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Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Kernels for generalized multiple-instance learning.

Qingping Tao1, Stephen D Scott, N V Vinodchandran

  • 1GC Image, LLC, Lincoln, NE 68505, USA.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 8, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces scalable kernels for multiple-instance learning (MIL), improving high-dimensional data analysis. The new methods offer efficient solutions for complex MIL problems in various applications.

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Last Updated: Jun 28, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
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Published on: March 1, 2024

Area of Science:

  • Machine Learning
  • Computer Science

Background:

  • Multiple-instance learning (MIL) is effective but its recent generalization has scalability issues in high dimensions.
  • Existing generalized MIL algorithms are computationally expensive for high-dimensional data.

Purpose of the Study:

  • To develop scalable algorithms for a generalized multiple-instance learning model.
  • To adapt a generalized MIL algorithm using support vector machines and novel kernels.
  • To address the computational limitations of existing high-dimensional MIL approaches.

Main Methods:

  • Adapted a generalized MIL algorithm using support vector machines with a new kernel, k\wedge.
  • Developed a fully polynomial randomized approximation scheme (FPRAS) for a #P-complete kernel computation problem.
  • Introduced extended kernels kmin and k\wedge/\vee for improved representation and normalization.

Main Results:

  • Reduced time complexity from exponential to polynomial in dimension for the generalized MIL algorithm.
  • Empirically validated the performance of the new kernels (k\wedge, kmin, k\wedge/\vee) on diverse datasets.
  • Demonstrated competitive or superior performance of the proposed kernels compared to conventional MIL algorithms.

Conclusions:

  • The proposed kernel-based approach significantly enhances the scalability of generalized multiple-instance learning.
  • The novel kernels provide efficient and effective solutions for high-dimensional MIL tasks.
  • This work opens new avenues for applying advanced MIL techniques to complex real-world problems.