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

被引:0
作者
Trevor S. Frisby
Shawn J. Baker
Guillaume Marçais
Quang Minh Hoang
Carl Kingsford
Christopher J. Langmead
机构
[1] Carnegie Mellon University,Computational Biology Department
[2] Carnegie Mellon University,Computer Science Department
来源
BMC Bioinformatics | / 22卷
关键词
Feature selection; Hierarchical feature spaces; Knowledge graphs; Integer linear programming; Machine learning;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
相关论文
共 198 条
  • [1] Leung MKK(2016)Machine learning in genomic medicine: a review of computational problems and data sets Proc IEEE 104 176-197
  • [2] Delong A(2018)The high-throughput analyses era: Are we ready for the data struggle? High Throughput 7 8-49
  • [3] Alipanahi B(2008)The properties of high-dimensional data spaces: implications for exploring gene and protein expression data Nat Rev Cancer 8 37-332
  • [4] Frey BJ(2015)Machine learning applications in genetics and genomics Nat Rev Genet 16 321-87
  • [5] D’Argenio V(2012)A few useful things to know about machine learning Commun ACM 55 78-19445
  • [6] Clarke R(2017)Feature selection: a data perspective ACM Comput Surv 50 94-271
  • [7] Ressom HW(1997)Selection of relevant features and examples in machine learning Artif Intell 97 245-198363
  • [8] Wang A(2015)A review of feature selection and feature extraction methods applied on microarray data Adv Bioinform 2015 198363-2517
  • [9] Xuan J(2007)A review of feature selection techniques in bioinformatics Bioinformatics 23 2507-55
  • [10] Liu MC(1997)Overcoming the myopia of inductive learning algorithms with relieff Appl Intell 7 39-29