![]() ![]() We analyse large-scale simulated data, antibiotic resistance, and gene-silencing data sets to demonstrate the accuracy and performance of our approach.Įpistatic gene interactions have practical implications for personalised medicine, and synthetic lethal interactions in particular can be used in cancer treatment. Moreover, our method scales to three-way interactions among thousands of genes, while avoiding a number of the limitations of previous approaches. We improve upon one such method, developing an approach that is significantly faster than the current state of the art. Even the fastest of these cannot include three-way interactions, however. Recently, methods have been developed to solve regression problems that include these interacting effects. ![]() Due to the enormous number of potential interactions, each gene or variation in the data is often modelled on its own, without considering interactions between them. These large data sets often stretch the limits of classic computational methods, requiring too much memory or simply taking a prohibitively long time to run. In recent years, large-scale genetic data sets have become available for analysis. For example, we have identified a combination of known tumour suppressor genes that is predicted (using Pint) to cause a significant increase in cell proliferation. Pint outperforms known methods in simulated data, and identifies a number of biologically plausible gene effects in both the antibiotic and siRNA models. We demonstrate our proposed method, Pint, on both simulated and real data sets, including antibiotic resistance testing and siRNA perturbation screens. ![]() Expanding upon recent state-of-the-art methods, we make a number of improvements to the performance on large-scale data, making consideration of three-way interactions possible. Recently developed tools scale to human exome-wide screens for pairwise interactions, but none to date have included the possibility of three-way interactions. In bacteria, epistasis has practical consequences in determining antimicrobial resistance as the genetic background of a strain plays an important role in determining resistance. For example, siRNA perturbation screens can be used to identify combinatorial gene-silencing effects. Epistatic effects play an important role in such association studies. Large-scale genotype-phenotype screens provide a wealth of data for identifying molecular alterations associated with a phenotype. ![]()
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