Publisher's Note: Abstraction for data integration: Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction

Abstract

With advances in genomics, transcriptomics, metabolomics and proteomics, and more expansive electronic clinical record monitoring, as well as advances in computation, we have entered the Big Data era in biomedical research. Data gathering is growing rapidly while only a small fraction of this data is converted to useful knowledge or reused in future studies. To improve this, an important concept that is often overlooked is data abstraction. To fuse and reuse biomedical datasets from diverse resources, data abstraction is frequently required. Here we summarize some of the major Big Data biomedical research resources for genomics, proteomics and phenotype data, collected from mammalian cells, tissues and organisms. We then suggest simple data abstraction methods for fusing this diverse but related data. Finally, we demonstrate examples of the potential utility of such data integration efforts, while warning about the inherit biases that exist within such data.

Machine Learning and Integrative Analysis of Biomedical Big Data

“…Given that the biological samples are the same, it is statistically plausible to infer missing values in one omics from observed values and in other omics by exploiting any existing correlations found through complete cases. Complete case refers to the samples with measurements available on all variables under consideration [ 106 , 107 , 109 , 110 ]. Generally, most modern missing data methods focus on item non-response case, i.e., when data is missing on some variables for some biological samples [ 106 , 111 , 112 ].…”

Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine

“…One of the greatest challenges in precision medicine is to integrate all available patient-derived data for accurate diagnosis and treatment, which would require novel data-driven approaches rather than more conventional hypothesis-driven approaches ( Rouillard et al., 2015 ). Genomic information for patients, albeit fundamental and often necessary, may not be sufficient due to the fact that the human genome is dynamically adjusting its functions by epigenetic regulations ( Jafari et al., 2017 ; Nussinov et al., 2019 ).…”