Akram Emdadi1, Changiz Eslahchi2,3. 1. Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran. 2. Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran. Ch-Eslahchi@sbu.ac.ir. 3. School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), 193955746, Tehran, Iran. Ch-Eslahchi@sbu.ac.ir.
Abstract
BACKGROUND: 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. RESULTS: This research introduces a novel method for feature selection of mutation data based on signature assignments and hidden Markov models. Also, we use the autoencoder models for feature selection of gene expression and copy number variation data. After selecting features, the logistic matrix factorization model is applied to predict drug response values. Besides, by comparing to one of the most powerful feature selection methods, the ensemble feature selection method (EFS), we showed that the performance of the predictive model based on selected features introduced in this paper is much better for drug response prediction. Two datasets, the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) are used to indicate the efficiency of the proposed method across unseen patient cell-line. Evaluation of the proposed model showed that Auto-HMM-LMF could improve the accuracy of the results of the state-of-the-art algorithms, and it can find useful features for the logistic matrix factorization method. CONCLUSIONS: We depicted an application of Auto-HMM-LMF in exploring the new candidate drugs for head and neck cancer that showed the proposed method is useful in drug repositioning and personalized medicine. The source code of Auto-HMM-LMF method is available in https://github.com/emdadi/Auto-HMM-LMF .
BACKGROUND: 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 humancancer 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. RESULTS: This research introduces a novel method for feature selection of mutation data based on signature assignments and hidden Markov models. Also, we use the autoencoder models for feature selection of gene expression and copy number variation data. After selecting features, the logistic matrix factorization model is applied to predict drug response values. Besides, by comparing to one of the most powerful feature selection methods, the ensemble feature selection method (EFS), we showed that the performance of the predictive model based on selected features introduced in this paper is much better for drug response prediction. Two datasets, the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) are used to indicate the efficiency of the proposed method across unseen patient cell-line. Evaluation of the proposed model showed that Auto-HMM-LMF could improve the accuracy of the results of the state-of-the-art algorithms, and it can find useful features for the logistic matrix factorization method. CONCLUSIONS: We depicted an application of Auto-HMM-LMF in exploring the new candidate drugs for head and neck cancer that showed the proposed method is useful in drug repositioning and personalized medicine. The source code of Auto-HMM-LMF method is available in https://github.com/emdadi/Auto-HMM-LMF .
Authors: Yan Zang; Sufi M Thomas; Elena T Chan; Christopher J Kirk; Maria L Freilino; Hannah M DeLancey; Jennifer R Grandis; Changyou Li; Daniel E Johnson Journal: Clin Cancer Res Date: 2012-08-28 Impact factor: 12.531
Authors: Bin Li; Hyunjin Shin; Georgy Gulbekyan; Olga Pustovalova; Yuri Nikolsky; Andrew Hope; Marina Bessarabova; Matthew Schu; Elona Kolpakova-Hart; David Merberg; Andrew Dorner; William L Trepicchio Journal: PLoS One Date: 2015-06-24 Impact factor: 3.240
Authors: Simon A Forbes; David Beare; Harry Boutselakis; Sally Bamford; Nidhi Bindal; John Tate; Charlotte G Cole; Sari Ward; Elisabeth Dawson; Laura Ponting; Raymund Stefancsik; Bhavana Harsha; Chai Yin Kok; Mingming Jia; Harry Jubb; Zbyslaw Sondka; Sam Thompson; Tisham De; Peter J Campbell Journal: Nucleic Acids Res Date: 2016-11-28 Impact factor: 16.971
Authors: Serena Nik-Zainal; Ludmil B Alexandrov; David C Wedge; Peter Van Loo; Christopher D Greenman; Keiran Raine; David Jones; Jonathan Hinton; John Marshall; Lucy A Stebbings; Andrew Menzies; Sancha Martin; Kenric Leung; Lina Chen; Catherine Leroy; Manasa Ramakrishna; Richard Rance; King Wai Lau; Laura J Mudie; Ignacio Varela; David J McBride; Graham R Bignell; Susanna L Cooke; Adam Shlien; John Gamble; Ian Whitmore; Mark Maddison; Patrick S Tarpey; Helen R Davies; Elli Papaemmanuil; Philip J Stephens; Stuart McLaren; Adam P Butler; Jon W Teague; Göran Jönsson; Judy E Garber; Daniel Silver; Penelope Miron; Aquila Fatima; Sandrine Boyault; Anita Langerød; Andrew Tutt; John W M Martens; Samuel A J R Aparicio; Åke Borg; Anne Vincent Salomon; Gilles Thomas; Anne-Lise Børresen-Dale; Andrea L Richardson; Michael S Neuberger; P Andrew Futreal; Peter J Campbell; Michael R Stratton Journal: Cell Date: 2012-05-17 Impact factor: 41.582