Harvestman: a framework for hierarchical feature learning and selection from whole genome sequencing data

Harvestman: a framework for hierarchical feature learning and selection from whole genome sequencing data

Abstract Background Supervised learning from high-throughput sequencing data presents many challenges. For one, the curse of dimensionality often leads to overfitting as well as issues with scalability. This can bring about inaccurate models or those that require extensive compute time and resources...

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Bibliographic Details
Main Authors: Trevor S. Frisby, Shawn J. Baker, Guillaume Marçais, Quang Minh Hoang, Carl Kingsford, Christopher J. Langmead
Format: Article
Language:English
Published: BMC 2021-04-01
Series:BMC Bioinformatics
Subjects:
Feature selection
Hierarchical feature spaces
Knowledge graphs
Integer linear programming
Machine learning
Online Access:https://doi.org/10.1186/s12859-021-04096-6

Internet

https://doi.org/10.1186/s12859-021-04096-6

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