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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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CRESCENT: a deep learning framework with multi-scale attention for detecting recurrent copy number alterations.

Xikang Feng1,2, Zheng Xu2, Sisi Peng2

  • 1Research & Development Institute, Northwestern Polytechnical University, Sanhang Science & Technology Buliding, No. 45th, Gaoxin South 9th Road, Nanshan District, Shenzhen City, 518063, China.

Briefings in Bioinformatics
|April 17, 2026
PubMed
Summary
This summary is machine-generated.

CRESCENT, a deep learning framework, accurately detects copy number alterations (CNAs) across various scales in cancer. It outperforms existing tools, identifying key drivers of tumor evolution.

Keywords:
cancer analysiscopy number alterationsdeep learningfocal CNArecurrent CNA

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Area of Science:

  • Genomics
  • Computational Biology
  • Cancer Research

Background:

  • Recurrent copy number alterations (CNAs) are critical drivers of cancer development.
  • Existing methods face challenges in detecting CNAs across diverse genomic scales (focal, segmental, arm-level).

Purpose of the Study:

  • To develop a novel deep learning framework, CRESCENT, for sensitive and generalized detection of recurrent CNAs.
  • To improve the balance of sensitivity across different CNA scales.

Main Methods:

  • Developed CRESCENT, a deep learning framework integrating multi-scale sampling with CNNs and self-attention.
  • Processed copy number profiles from 7689 TCGA cancer cases.
  • Validated performance using leave-one-project-out cross-validation and independent cohorts (CGCI, TARGET).

Main Results:

  • CRESCENT demonstrated robust generalization with high AUCs for amplifications (0.894-0.967) and deletions (0.804-0.929).
  • Outperformed standard tools (GISTIC2, RUBIC) in detecting significant events across focal and broad scales.
  • Identified critical oncogenic drivers and prognostic markers missed by conventional methods.

Conclusions:

  • CRESCENT offers a highly sensitive and generalized approach for detecting recurrent CNAs.
  • The framework aids in decoding tumor evolution and identifying novel therapeutic targets.