Heterogeneous Biomedical Database Integration Using a Hybrid Strategy: A p53 Cancer Research Database

Autor: Vadim Y. Bichutskiy, Richard Colman, Rainer K. Brachmann, Richard H. Lathrop

Publikováno v: Cancer Informatics, Vol 2, Pp 277-287 (2006)

Complex problems in life science research give rise to multidisciplinary collaboration, and hence, to the need for heterogeneous database integration. The tumor suppressor p53 is mutated in close to 50% of human cancers, and a small drug-like molecul

Externí odkaz: https://doaj.org/article/864d111cb41e4602ba315dc5963c3c38

Ensemble-based computational approach discriminates functional activity of p53 cancer and rescue mutants.

Autor: Özlem Demir, Roberta Baronio, Faezeh Salehi, Christopher D Wassman, Linda Hall, G Wesley Hatfield, Richard Chamberlin, Peter Kaiser, Richard H Lathrop, Rommie E Amaro

Publikováno v: PLoS Computational Biology, Vol 7, Iss 10, p e1002238 (2011)

The tumor suppressor protein p53 can lose its function upon single-point missense mutations in the core DNA-binding domain ("cancer mutants"). Activity can be restored by second-site suppressor mutations ("rescue mutants"). This paper relates the fun

Externí odkaz: https://doaj.org/article/13916c5e1ca2495eb6707700eecd1972

Predicting positive p53 cancer rescue regions using Most Informative Positive (MIP) active learning.

Autor: Samuel A Danziger, Roberta Baronio, Lydia Ho, Linda Hall, Kirsty Salmon, G Wesley Hatfield, Peter Kaiser, Richard H Lathrop

Publikováno v: PLoS Computational Biology, Vol 5, Iss 9, p e1000498 (2009)

Many protein engineering problems involve finding mutations that produce proteins with a particular function. Computational active learning is an attractive approach to discover desired biological activities. Traditional active learning techniques ha

Externí odkaz: https://doaj.org/article/c2e4d539b0474c5faa4d94688f6e0e1d

All-codon scanning identifies p53 cancer rescue mutations

Autor: Linda V. Hall, G. Wesley Hatfield, Kirsty Anne Lily Salmon, Richard H. Lathrop, Roberta Baronio, Samuel A. Danziger, Peter K. Kaiser

Publikováno v: Baronio, Roberta; Danziger, Samuel A.; Hall, Linda V.; Salmon, Kirsty; Hatfield, G. Wesley; Lathrop, Richard H.; et al.(2010). All-codon scanning identifies p53 cancer rescue mutations. Nucleic Acids Research, 38(20), 7079-7088. UC Irvine: Retrieved from: http://www.escholarship.org/uc/item/81w7m8dj
Nucleic Acids Research

In vitro scanning mutagenesis strategies are valuable tools to identify critical residues in proteins and to generate proteins with modified properties. We describe the fast and simple All-Codon Scanning (ACS) strategy that creates a defined gene lib

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cc4c0f2d784aad1eb7d1e621bd136aef
https://doi.org/10.1093/nar/gkq571

Choosing where to look next in a mutation sequence space: Active Learning of informative p53 cancer rescue mutants

Autor: Jue Zeng, Samuel A. Danziger, Ying Wang, Richard H. Lathrop, Rainer K. Brachmann

Publikováno v: ISMB/ECCB (Supplement of Bioinformatics)
Danziger, Samuel A; Zeng, Jue; Wang, Ying; Brachmann, Rainer K; & Lathrop, Richard H. (2007). Choosing where to look next in a mutation sequence space: Active Learning of informative p53 cancer rescue mutants.. Bioinformatics (Oxford, England), 23(13), 104-114. UC Irvine: Retrieved from: http://www.escholarship.org/uc/item/4zs4g23m

MOTIVATION: Many biomedical projects would benefit from reducing the time and expense of in vitro experimentation by using computer models for in silico predictions. These models may help determine which expensive biological data are most useful to a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a312053a8c898132768cd11ea7634f6
https://doi.org/10.1093/bioinformatics/btm166