Search

Motivation Machine learning (ML) methods are motivated by the need to automate information extraction from large datasets in order to support human users in data-driven tasks. This is an attractive approach for integrative joint analysis of vast amounts of omics data produced in next generation sequencing and other -omics assays. A systematic assessment of the current literature can help to identify key trends and potential gaps in methodology and applications. We surveyed the literature on ML multi-omic data integration and quantitatively explored the goals, techniques and data involved in this field. We were particularly interested in examining how researchers use ML to deal with the volume and complexity of these datasets. Results Our main finding is that the methods used are those that address the challenges of datasets with few samples and many features. Dimensionality reduction methods are used to reduce the feature count alongside models that can also appropriately handle relatively few samples. Popular techniques include autoencoders, random forests and support vector machines. We also found that the field is heavily influenced by the use of The Cancer Genome Atlas dataset, which is accessible and contains many diverse experiments. Availability and implementation All data and processing scripts are available at this GitLab repository: https://gitlab.com/polavieja%5flab/ml%5fmulti-omics%5freview/ or in Zenodo: https://doi.org/10.5281/zenodo.7361807. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Coordination of cell proliferation and migration is fundamental for life, and its dysregulation has catastrophic consequences, such as cancer. How cell cycle progression affects migration, and vice versa, remains largely unknown. We address these questions by combining in silico modelling and in vivo experimentation in the zebrafish trunk neural crest (TNC). TNC migrate collectively, forming chains with a leader cell directing the movement of trailing followers. We show that the acquisition of migratory identity is autonomously controlled by Notch signalling in TNC. High Notch activity defines leaders, while low Notch determines followers. Moreover, cell cycle progression is required for TNC migration and is regulated by Notch. Cells with low Notch activity stay longer in G1 and become followers, while leaders with high Notch activity quickly undergo G1/S transition and remain in S-phase longer. In conclusion, TNC migratory identities are defined through the interaction of Notch signalling and cell cycle progression. [ABSTRACT FROM AUTHOR]