The Adaptive Daisy Model (ADaM) package implements a semi-supervised algorithm for computing a fuzzy-intersection of non-fuzzy sets by adaptively determining the minimal number of sets to which an element should belong in order to be a member of the fuzzy-intersection (the membership threshold).
This threshold maximises the deviance from expectation of the cardinality of the resulting fuzzy-intersection, as well as the coverage of predefined elements.
This method can be used to identify the minimal number of cell lines from a given tissue in which the inactivation of a gene (for example via CRISPR-Cas9 targeting) should exert a reduction of viabilty (or fitness effect) in order for that gene to be considered a core-fitness essential gene for the tissue under consideration.
This method is used to discriminate between core-fitness and context-specific essential genes in a study describing a large scale genome-wide CRISPR-Cas9 pooled drop-out screening  (a detailed description of the algorithm is included in the Supplemental Information of ).
ADaM was inspired by the Daisy Model method introduced in 
 Behan FM & Iorio F & Picco G et al., In press.
 Hart T et al., High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities. Cell. 2015;163:1515–26.
The Cancer Dependency Map integrates the work of multiple experimental and computational research project at the Sanger Institute with the shared aim of identifying dependencies in cancer cells which could be exploited to develop new therapies. This knowledge is foundational for our understanding of cancer biology and the development of precision cancer medicine.
The Cancer, Ageing and Somatic Mutation Programme seeks to provide leadership in data aggregation and informatics innovation, developing high-throughput cellular models of cancer for genome-wide functional screens and drug testing, and exploring basic scientific questions about the role somatic mutation plays in clonal evolution, ageing and development.