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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Multiple-kernel learning for genomic data mining and prediction.

Christopher M Wilson¹, Kaiqiao Li², Xiaoqing Yu¹

¹Department of Biostatistics and Bioinformatics at Moffitt Cancer Center, Tampa, FL, USA.

BMC Bioinformatics

|August 17, 2019

Summary

This summary is machine-generated.

Multiple kernel learning (MKL) integrates diverse genomic data for better cancer prognosis. This study introduces R implementations of MKL, demonstrating its utility in identifying key gene sets for cancer prediction.

Keywords:

Classification Data integration Genomics Kernel methods Machine learning Multiple kernel learning

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Area of Science:

Genomics
Bioinformatics
Machine Learning

Background:

Medical technology advances enable personalized treatments using diverse data.
Multiple Kernel Learning (MKL) is suitable for integrating high-throughput data but underutilized in genomics.
Lack of guidelines and benchmark datasets hinders MKL adoption in genomic research.

Purpose of the Study:

To provide R implementations of MKL for genomic data integration.
To demonstrate MKL's capability in model selection and analysis of multi-source genomic data.
To facilitate the use of MKL in cancer research.

Main Methods:

Developed three MKL implementations in R.
Applied MKL to simulated data for model selection validation.
Integrated clinical and miRNA gene expression data for ovarian cancer analysis.
Utilized The Cancer Genome Atlas (TCGA) data for prognostic prediction across 15 cancer types.

Main Results:

MKL successfully selected appropriate models in simulated data.
Combined clinical and miRNA data effectively in an ovarian cancer study.
Identified known and novel gene sets for prognostic prediction in 15 cancer types using TCGA data.

Conclusions:

MKL, with optimization techniques, is a powerful tool for predictive modeling with multi-source genomic data.
MKL offers automated kernel prioritization and parameter tuning.
An R package, RMKL, is available for public use, promoting MKL adoption in genomics.