi-Busheri… - Cancer cell …, 2021 - Springer
Background Relapse and metastasis in colorectal cancer (CRC) are often attributed to
cancer stem-like cells (CSCs), as small sub-population of tumor cells with ability of drug …

[HTML][HTML] Performance comparison of deep learning autoencoders for cancer subtype detection using multi-omics data

EF Franco, P Rana, A Cruz, VV Calderon, V Azevedo… - Cancers, 2021 - mdpi.com
Simple Summary Here, we compared the performance of four different autoencoders:(a)
vanilla,(b) sparse,(c) denoising, and (d) variational for subtype detection on four cancer …

Deep latent space fusion for adaptive representation of heterogeneous multi-omics data

C Zhang, Y Chen, T Zeng, C Zhang… - Briefings in …, 2022 - academic.oup.com
The integration of multi-omics data makes it possible to understand complex biological
organisms at the system level. Numerous integration approaches have been developed by …

Computational estimation of quality and clinical relevance of cancer cell lines

L Trastulla, J Noorbakhsh, F Vazquez… - Molecular Systems …, 2022 - embopress.org
Immortal cancer cell lines (CCLs) are the most widely used system for investigating cancer
biology and for the preclinical development of oncology therapies. Pharmacogenomic and …

Subtype-DCC: decoupled contrastive clustering method for cancer subtype identification based on multi-omics data

J Zhao, B Zhao, X Song, C Lyu, W Chen… - Briefings in …, 2023 - academic.oup.com
Due to the high heterogeneity and complexity of cancers, patients with different cancer
subtypes often have distinct groups of genomic and clinical characteristics. Therefore, the …