A systematic biology tool was developed to identify dysregulated miRNAs via a miRNA-miRNA interaction network. 'IDMIR' first constructed a weighted miRNA interaction network through integrating miRNA-target interaction information, molecular function data from Gene Ontology (GO) database and gene transcriptomic data in specific-disease context, and then, it used a network propagation algorithm on the network to identify significantly dysregulated miRNAs.

Most existing approaches for network reconstruction can only infer an overall network 
	and, also, fail to capture a complete set of network properties. To address these issues, 
	a new model has been developed, which converts static data into their 'dynamic' form. 
	'idopNetwork' is an 'R' interface to this model, it can inferring informative, dynamic, 
	omnidirectional and personalized networks. For more information on functional 
	clustering part, see Kim et al. (2008) <doi:10.1534/genetics.108.093690>, 
	Wang et al. (2011) <doi:10.1093/bib/bbr032>. For more information on our model, 
	see Chen et al. (2019) <doi:10.1038/s41540-019-0116-1>, and Cao et al. (2022) 
	<doi:10.1080/19490976.2022.2106103>.

A method to integrate molecular profiles of cancer patients (gene copy number and mRNA abundance) to 
	identify candidate gain of function alterations. These candidate alterations can be subsequently further tested  
	to discover cancer driver alterations. Briefly, this method tests of genomic correlates of mRNA dysregulation and prioritise 
	those where DNA gains/amplifications are associated with elevated mRNA expression of the same gene. For details see,
	Haider S et al. (2016) "Genomic alterations underlie a pan-cancer metabolic shift associated with tumour hypoxia", Genome Biology, <https://pubmed.ncbi.nlm.nih.gov/27358048/>.

Implements a nonparametric maximum likelihood method for assessing 
  potentially time-varying vaccine efficacy (VE) against SARS-CoV-2 infection 
  under staggered enrollment and time-varying community transmission, allowing 
  crossover of placebo volunteers to the vaccine arm. 
  Lin, D. Y., Gu, Y., Zeng, D., Janes, H. E., and Gilbert, P. B. (2021) 
  <doi:10.1093/cid/ciab630>.