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ruppinlab/madhitter: MadHitter_for_paper

Authors: Pattara Sukprasert; Alejandro Schaffer; rahulsimham;

ruppinlab/madhitter: MadHitter_for_paper

Abstract

MadHitter is a software package designed to find optimal combinations of gene targets for personalized cancer treatments. The program works by modelling the instances into integer linear programs and relies on a mixed integer programming software package to find optimal treatments for patients. Currently, we use SCIP or Gurobito solve the integer linear programs. The family of problems that we consider are variants of the hitting set problem from computer science literature. In our context, an instance is comprised of a cohort of patients, which corresponds to a collection of hitting set instances on the same universe. The universe is a set of genes. The elements of the hitting set instance for any patient are cells. Each cell may or may not express each gene; if the gene is expressed, then the level of expression is a numerical value. Thus, we say that a "gene g hits a cell c" if g has a non-zero expression value in c.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Cancer Research