Pathway-structured predictive modeling for multi-level drug response in multiple myeloma
Author(s) -
Xinyan Zhang,
Bing-Zong Li,
Huiying Han,
Sha Song,
Hongxia Xu,
Zixuan Yi,
Yating Hong,
Wenzhuo Zhuang,
Nengjun Yi
Publication year - 2018
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bty436
Subject(s) - logistic regression , computer science , ordinal scale , ordinal regression , ordered logit , machine learning , artificial intelligence , data mining , statistics , mathematics
Molecular analyses suggest that myeloma is composed of distinct sub-types that have different molecular pathologies and various response rates to certain treatments. Drug responses in multiple myeloma (MM) are usually recorded as a multi-level ordinal outcome. One of the goals of drug response studies is to predict which response category any patients belong to with high probability based on their clinical and molecular features. However, as most of genes have small effects, gene-based models may provide limited predictive accuracy. In that case, methods for predicting multi-level ordinal drug responses by incorporating biological pathways are desired but have not been developed yet.
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