In omics data integration studies, it is common, for a variety of reasons, for some individuals to not be present in all data tables. Missing row values are challenging to deal with because most statistical met
Coupled or tensor MF methods can learn latent correlated structure within and between omics data sets (15., 16., 17., 18.) and have been applied to the analysis of molecular data from different technology platforms (19) and integration of diverse multi-omics data (15, 17). An attractive ...
To sum up, while Multiple kernel learning remains an under-utilized tool for genomic data mining [15], in this work, we propose MKL methods to integrate multi-omics data based both on unsupervised convex linear optimization and deep learning. We aim to show the advantages of this setting by ...
Overview of the proposed pathway-based multi-omics integration method for survival prediction Full size image Fig. 2 Survival prediction performance comparison between pathway profiles of four pathway-based methods on the gene expression data and those of the iDRW method on the gene expression and co...
single-omics approaches may not be best suited for understanding the etiology of CLL. Instead, a multi-omics approach may yield better insights into its pathomechanism and could be instrumental in the development of improved prognostic or therapeutic methods. However, the fragmented nature of CLL in...
The Singapore Integrative Omics Study provides valuable insights on establishing population reference measurement in 364 Chinese, Malay, and Indian individuals. These measurements include > 2.5 millions genetic variants, 21,649 transcripts expressi
High-dimensional multiple source data integration has become an important approach for exploratory data analysis. For integrative clustering analysis joint
Machine learning (ML) has emerged as a powerful tool to address this gap, enabling the integration of high-dimensional omics data for predictive modeling. Recent studies have leveraged ML algorithms to identify prognostic gene signatures, predict drug sensitivity, and stratify patients for immunotherapy...
Kernel based integration methods were first proposed in (Lanckriet et al., 2004), wherein a 1-norm soft margin SVM is trained for a classification problem separating membrane proteins from ribosomal proteins. They combined heterogeneous biological datasets such as PPI, amino acid sequences and gene...
et al. penalizedclr: an R package for penalized conditional logistic regression for integration of multiple omics layers. BMC Bioinformatics 25, 226 (2024). https://doi.org/10.1186/s12859-024-05850-2 Download citation Received08 January 2024 Accepted20 June 2024 Published27 June 2024 DOIhttps:/...