Major biotechnological advances have facilitated a tremendous boost in collecting (gen-/transcript-/prote-/methyl-/metabol-) cancer-omics data in large sample sizes worldwide. Coordinated efforts have yielded many studies associating diseases with genetic markers (genome-wide association studies) or with molecular phenotypes. Whereas omics-disease associations have led to biologically meaningful and coherent mechanisms, the identified (non-germline) cancer biomarkers may simply be correlated or consequences of the explored diseases. Most common diseases have heritability, and the remaining causes comprise modifiable environmental, lifestyle, and molecular factors. Causal inference methods can be adapted to combine molecular phenotypes (i.e., omics data) and complex diseases to reveal robust causal cancer biomarkers. However, related methods are still underdeveloped.
This Research Topic aims to promote the methodology development in causal inference for the cancer-omics data and then benefit the studies -omics studies related to the cancer disease. Driven by computational and methodological advances, developments in observational causal inference related to -omics, or high-dimensional molecular biomarkers, are proceeding apace in computer science and bioinformatics. Such work might suggest –omics can be leveraged to address a variety of goals in causal disease inference, such as identifying biochemical targets for intervention, decomposing effects of modifiable disease pathways, or clarifying how embodied biological processes mediate the effects of social factors. And yet, very basic questions related to causal effect identification in molecular cancer epidemiology and its relevance to population health remain unanswered.
The main scope of this research topic is the development of causal inference methods for cancer research based on the -omics data. Both methodology and application research papers are welcome. The following are some themes available for this topic.
• Construct a general model to understand the causal structure in cancer-omics data analysis.
• Address standard causal inference assumptions in molecular cancer epidemiology. For example, can we ensure consistency is reasonably fulfilled?
• Leverage biological knowledge to investigate cancer epigenetic mediation.
• The implications of genetic instruments for agnostic causal discovery.
• Cancer–omics network discovery or other data reduction strategies help understand causal cancer development mechanisms.
• Uncover cancer biomarkers through causal analysis of single cell sequencing data analysis.
Major biotechnological advances have facilitated a tremendous boost in collecting (gen-/transcript-/prote-/methyl-/metabol-) cancer-omics data in large sample sizes worldwide. Coordinated efforts have yielded many studies associating diseases with genetic markers (genome-wide association studies) or with molecular phenotypes. Whereas omics-disease associations have led to biologically meaningful and coherent mechanisms, the identified (non-germline) cancer biomarkers may simply be correlated or consequences of the explored diseases. Most common diseases have heritability, and the remaining causes comprise modifiable environmental, lifestyle, and molecular factors. Causal inference methods can be adapted to combine molecular phenotypes (i.e., omics data) and complex diseases to reveal robust causal cancer biomarkers. However, related methods are still underdeveloped.
This Research Topic aims to promote the methodology development in causal inference for the cancer-omics data and then benefit the studies -omics studies related to the cancer disease. Driven by computational and methodological advances, developments in observational causal inference related to -omics, or high-dimensional molecular biomarkers, are proceeding apace in computer science and bioinformatics. Such work might suggest –omics can be leveraged to address a variety of goals in causal disease inference, such as identifying biochemical targets for intervention, decomposing effects of modifiable disease pathways, or clarifying how embodied biological processes mediate the effects of social factors. And yet, very basic questions related to causal effect identification in molecular cancer epidemiology and its relevance to population health remain unanswered.
The main scope of this research topic is the development of causal inference methods for cancer research based on the -omics data. Both methodology and application research papers are welcome. The following are some themes available for this topic.
• Construct a general model to understand the causal structure in cancer-omics data analysis.
• Address standard causal inference assumptions in molecular cancer epidemiology. For example, can we ensure consistency is reasonably fulfilled?
• Leverage biological knowledge to investigate cancer epigenetic mediation.
• The implications of genetic instruments for agnostic causal discovery.
• Cancer–omics network discovery or other data reduction strategies help understand causal cancer development mechanisms.
• Uncover cancer biomarkers through causal analysis of single cell sequencing data analysis.