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Functional Classification of Joints
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Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
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Cross-Modal Multivariate Pattern Analysis
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Sparse Partial Least Squares Methods for Joint Modular Pattern Discovery.

Jinyu Chen1, Shihua Zhang2

  • 1NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.

Methods in Molecular Biology (Clifton, N.J.)
|December 19, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel sparse partial least squares (SPLS) framework to uncover complex gene-drug associations. The methods identify coordinated patterns in genomic factors and cancer drug responses, improving pharmacogenomics data analysis.

Keywords:
BioinformaticsCancer genomicsIntegrative analysisNetwork-regularized penaltyPartial least squares

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Area of Science:

  • Genomics
  • Pharmacogenomics
  • Computational Biology

Background:

  • The relationship between genomic factors and cancer drug response is not fully understood.
  • Patient responses to anticancer treatments vary due to genetic and somatic alterations.
  • Identifying complex genomic factor-drug response relationships in pharmacogenomics data is challenging.

Purpose of the Study:

  • To develop a framework for identifying multiple-to-multiple relationships between genomic factors and drug response.
  • To uncover joint modular patterns in large-scale gene expression and drug response data.
  • To reveal coordinated gene-drug associations.

Main Methods:

  • Introduction of a sparse partial least squares (SPLS) framework.
  • Application of network-regularized penalty within the SPLS framework.
  • Analysis of large-scale pairwise gene-expression and drug-response data.

Main Results:

  • Identification of joint modular patterns linking genomic factors and drug response.
  • Discovery of coordinated gene-drug associations.
  • Demonstration of SPLS framework's capability in analyzing complex biological data.

Conclusions:

  • The developed SPLS-based methods are powerful tools for uncovering associations between different feature types.
  • SPLS methods can be applied to various biological problems, including expression quantitative trait loci (eQTL) analysis.
  • This framework advances the understanding of genomic influences on cancer drug response.