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Visualitza Departament de Medicina i Ciències de la Vida per matèria "Algorismes"

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Comparison of algorithms for the detection of cancer drivers at subgene resolution

Porta-Pardo, Eduard; Kamburov, Atanas; Tamborero Noguera, David; Pons, Tirso; Grases, Daniela; Valencia, Alfonso; López Bigas, Núria; Getz, Gad; Godzik, Adam (Nature Publishing Group, 2017)

Understanding genetic events that lead to cancer initiation and progression remains one of the biggest challenges in cancer biology. Traditionally, most algorithms for cancer-driver identification look for genes that have ...
Fast computation and applications of genome mappability

Derrien, Thomas; Estellé, Jordi; Marco Sola, Santiago; Knowles, David G.; Raineri, Emanuele; Guigó Serra, Roderic; Ribeca, Paolo (Public Library of Science (PLoS), 2012)

We present a fast mapping-based algorithm to compute the mappability of each region of a reference genome up to a specified number of mismatches. Knowing the mappability of a genome is crucial for the interpretation of ...
Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes

Leiserson, Mark D.M.; Vandin, Fabio; Wu, Hsin-Ta; Dobson, Jason R.; Eldridge, Jonathan V.; Thomas, Jacob L.; Papoutsaki, Alexandra; Kim, Younhun; Niu, Beifang; McLellan, Michael; Lawrence, Michael S.; Gonzalez-Perez, Abel; Tamborero Noguera, David; Cheng, Yuwei; Ryslik, Gregory A.; López Bigas, Núria; Getz, Gad; Ding, Li; Raphael, Benjamin J. (Nature Research, 2015)

Cancers exhibit extensive mutational heterogeneity, and the resulting long-tail phenomenon complicates the discovery of genes and pathways that are significantly mutated in cancer. We perform a pan-cancer analysis of mutated ...
Reverse engineering a gene network using an asynchronous parallel evolution strategy

Jostins, Luke; Jaeger, Johannes, 1973- (BioMed Central, 2010)

Background: The use of reverse engineering methods to infer gene regulatory networks by fitting mathematical models to gene expression data is becoming increasingly popular and successful. However, increasing model complexity ...