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Lior Pachter

Researcher at California Institute of Technology

Publications -  308
Citations -  95965

Lior Pachter is an academic researcher from California Institute of Technology. The author has contributed to research in topics: Gene & Genome. The author has an hindex of 69, co-authored 281 publications receiving 83783 citations. Previous affiliations of Lior Pachter include University of Miami & University of Oxford.

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Convex Rank Tests and Semigraphoids

Jason Morton, +4 more
- 20 Feb 2007 - 
TL;DR: The methods refine existing rank tests of nonparametric statistics, such as the sign test and the runs test, and are useful for exploratory analysis of ordinal data and of particular interest are graphical tests, which correspond to both graphical models and to graph associahedra.
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Shape-based peak identification for ChIP-Seq

Valerie Hower, +2 more
- 12 Jan 2011 - 
TL;DR: This work demonstrates a novel method for identifying statistically significant peaks from read coverage using ChIP-Seq data that improves on the accuracy of previous methods in resolving peaks and introduces a robust statistical test based on ideas from topological data analysis.
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Single-cell analysis at the threshold.

Xi Chen, +7 more
- 08 Nov 2016 - 
TL;DR: Single-cell analysis at the threshold is described as a novel and scalable approach to single-cell cell analysis that aims to provide real-time information about the architecture of the cell and its response to treatment.
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Identification of transposable elements using multiple alignments of related genomes

Anat Caspi, +1 more
- 01 Feb 2006 - 
TL;DR: A large-scale comparative method for identifying repetitive mobile DNA regions that are highly enriched for transposable elements and can be used to determine shifts in the eu-heterochromatin junction in the pericentric chromosome regions.
Journal ArticleDOI

Accurate design of translational output by a neural network model of ribosome distribution

Robert Tunney, +7 more
- 02 Jul 2018 - 
TL;DR: It is demonstrated that the model captures information determining translation dynamics in vivo; that this information can be harnessed to design coding sequences; and that control of translation elongation alone is sufficient to produce large quantitative differences in protein output.