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

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. Additi...

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Bibliographic Details
Main Authors: Baker, S.J (Author), Frisby, T.S (Author), Hoang, Q.M (Author), Kingsford, C. (Author), Langmead, C.J (Author), Marçais, G. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
Biological interpretation
Breast Neoplasms
breast tumor
Curse of dimensionality
Deep Learning
Diseases
Encoding (symbols)
Feature extraction
Feature selection
Feature selection methods
Genes
genetics
genome
Genome
genomics
Genomics
Hierarchical feature spaces
High-throughput sequencing
human
Humans
Integer linear programming
Integer linear programs
Integer programming
Knowledge graphs
Knowledge representation
Machine learning
Model buildings
Predictive capabilities
Signal encoding
Whole genome sequences
whole genome sequencing
Whole genome sequencing
Whole Genome Sequencing
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