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Elife. 2017 Jan 26;6. pii: e18541. doi: 10.7554/eLife.18541.

Selecting the most appropriate time points to profile in high-throughput studies.

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Machine Learning and Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States.
Division of Neonatology, Department of Pediatrics, University of Alabama at Birmingham, Birmingham, United States.
Division of Respiratory Medicine, Department of Pediatrics, University of California, San Diego, United States.
CARady Children's Hospital San Diego, San Diego, United States.
Section of Pulmonary, Critical Care and Sleep Medicine, School of Medicine, Yale University, New Haven, United States.


Biological systems are increasingly being studied by high throughput profiling of molecular data over time. Determining the set of time points to sample in studies that profile several different types of molecular data is still challenging. Here we present the Time Point Selection (TPS) method that solves this combinatorial problem in a principled and practical way. TPS utilizes expression data from a small set of genes sampled at a high rate. As we show by applying TPS to study mouse lung development, the points selected by TPS can be used to reconstruct an accurate representation for the expression values of the non selected points. Further, even though the selection is only based on gene expression, these points are also appropriate for representing a much larger set of protein, miRNA and DNA methylation changes over time. TPS can thus serve as a key design strategy for high throughput time series experiments. Supporting Website:


computational biology; experimental design; lung developement; mouse; systems biology; time point selection

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