Functional Census of Mutation Sequence Spaces: The Example of p53 Cancer Rescue Mutants. - Samuel A. Danziger; Sanjay Joshua Swamidass; Jue Zeng; Lawrence R. Dearth; Qiang Lu; Jonathan H. Chen; Jianlin Cheng; Vinh P. Hoang; Hiroto Saigo; Ray Luo 001; Pierre Baldi; Rainer K. Brachmann; et al. (IEEE ACM Trans. Comput. Biol. Bioinform., 2006)

Choosing where to look next in a mutation sequence space: Active Learning of informative p53 cancer rescue mutants. - Samuel A. Danziger; Jue Zeng; Ying Wang; Rainer K. Brachmann; Richard H. Lathrop (ISMB/ECCB (Supplement of Bioinformatics), 2007)

Predicting Positive p53 Cancer Rescue Regions Using Most Informative Positive (MIP) Active Learning. - Samuel A. Danziger; Roberta Baronio; Lydia Ho; Linda Hall; Kirsty Salmon; G. Wesley Hatfield; Peter Kaiser; Richard H. Lathrop (PLoS Comput. Biol., 2009)

PhosphoChain: a novel algorithm to predict kinase and phosphatase networks from high-throughput expression data. - Wei-Ming Chen; Samuel A. Danziger; Jung-Hsien Chiang; John D. Aitchison (Bioinform., 2013)

Bicluster Sampled Coherence Metric (BSCM) provides an accurate environmental context for phenotype predictions. - Samuel A. Danziger; David J. Reiss; Alexander V. Ratushny; Jennifer J. Smith; Christopher L. Plaisier; John D. Aitchison; Nitin S. Baliga (BMC Syst. Biol., 2015)