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Paper   IPM / Biological Sciences / 16513
School of Biological Sciences
  Title:   Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model
  Author(s): 
1.  Akram Emdadi
2.  Changiz Eslahchi
  Status:   Published
  Journal: BMC Bioinformatics
  No.:  22
  Year:  2021
  Pages:   1-22
  Supported by:  IPM
  Abstract:
Predicting the response of cancer cell lines to specific drugs is an essential problem in personalized medicine. Since drug response is closely associated with genomic information in cancer cells, some large panels of several hundred human cancer cell lines are organized with genomic and pharmacogenomic data. Although several methods have been developed to predict the drug response, there are many challenges in achieving accurate predictions. This study proposes a novel feature selection-based method, named Auto-HMM-LMF, to predict cell line-drug associations accurately. Because of the vast dimensions of the feature space for predicting the drug response, Auto-HMM-LMF focuses on the feature selection issue for exploiting a subset of inputs with a significant contribution.
https://doi.org/10.1186/s12859-021-03974-3

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