RefDNN: a reference drug based neural network for more accurate prediction of anticancer drug resistance

Choi, Jonghwan; Park, Sang‐Hyun; Ahn, Jaegyoon

doi:10.1038/s41598-020-58821-x

Cited by 39 publications

(31 citation statements)

References 38 publications

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“…Some of the computational methods have considered drug information such as chemical substructure of drugs, besides made use of cell line information. In the proposed computational methods, various machine learning methods have been utilized such as sparse linear regression 4 , 9 – 11 , random forest 2 , 12 , 13 , kernel-based methods 4 , 14 – 17 , matrix factorization 1 , 18 – 20 , neural networks and deep learning 21 – 24 .…”

Section: Introductionmentioning

confidence: 99%

ADRML: anticancer drug response prediction using manifold learning

Moughari

Eslahchi

2020

Sci Rep

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one of the prominent challenges in precision medicine is to select the most appropriate treatment strategy for each patient based on the personalized information. the availability of massive data about drugs and cell lines facilitates the possibility of proposing efficient computational models for predicting anticancer drug response. In this study, we propose ADRML, a model for Anticancer Drug Response Prediction using Manifold Learning to systematically integrate the cell line information with the drug information to make accurate predictions about drug therapeutic. the proposed model maps the drug response matrix into the lower-rank spaces that lead to obtaining new perspectives about cell lines and drugs. The drug response for a new cell line-drug pair is computed using the low-rank features. The evaluation of ADRML performance on various types of cell lines and drug information, in addition to the comparisons with previously proposed methods, shows that ADRML provides accurate and robust predictions. Further investigations about the association between drug response and pathway activity scores reveal that the predicted drug responses can shed light on the underlying drug mechanism. Also, the case studies suggest that the predictions of ADRML about novel cell line-drug pairs are validated by reliable pieces of evidence from the literature. Consequently, the evaluations verify that ADRML can be used in accurately predicting and imputing the anticancer drug response. Precision medicine aims to finely select treatments for cancer based on the genetic information of individual patients 1. One of the highly critical problems in precision medicine is predicting anticancer drug response for each patient 2-4. Due to the heterogeneity of tumors, the patients with the same type of cancer may show various therapeutic responses toward similar drugs 5. Therefore, providing computational methods to discover the relationship between genomic information and drug sensitivity is of high importance and can be beneficial in precision medicine 3,6. Genomics of Drug Sensitivity in Cancer (GDSC) 7 and Cancer Cell Line Encyclopedia (CCLE) 8 are two projects that have provided molecular profiles and drug response values for hundreds of cancer cell lines against several anticancer drugs. These large datasets facilitate the development of computational methods for anticancer drug sensitivity prediction. Numerous computational methods have been proposed to predict drug response using gene expression profile, or other molecular information of cell lines. Some of the computational methods have considered drug information such as chemical substructure of drugs, besides made use of cell line information. In the proposed computational methods, various machine learning methods have been utilized such as sparse linear regression 4,9-11 , random forest 2,12,13 , kernel-based methods 4,14-17 , matrix factorization 1,18-20 , neural networks and deep learning 21-24. Wang et al. have proposed a Similarity Regularized Matrix Factorization (SRMF) meth...

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Section: Introductionmentioning

confidence: 99%

ADRML: anticancer drug response prediction using manifold learning

Moughari

Eslahchi

2020

Sci Rep

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“…As a subfield of machine learning approaches, deep learning methods have been shown to exhibit unprecedented performance in various areas of biological prediction [51][52][53][54][55][56][57][58][59][60][61] . We described a novel deep neural network model in the present study, termed AptaNet, for predicting API.…”

Section: Discussionmentioning

confidence: 99%

AptaNet as a deep learning approach for aptamer–protein interaction prediction

Emami

Ferdousi

2021

Sci Rep

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Aptamers are short oligonucleotides (DNA/RNA) or peptide molecules that can selectively bind to their specific targets with high specificity and affinity. As a powerful new class of amino acid ligands, aptamers have high potentials in biosensing, therapeutic, and diagnostic fields. Here, we present AptaNet—a new deep neural network—to predict the aptamer–protein interaction pairs by integrating features derived from both aptamers and the target proteins. Aptamers were encoded by using two different strategies, including k-mer and reverse complement k-mer frequency. Amino acid composition (AAC) and pseudo amino acid composition (PseAAC) were applied to represent target information using 24 physicochemical and conformational properties of the proteins. To handle the imbalance problem in the data, we applied a neighborhood cleaning algorithm. The predictor was constructed based on a deep neural network, and optimal features were selected using the random forest algorithm. As a result, 99.79% accuracy was achieved for the training dataset, and 91.38% accuracy was obtained for the testing dataset. AptaNet achieved high performance on our constructed aptamer-protein benchmark dataset. The results indicate that AptaNet can help identify novel aptamer–protein interacting pairs and build more-efficient insights into the relationship between aptamers and proteins. Our benchmark dataset and the source codes for AptaNet are available in: https://github.com/nedaemami/AptaNet.

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“…Drug repositioning is the process of selecting a known drug for an alternative pharmacological purpose. For this issue, we considered 37 US Food and Drug Administration (FDA) approved drugs that were not tested in the GDSC dataset from the study of Choi et al [23]. The Auto-HMM-LMF model was trained on the GDSC dataset and the probability of sensitivity of 20 cell lines of head and neck cancer (HNSC) across 20 anticancer drugs of 37 drugs were predicted and were shown in Fig.…”

Section: Application For Drug Repositioningmentioning

confidence: 99%

Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model

Emdadi

Eslahchi

2021

BMC Bioinformatics

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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.

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RefDNN: a reference drug based neural network for more accurate prediction of anticancer drug resistance

Cited by 39 publications

References 38 publications

ADRML: anticancer drug response prediction using manifold learning

ADRML: anticancer drug response prediction using manifold learning

AptaNet as a deep learning approach for aptamer–protein interaction prediction

Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model

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