Active Learning techniques for choosing the most informative data enable biologists and computer scientists to optimize experimental data choices.

Jul 1, 2007 · Active Learning techniques for choosing the most informative data enable biologists and computer scientists to optimize experimental data choices.

Oct 17, 2024 · ArticlePDF Available. Choosing where to look next in a mutation sequence space: Active Learning of informative p53 cancer rescue mutants.

These models may help determine which expensive biological data are most useful to acquire next. Active Learning techniques for choosing the most informative ...

Five novel and five existing Active Learning techniques, together with three control methods, were tested on 57 previously unknown p53 cancer rescue mutants ...

Choosing where to look next in a mutation sequence space: Active Learning of informative p53 cancer rescue mutants. Danziger, Samuel A;; Zeng, Jue;; Wang ...

17 In a Nutshell Cancer Rescue Mutants Use Active Learning to select the p53 mutants that will be the most informative. Test the predictions in-vitro. Build ...

Choosing where to look next in a mutation sequence space: Active Learning of informative p53 cancer rescue mutants. Danziger, Samuel A;; Zeng, Jue;; Wang ...

Sep 4, 2009 · Choosing where to look next in a mutation sequence space: Active Learning of informative p53 cancer rescue mutants. Bioinformatics. 2007;23 ...

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