Justin B Kinney
Affiliation: Cold Spring Harbor Laboratory
- Parametric inference in the large data limit using maximally informative modelsJustin B Kinney
Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, U S A
Neural Comput 26:637-53. 2014..The presence of diffeomorphic modes reflects a fundamental and nontrivial substructure within parameter space, one that is obscured by standard likelihood-based inference. ..
- Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequenceJustin B Kinney
Department of Physics, Princeton University, Princeton, NJ 08544, USA
Proc Natl Acad Sci U S A 107:9158-63. 2010..The principles of our method can also be applied to a wide range of other problems in molecular biology...
- Precise physical models of protein-DNA interaction from high-throughput dataJustin B Kinney
Physics Department and Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
Proc Natl Acad Sci U S A 104:501-6. 2007..Results from in vivo and in vitro experiments also provide highly consistent characterizations of Abf1p, a result that contrasts with a previous analysis of the same data...
- Equitability, mutual information, and the maximal information coefficientJustin B Kinney
Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724
Proc Natl Acad Sci U S A 111:3354-9. 2014..We conclude that estimating mutual information provides a natural (and often practical) way to equitably quantify statistical associations in large datasets. ..