Our study identifies reference-free ‘absolute’ feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials....
Thanks to the advances of AI and ML models, one can achieve a useful fusion of multimodal data with high-dimensionality10, various statistical properties, and different missing value patterns11. Multimodal ML is the domain that can integrate different data modalities. In recent years, multimodal da...
the same theme. However, aggregating this data is vital for more complex analyses to be carried out to improve services, applications, and quality of life. We believe that data heterogeneity can be resolved by establishing more standardized forms of collection and storage that facilitate integration...
There are many ways to go about dealing with multi-source problems. However, these are done primarily to solve the problems of: Identifying the presence of similar/same records and merging them into a single record Re-structuring the schema to ensure there is good schema integration 13. What ...
Smoother-guided dimensionality reduction and integration of spatial and single-cell data Learning an informative low-dimensional representation is crucial for understanding the biological dynamics underlying noisy omics data. Smoother’s ability to impose structural dependencies via a versatile loss function al...
One of the mechanisms to automate integration with data sources, scalable batch and real-time data processing, data versioning, and feature management is to use a feature store. A feature store is a central hub for producing, sharing, and monitoring features. Feature stores are essential in ...
Continuous data requires the application of a cutoff before its integration in the KG. Below, we detail how these cutoffs were chosen depending on the nature of the data. Transcriptomics and proteomics data We adapted the strategy followed by Harmonizome, which is based on traditional statistical ...
Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis Article Open access 11 May 2020 Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space Article Open access 17 October 2022 Paired single-cell...
However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, ...
As a high-level fusion, the results are of high flexibility, strong anti-interference, and good fault tolerance. However, due to the high requirements for data preprocessing and feature extraction, the cost of decision-level integration is relatively high, which is also an existing shortcoming, ...