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Michael Brudno

Researcher at University of Toronto

Publications -  188
Citations -  26208

Michael Brudno is an academic researcher from University of Toronto. The author has contributed to research in topics: Gene & Medicine. The author has an hindex of 58, co-authored 170 publications receiving 23492 citations. Previous affiliations of Michael Brudno include Stanford University & The Centre for Applied Genomics.

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The ENCODE (ENCyclopedia of DNA elements) Project

Elise A. Feingold, +196 more
- 22 Oct 2004 - 
TL;DR: The ENCyclopedia Of DNA Elements (ENCODE) Project is organized as an international consortium of computational and laboratory-based scientists working to develop and apply high-throughput approaches for detecting all sequence elements that confer biological function.
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Genome sequence of the Brown Norway rat yields insights into mammalian evolution

Richard A. Gibbs, +242 more
- 01 Apr 2004 - 
TL;DR: This first comprehensive analysis of the genome sequence of the Brown Norway (BN) rat strain is reported, which is the third complete mammalian genome to be deciphered, and three-way comparisons with the human and mouse genomes resolve details of mammalian evolution.

Genome sequence of the Brown Norway rat yields insights into mammalian evolutionRat Genome Sequencing Project ConsortiumNature200442849352115057822

Richard A. Gibbs, +226 more
Abstract: The laboratory rat (Rattus norvegicus) is an indispensable tool in experimental medicine and drug development, having made inestimable contributions to human health. We report here the genome sequence of the Brown Norway (BN) rat strain. The sequence represents a high-quality ‘draft’ covering over 90% of the genome. The BN rat sequence is the third complete mammalian genome to be deciphered, and three-way comparisons with the human and mouse genomes resolve details of mammalian evolution. This first comprehensive analysis includes genes and proteins and their relation to human disease, repeated sequences, comparative genome-wide studies of mammalian orthologous chromosomal regions and rearrangement breakpoints, reconstruction of ancestral karyotypes and the events leading to existing species, rates of variation, and lineage-specific and lineage-independent evolutionary events such as expansion of gene families, orthology relations and protein evolution.
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Similarity network fusion for aggregating data types on a genomic scale

TL;DR: Similarity network fusion substantially outperforms single data type analysis and established integrative approaches when identifying cancer subtypes and is effective for predicting survival.