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Related Concept Videos

Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...

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Related Experiment Video

Updated: May 22, 2026

Pattern-based Search of Epigenomic Data Using GeNemo
06:38

Pattern-based Search of Epigenomic Data Using GeNemo

Published on: October 8, 2017

The partitioned LASSO-patternsearch algorithm with application to gene expression data.

Weiliang Shi1, Grace Wahba, Rafael A Irizarry

  • 1Sanofi-Aventis, Cambridge, Massachusetts, USA. weiliang.shi@sanofi.com

BMC Bioinformatics
|May 17, 2012
PubMed
Summary
This summary is machine-generated.

A new algorithm improves gene pathway discovery by reducing data errors and handling high-dimensional genomic data. This method enhances accuracy and prediction in identifying genetic risk factors.

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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Related Experiment Videos

Last Updated: May 22, 2026

Pattern-based Search of Epigenomic Data Using GeNemo
06:38

Pattern-based Search of Epigenomic Data Using GeNemo

Published on: October 8, 2017

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Area of Science:

  • Systems Biology
  • Genomics
  • Bioinformatics

Background:

  • Reverse engineering gene pathways is challenging due to high dimensionality and data errors.
  • Gene expression barcoding mitigates spurious associations from batch and probe effects.
  • Binary expression data is suitable for regularization methods in high-dimensional settings.

Purpose of the Study:

  • To introduce a novel algorithm for identifying patterns of multiple dichotomous risk factors in genomic studies.
  • To address limitations in gene pathway analysis caused by data dimensionality and systematic errors.

Main Methods:

  • Proposed the Partitioned LASSO-Patternsearch algorithm.
  • Employed a partitioning scheme to solve parallel LASSO-Patternsearch subproblems.
  • Utilized an aggregation stage to identify significant patterns from selected variables.

Main Results:

  • Applied the algorithm to dichotomized gene expression data.
  • Selected genes and second-order interactions known to be related to outcomes.
  • Demonstrated accurate selection of variables and patterns.

Conclusions:

  • The proposed method offers improved accuracy in variable and pattern selection.
  • Models generated by the algorithm are smaller and exhibit better prediction accuracy.
  • Outperforms several alternative methodologies in simulations and data analyses.