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Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
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PSO-CFDP: A Particle Swarm Optimization-Based Automatic Density Peaks Clustering Method for Cancer Subtyping.

Xuhui Zhu1, Junliang Shang2,3, Yan Sun1

  • 1School of Information Science and Engineering, Qufu Normal University, Rizhao, China.

Human Heredity
|August 15, 2019
PubMed
Summary

This study introduces PSO-CFDP, an automated method for cancer subtyping that overcomes manual parameter limitations in existing algorithms. It improves cancer subtyping accuracy by automatically determining optimal cluster centers and cutoff distances.

Keywords:
Automatically determined parameter valuesCancer subtypingDensity peaks clusteringParticle swarm optimization algorithmVariance of regional density

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Cancer subtyping is crucial for personalized medicine, aiding prediction, diagnosis, and treatment.
  • Clustering algorithms are widely used for cancer subtyping, with Clustering by Fast Search and Find of Density Peaks (CFDP) showing promise.
  • Manual parameter selection in CFDP (cluster centers, cutoff distance) hinders optimal performance.

Purpose of the Study:

  • To develop an automated cancer subtyping method that overcomes the limitations of manual parameter tuning in CFDP.
  • To enhance the accuracy and efficiency of cancer subtyping through automatic parameter optimization.

Main Methods:

  • Proposed PSO-CFDP, an enhanced clustering algorithm utilizing particle swarm optimization (PSO) for automatic parameter determination.
  • Compared PSO-CFDP against CFDP, LR-CFDP, STClu, and CH-CCFDAC on benchmark and real-world cancer gene expression datasets.

Main Results:

  • PSO-CFDP successfully automated the determination of cluster centers and cutoff distance.
  • The method achieved improved accuracy in cancer subtyping compared to existing approaches.
  • Automatic parameter optimization was achieved within a controllable time and computational cost.

Conclusions:

  • PSO-CFDP offers a robust and automated solution for cancer subtyping.
  • The automated approach enhances the reliability and accuracy of cancer subtyping for clinical applications.
  • This method holds potential for advancing precision oncology through improved patient stratification.