Manuscript Topics

The dysfunctions of many complex diseases, such as cancers, diabetes and Alzheimer’s disease, involve various molecules on multiple level. Although the omics technologies become more and more powerful and are bale to capture most of the biological signals, the analysis of such multi omics big data, especially the integration of heterogeneous data and interpretation of the complex model constructed from the complex data, is still challenging.

The network methods, such as Bayesian method, are widely used to modeling the relationships among different molecules. The nodes may be genes, mutations, methylations, proteins, metabolites, even phenotypes. The edge may be regulations, eQTLs, co-expression, or functional associations. There have been many methods for network construction, such as Bayesian network, co-expression network, Boolean network. The constructed network can be divided into modules for detailed investigation, such as key driver identification and functional analysis. But current methods are not enough for the big data analysis of complex diseases.

If on patients have mutation, methylation, mRNA, lncRNA, circRNA, microRNA, protein and metabolite data, there will be millions of variables. How to construct the genome wide regulatory network efficiently?

The sample sizes of single cell sequencing are much larger than traditional studies. Usually, several thousand or tens of thousand cells were sequenced. The data is extremely large. Meanwhile, it is sparse and the value of many genes in many cells is not measured.

Potential topics include, but not limited to:
Multi-omics data analysis
Heterogeneous data integration
Network construction
Module identification
Key driver analysis
Applications of graph theory
Applications of optimization theory
Single cell sequencing
eQTL regulatory network
Network embedding
Network feature engineering
Predictive modeling

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