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chromnet.cs.washington.edu

ChromNet

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https://chromnet.cs.washington.edu

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haldanessieve.org haldanessieve.org

Learning the human chromatin network from all ENCODE ChIP-seq data | Haldane's Sieve

https://haldanessieve.org/2015/08/06/learning-the-human-chromatin-network-from-all-encode-chip-seq-data

Discussing preprints in population and evolutionary genetics. Skip to primary content. Learning the human chromatin network from all ENCODE ChIP-seq data. August 6, 2015. Learning the human chromatin network from all ENCODE ChIP-seq data. Scott M. Lundberg, William B. Tu, Brian Raught, Linda Z. Penn, Michael M. Hoffman, Su-In Lee. Doi: http:/ dx.doi.org/10.1101/023911. This entry was posted in Uncategorized. Leave a Reply Cancel reply. Enter your comment here. Address never made public).

pennlab.ca pennlab.ca

ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data. - Penn Lab

http://pennlab.ca/publications/chromnet-learning-the-human-chromatin-network-from-all-encode-chip-seq-data

ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data. July 2, 2015. Genome Biol. 2016 Apr 30;17:82. Lundberg SM, Tu WB, Raught B, Penn LZ, Hoffman MM, Lee SI. Link: http:/ genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0925-0.

suinlee.cs.washington.edu suinlee.cs.washington.edu

Software - Lee Lab

http://suinlee.cs.washington.edu/software

536 Paul Allen Center. Suinlee AT uw.edu. Research Group of Prof. Su-In Lee. CSE 527 Computational Biology. GENOME 560 Statistics for Genome Scientists. Learning Sparse Gaussian Graphical Models with Overlapping Blocks. Neural Information Processing Systems (NIPS) 2016. Afiye Celik, Benjamin A. Logsdon, Stephanie Battle, Charles W. Drescher, Mara H. Rendi, David R. Hawkins, and. 2016 Jun 10;8(1):66. Maxim Grechkin, Benjamin A. Logsdon, Andrew J. Gentles, and. Identifying Network Perturbation in Cancer.

suinlee.cs.washington.edu suinlee.cs.washington.edu

Lee Lab

http://suinlee.cs.washington.edu/home

536 Paul Allen Center. Suinlee AT uw.edu. Research Group of Prof. Su-In Lee. CSE 527 Computational Biology. GENOME 560 Statistics for Genome Scientists. S lab seeks to develop and apply powerful machine learning techniques to analyze high-throughput molecular data to 1) understand basic biological mechanisms,. 2) identify molecular mechanisms of disease progression and therapeutic targets, and 3) develop. Personalized treatments based on molecular profiles . Our research is mainly funded by the.

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